"""simple docstring"""

import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Dict     , UpperCamelCase__							:    int				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=foo





						def __call__(				self							:    Optional[int]				)				->				Optional[Any]:
													'''simple docstring'''



													return self.foo






class 		_lowercase (						datasets.Dataset      ):



						"""simple docstring"""




						@fingerprint_transform(inplace=UpperCamelCase__				)
						def   UpperCAmelCase_ (				self							:    Optional[int]     , UpperCamelCase__							:    List[str]     , *UpperCamelCase__							:    Dict     , **UpperCamelCase__							:    Dict				)				->				str:
													'''simple docstring'''



													return DatasetChild(self.data     , fingerprint=UpperCamelCase__				)





						@fingerprint_transform(inplace=UpperCamelCase__				)
						def   UpperCAmelCase_ (				self							:    List[str]     , UpperCamelCase__							:    List[Any]     , *UpperCamelCase__							:    List[str]     , **UpperCamelCase__							:    Optional[int]				)				->				Any:
													'''simple docstring'''



													return DatasetChild(self.data     , fingerprint=UpperCamelCase__				)






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Dict     , UpperCamelCase__							:    int				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=callable





						def __call__(				self							:    Tuple     , *UpperCamelCase__							:    Optional[Any]     , **UpperCamelCase__							:    List[Any]				)				->				List[str]:
													'''simple docstring'''



													if self.callable is not None:
																				return self.callable(*UpperCamelCase__     , **UpperCamelCase__				)





						def __getstate__(				self							:    int				)				->				Tuple:
													'''simple docstring'''



													raise pickle.PicklingError()






class 		_lowercase (						__a      ):



						"""simple docstring"""




						@require_transformers
						@pytest.mark.integration
						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Any:
													'''simple docstring'''



													from transformers import AutoTokenizer

													def encode(UpperCamelCase__							:    str				):
																				return tokenizer(UpperCamelCase__				)

													# TODO: add hash consistency tests across sessions
													__UpperCamelCase							=AutoTokenizer.from_pretrained('''bert-base-uncased'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(lambda UpperCamelCase__				: tokenizer(UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=AutoTokenizer.from_pretrained('''bert-base-cased'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(lambda UpperCamelCase__				: tokenizer(UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=AutoTokenizer.from_pretrained('''bert-base-uncased'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(lambda UpperCamelCase__				: tokenizer(UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						@require_transformers
						@pytest.mark.integration
						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				str:
													'''simple docstring'''



													from transformers import AutoTokenizer

													__UpperCamelCase							=AutoTokenizer.from_pretrained('''gpt2'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													tokenizer('''Hello world !'''				)  # call once to change the tokenizer's cache
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)





						@require_regex
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				int:
													'''simple docstring'''



													import regex

													__UpperCamelCase							=regex.Regex('''foo'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=regex.Regex('''bar'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=regex.Regex('''foo'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Dict				)				->				Union[str, Any]:
													'''simple docstring'''



													def func():
																				return foo

													__UpperCamelCase							=[0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=[1]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=[0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Optional[int]:
													'''simple docstring'''



													def func():
																				pass

													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()

													def func():
																				pass

													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([0]				)				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([1]				)				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([0]				)				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([0]				).__call__				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([1]				).__call__				)				).hexdigest()
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(Foo([0]				).__call__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=(
													    '''co_argcount''',
													    '''co_kwonlyargcount''',
													    '''co_nlocals''',
													    '''co_stacksize''',
													    '''co_flags''',
													    '''co_code''',
													    '''co_consts''',
													    '''co_names''',
													    '''co_varnames''',
													    '''co_filename''',
													    '''co_name''',
													    '''co_firstlineno''',
													    '''co_lnotab''',
													    '''co_freevars''',
													    '''co_cellvars''',
													)

													def _create_code_pyaa(*UpperCamelCase__							:    int				):
																				if hasattr(args[-3]     , '''encode'''				):
																											__UpperCamelCase							=list(UpperCamelCase__				)
																											__UpperCamelCase							=args[-3].encode()  # co_lnotab
																											__UpperCamelCase							=args[-10].encode()  # co_code
																				if hasattr(UpperCamelCase__     , '''co_posonlyargcount'''				):
																											if len(UpperCamelCase__				) == 16:
																																		return CodeType(*UpperCamelCase__				)
																											elif len(UpperCamelCase__				) == 15:
																																		return CodeType(args[0]     , 0     , *args[1:]				)
																											return CodeType(args[0]     , 0     , 0     , *args[1:]				)
																				elif hasattr(UpperCamelCase__     , '''co_kwonlyargcount'''				):
																											if len(UpperCamelCase__				) == 16:
																																		return CodeType(args[0]     , *args[2:]				)
																											elif len(UpperCamelCase__				) == 15:
																																		return CodeType(*UpperCamelCase__				)
																											return CodeType(args[0]     , 0     , *args[1:]				)
																				if len(UpperCamelCase__				) == 16:
																											return CodeType(args[0]     , *args[3:]				)
																				elif len(UpperCamelCase__				) == 15:
																											return CodeType(args[0]     , *args[2:]				)
																				return CodeType(*UpperCamelCase__				)

													def create_ipython_func(UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    Tuple				):
																				def func():
																											return returned_obj

																				__UpperCamelCase							=func.__code__
																				# Use _create_code from dill in order to make it work for different python versions
																				if datasets.config.PY_VERSION < version.parse('''3.8'''				):
																											__UpperCamelCase							=_create_code_pyaa(
																											    *[getattr(UpperCamelCase__     , UpperCamelCase__				) if k != '''co_filename''' else co_filename for k in code_args_pyaa]				)
																				else:
																											__UpperCamelCase							=code.replace(co_filename=UpperCamelCase__				)
																				return FunctionType(UpperCamelCase__     , func.__globals__     , func.__name__     , func.__defaults__     , func.__closure__				)

													__UpperCamelCase	,      __UpperCamelCase							='''<ipython-input-2-e0383a102aae>''', [0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase	,      __UpperCamelCase							='''<ipython-input-2-e0383a102aae>''', [1]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase	,      __UpperCamelCase							='''<ipython-input-5-713f6613acf3>''', [0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)

													__UpperCamelCase	,      __UpperCamelCase							=os.path.join(gettempdir()     , '''ipykernel_12345'''     , '''321456789.py'''				), [0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase	,      __UpperCamelCase							=os.path.join(gettempdir()     , '''ipykernel_12345'''     , '''321456789.py'''				), [1]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													__UpperCamelCase	,      __UpperCamelCase							=os.path.join(gettempdir()     , '''ipykernel_12345'''     , '''654123987.py'''				), [0]
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCamelCase__     , UpperCamelCase__				)				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    int				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=[0], [1]

													def func():
																				return foo, bar

													__UpperCamelCase							='''__main__'''

													def globalvars_mocka_side_effect(UpperCamelCase__							:    List[str]     , *UpperCamelCase__							:    Union[str, Any]     , **UpperCamelCase__							:    Dict				):
																				return {"foo": foo, "bar": bar}

													def globalvars_mocka_side_effect(UpperCamelCase__							:    Dict     , *UpperCamelCase__							:    Any     , **UpperCamelCase__							:    Optional[int]				):
																				return {"bar": bar, "foo": foo}

													with patch('''dill.detect.globalvars'''     , side_effect=UpperCamelCase__				) as globalvars_mocka:
																				__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
																				self.assertGreater(globalvars_mocka.call_count     , 0				)
													with patch('''dill.detect.globalvars'''     , side_effect=UpperCamelCase__				) as globalvars_mocka:
																				__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
																				self.assertGreater(globalvars_mocka.call_count     , 0				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=Hasher.hash('''hello'''				)
													__UpperCamelCase							=Hasher.hash('''hello'''				)
													__UpperCamelCase							=Hasher.hash('''there'''				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    str				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Hasher.hash(Foo('''hello'''				)				)
													__UpperCamelCase							=Hasher.hash(Foo('''hello'''				)				)
													__UpperCamelCase							=Hasher.hash(Foo('''there'''				)				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=Hasher()
													for x in ["hello", Foo('''hello'''				)]:
																				hasher.update(UpperCamelCase__				)
													__UpperCamelCase							=hasher.hexdigest()
													__UpperCamelCase							=Hasher()
													for x in ["hello", Foo('''hello'''				)]:
																				hasher.update(UpperCamelCase__				)
													__UpperCamelCase							=hasher.hexdigest()
													__UpperCamelCase							=Hasher()
													for x in ["there", Foo('''there'''				)]:
																				hasher.update(UpperCamelCase__				)
													__UpperCamelCase							=hasher.hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				List[str]:
													'''simple docstring'''



													with self.assertRaises(pickle.PicklingError				):
																				Hasher.hash(UnpicklableCallable(Foo('''hello'''				)				)				)





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							='''abc'''
													__UpperCamelCase							=[string, string]  # two strings have the same ids
													__UpperCamelCase							=[string, string]
													__UpperCamelCase							=json.loads(f"""[\"{string}\", \"{string}\"]"""				)  # two strings have different ids
													self.assertIs(obja[0]     , UpperCamelCase__				)
													self.assertIs(obja[0]     , obja[1]				)
													self.assertIs(obja[0]     , UpperCamelCase__				)
													self.assertIs(obja[0]     , obja[1]				)
													self.assertIsNot(obja[0]     , UpperCamelCase__				)
													self.assertIsNot(obja[0]     , obja[1]				)
													__UpperCamelCase							=Hasher.hash(UpperCamelCase__				)
													__UpperCamelCase							=Hasher.hash(UpperCamelCase__				)
													__UpperCamelCase							=Hasher.hash(UpperCamelCase__				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)





						@require_tiktoken
						def   UpperCAmelCase_ (				self							:    Any				)				->				Optional[Any]:
													'''simple docstring'''



													import tiktoken

													__UpperCamelCase							=tiktoken.get_encoding('''gpt2'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=tiktoken.get_encoding('''r50k_base'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=tiktoken.get_encoding('''gpt2'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						@require_torch
						def   UpperCAmelCase_ (				self							:    str				)				->				Union[str, Any]:
													'''simple docstring'''



													import torch

													__UpperCamelCase							=torch.tensor([1.0]				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=torch.tensor([2.0]				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=torch.tensor([1.0]				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)





						@require_spacy
						@require_spacy_model('''en_core_web_sm'''				)
						@require_spacy_model('''fr_core_news_sm'''				)
						@pytest.mark.integration
						def   UpperCAmelCase_ (				self							:    str				)				->				Any:
													'''simple docstring'''



													import spacy

													__UpperCamelCase							=spacy.load('''en_core_web_sm'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=spacy.load('''fr_core_news_sm'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													__UpperCamelCase							=spacy.load('''en_core_web_sm'''				)
													__UpperCamelCase							=mda(datasets.utils.py_utils.dumps(UpperCamelCase__				)				).hexdigest()
													self.assertEqual(UpperCamelCase__     , UpperCamelCase__				)
													self.assertNotEqual(UpperCamelCase__     , UpperCamelCase__				)






@pytest.mark.integration
def    lowerCAmelCase			(__UpperCamelCase							:    Path			):
							"""simple docstring"""


							__UpperCamelCase							=tmp_path / '''dir1'''
							__UpperCamelCase							=tmp_path / '''dir2'''
							dira.mkdir()
							dira.mkdir()
							__UpperCamelCase							='''script.py'''
							__UpperCamelCase							=dedent(
							    '''
    from datasets.fingerprint import Hasher
    def foo():
        pass
    print(Hasher.hash(foo))
    '''			)
							__UpperCamelCase							=dira / script_filename
							__UpperCamelCase							=dira / script_filename
							with script_patha.open('''w'''			) as f:
														f.write(__UpperCamelCase			)
							with script_patha.open('''w'''			) as f:
														f.write(__UpperCamelCase			)
							__UpperCamelCase							=subprocess.check_output(['''python''', str(__UpperCamelCase			)]			)
							__UpperCamelCase							=subprocess.check_output(['''python''', str(__UpperCamelCase			)]			)
							assert fingerprinta == fingerprinta


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							={'''a''': [0, 1, 2]}
							__UpperCamelCase							=DatasetChild(InMemoryTable.from_pydict(__UpperCamelCase			)			)
							__UpperCamelCase							=dataset.funca()._fingerprint
							assert expected_fingerprint == dataset.funca()._fingerprint
							assert expected_fingerprint != dataset.funca()._fingerprint

							with Pool(2			) as p:
														assert expected_fingerprint == p.apply_async(dataset.funca			).get()._fingerprint
														assert expected_fingerprint != p.apply_async(dataset.funca			).get()._fingerprint


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							={'''a''': [0, 1, 2]}






							class 		_lowercase (						datasets.Dataset      ):



													"""simple docstring"""




													@fingerprint_transform(inplace=UpperCamelCase__				)
													def   UpperCAmelCase_ (				self							:    int     , UpperCamelCase__							:    Union[str, Any]				)				->				Tuple:
																				'''simple docstring'''



																				return DummyDatasetChild(self.data     , fingerprint=UpperCamelCase__				)






							__UpperCamelCase							=DummyDatasetChild(InMemoryTable.from_pydict(__UpperCamelCase			)			).func()






							class 		_lowercase (						datasets.Dataset      ):



													"""simple docstring"""




													@fingerprint_transform(inplace=UpperCamelCase__     , version='''1.0.0'''				)
													def   UpperCAmelCase_ (				self							:    Tuple     , UpperCamelCase__							:    Optional[int]				)				->				int:
																				'''simple docstring'''



																				return DummyDatasetChild(self.data     , fingerprint=UpperCamelCase__				)






							__UpperCamelCase							=DummyDatasetChild(InMemoryTable.from_pydict(__UpperCamelCase			)			).func()






							class 		_lowercase (						datasets.Dataset      ):



													"""simple docstring"""




													@fingerprint_transform(inplace=UpperCamelCase__     , version='''2.0.0'''				)
													def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    Union[str, Any]				)				->				int:
																				'''simple docstring'''



																				return DummyDatasetChild(self.data     , fingerprint=UpperCamelCase__				)






							__UpperCamelCase							=DummyDatasetChild(InMemoryTable.from_pydict(__UpperCamelCase			)			).func()

							assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}			) == 3






def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=Hasher()
							hasher.update(lambda __UpperCamelCase			: x			)




"""simple docstring"""

import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import NatBackbone, NatForImageClassification, NatModel
				from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Tuple     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    Optional[Any]=13     , UpperCamelCase__							:    Optional[Any]=64     , UpperCamelCase__							:    str=4     , UpperCamelCase__							:    List[str]=3     , UpperCamelCase__							:    int=16     , UpperCamelCase__							:    str=[1, 2, 1]     , UpperCamelCase__							:    List[Any]=[2, 4, 8]     , UpperCamelCase__							:    Optional[Any]=3     , UpperCamelCase__							:    List[Any]=2.0     , UpperCamelCase__							:    int=True     , UpperCamelCase__							:    List[str]=0.0     , UpperCamelCase__							:    Optional[Any]=0.0     , UpperCamelCase__							:    Optional[Any]=0.1     , UpperCamelCase__							:    Tuple="gelu"     , UpperCamelCase__							:    Tuple=True     , UpperCamelCase__							:    Any=0.02     , UpperCamelCase__							:    str=1E-5     , UpperCamelCase__							:    Dict=True     , UpperCamelCase__							:    Optional[int]=None     , UpperCamelCase__							:    Any=True     , UpperCamelCase__							:    Tuple=10     , UpperCamelCase__							:    List[Any]=["stage1", "stage2"]     , UpperCamelCase__							:    Optional[int]=[1, 2]     , )				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=parent
													__UpperCamelCase							=batch_size
													__UpperCamelCase							=image_size
													__UpperCamelCase							=patch_size
													__UpperCamelCase							=num_channels
													__UpperCamelCase							=embed_dim
													__UpperCamelCase							=depths
													__UpperCamelCase							=num_heads
													__UpperCamelCase							=kernel_size
													__UpperCamelCase							=mlp_ratio
													__UpperCamelCase							=qkv_bias
													__UpperCamelCase							=hidden_dropout_prob
													__UpperCamelCase							=attention_probs_dropout_prob
													__UpperCamelCase							=drop_path_rate
													__UpperCamelCase							=hidden_act
													__UpperCamelCase							=patch_norm
													__UpperCamelCase							=layer_norm_eps
													__UpperCamelCase							=initializer_range
													__UpperCamelCase							=is_training
													__UpperCamelCase							=scope
													__UpperCamelCase							=use_labels
													__UpperCamelCase							=num_labels
													__UpperCamelCase							=out_features
													__UpperCamelCase							=out_indices





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													__UpperCamelCase							=None
													if self.use_labels:
																				__UpperCamelCase							=ids_tensor([self.batch_size]     , self.num_labels				)

													__UpperCamelCase							=self.get_config()

													return config, pixel_values, labels





						def   UpperCAmelCase_ (				self							:    int				)				->				Union[str, Any]:
													'''simple docstring'''



													return NatConfig(
													    num_labels=self.num_labels     , image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , embed_dim=self.embed_dim     , depths=self.depths     , num_heads=self.num_heads     , kernel_size=self.kernel_size     , mlp_ratio=self.mlp_ratio     , qkv_bias=self.qkv_bias     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , drop_path_rate=self.drop_path_rate     , hidden_act=self.hidden_act     , patch_norm=self.patch_norm     , layer_norm_eps=self.layer_norm_eps     , initializer_range=self.initializer_range     , out_features=self.out_features     , out_indices=self.out_indices     , )





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=NatModel(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													__UpperCamelCase							=__UpperCamelCase							=(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
													__UpperCamelCase							=int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

													self.parent.assertEqual(
													    result.last_hidden_state.shape     , (self.batch_size, expected_height, expected_width, expected_dim)				)





						def   UpperCAmelCase_ (				self							:    Dict     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    Any				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__     , labels=UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)

													# test greyscale images
													__UpperCamelCase							=1
													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													__UpperCamelCase							=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													__UpperCamelCase							=model(UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Dict     , UpperCamelCase__							:    str				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify hidden states
													self.parent.assertEqual(len(result.feature_maps				)     , len(config.out_features				)				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[0], 16, 16]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , len(config.out_features				)				)

													# verify backbone works with out_features=None
													__UpperCamelCase							=None
													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify feature maps
													self.parent.assertEqual(len(result.feature_maps				)     , 1				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[-1], 4, 4]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , 1				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.prepare_config_and_inputs()
													__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=config_and_inputs
													__UpperCamelCase							={'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_natten
@require_torch
class 		_lowercase (						__a					,					__a					,					unittest.TestCase      ):



						"""simple docstring"""




						lowercase__       =  (
						    (
						        NatModel,
						        NatForImageClassification,
						        NatBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						lowercase__       =  (
						    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
						    if is_torch_available()
						    else {}
						)
						lowercase__       =  False

						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)
													__UpperCamelCase							=ConfigTester(self     , config_class=UpperCamelCase__     , embed_dim=37				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				str:
													'''simple docstring'''



													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Optional[int]:
													'''simple docstring'''



													return





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*UpperCamelCase__				)





						@unittest.skip(reason='''Nat does not use inputs_embeds'''				)
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



													pass





						@unittest.skip(reason='''Nat does not use feedforward chunking'''				)
						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													pass





						def   UpperCAmelCase_ (				self							:    Any				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)				)
																				__UpperCamelCase							=model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(UpperCamelCase__     , nn.Linear				)				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				__UpperCamelCase							=inspect.signature(model.forward				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__UpperCamelCase							=[*signature.parameters.keys()]

																				__UpperCamelCase							=['''pixel_values''']
																				self.assertListEqual(arg_names[:1]     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''				)





						def   UpperCAmelCase_ (				self							:    Tuple     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    List[str]     , UpperCamelCase__							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=model_class(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													with torch.no_grad():
																				__UpperCamelCase							=model(**self._prepare_for_class(UpperCamelCase__     , UpperCamelCase__				)				)

													__UpperCamelCase							=outputs.hidden_states

													__UpperCamelCase							=getattr(
													    self.model_tester     , '''expected_num_hidden_layers'''     , len(self.model_tester.depths				) + 1				)
													self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

													# Nat has a different seq_length
													__UpperCamelCase							=(
													    config.patch_size
													    if isinstance(config.patch_size     , collections.abc.Iterable				)
													    else (config.patch_size, config.patch_size)
													)

													__UpperCamelCase							=image_size[0] // patch_size[0]
													__UpperCamelCase							=image_size[1] // patch_size[1]

													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )

													if model_class.__name__ != "NatBackbone":
																				__UpperCamelCase							=outputs.reshaped_hidden_states
																				self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

																				__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=reshaped_hidden_states[0].shape
																				__UpperCamelCase							=(
																				    reshaped_hidden_states[0].view(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				).permute(0     , 2     , 3     , 1				)
																				)
																				self.assertListEqual(
																				    list(reshaped_hidden_states.shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=(
													    self.model_tester.image_size
													    if isinstance(self.model_tester.image_size     , collections.abc.Iterable				)
													    else (self.model_tester.image_size, self.model_tester.image_size)
													)

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=True
																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__UpperCamelCase							=True

																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)





						@slow
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



													for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__UpperCamelCase							=NatModel.from_pretrained(UpperCamelCase__				)
																				self.assertIsNotNone(UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				int:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=_config_zero_init(UpperCamelCase__				)
													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(config=UpperCamelCase__				)
																				for name, param in model.named_parameters():
																											if "embeddings" not in name and param.requires_grad:
																																		self.assertIn(
																																		    ((param.data.mean() * 1E9).round() / 1E9).item()     , [0.0, 1.0]     , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     , )






@require_natten
@require_vision
@require_torch
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						@cached_property
						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Any:
													'''simple docstring'''



													return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''				) if is_vision_available() else None





						@slow
						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''				).to(UpperCamelCase__				)
													__UpperCamelCase							=self.default_image_processor

													__UpperCamelCase							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													__UpperCamelCase							=image_processor(images=UpperCamelCase__     , return_tensors='''pt'''				).to(UpperCamelCase__				)

													# forward pass
													with torch.no_grad():
																				__UpperCamelCase							=model(**UpperCamelCase__				)

													# verify the logits
													__UpperCamelCase							=torch.Size((1, 1000)				)
													self.assertEqual(outputs.logits.shape     , UpperCamelCase__				)
													__UpperCamelCase							=torch.tensor([0.38_05, -0.86_76, -0.39_12]				).to(UpperCamelCase__				)
													self.assertTrue(torch.allclose(outputs.logits[0, :3]     , UpperCamelCase__     , atol=1E-4				)				)






@require_torch
@require_natten
class 		_lowercase (						unittest.TestCase					,					__a      ):



						"""simple docstring"""




						lowercase__       =  (NatBackbone,) if is_torch_available() else ()
						lowercase__       =  NatConfig





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)



