
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_							:

 def __init__(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							foo

 def __call__(   self  ):




        """simple docstring"""


        return self.foo



class 		lowercase_							(							datasets.Dataset					):

 @fingerprint_transform(inplace=__UpperCamelCase  )
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        return DatasetChild(self.data  ,						fingerprint=__UpperCamelCase  )

 @fingerprint_transform(inplace=__UpperCamelCase  )
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        return DatasetChild(self.data  ,						fingerprint=__UpperCamelCase  )



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							callable

 def __call__(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


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

 def __getstate__(   self  ):




        """simple docstring"""


        raise pickle.PicklingError()



class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 @require_transformers
 @pytest.mark.integration
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        from transformers import AutoTokenizer

        def encode(__UpperCamelCase  ):
               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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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_							(							__SCREAMING_SNAKE_CASE					):

 def lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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  ):
               if hasattr(args[-3]  ,						"""encode"""  ):
                      UpperCamelCase_											=							list(__UpperCamelCase  )
                      UpperCamelCase_											=							args[-3].encode()  # co_lnotab
                      UpperCamelCase_											=							args[-1_0].encode()  # co_code
               if hasattr(__UpperCamelCase  ,						"""co_posonlyargcount"""  ):
                      if len(__UpperCamelCase  ) == 1_6:
                             return CodeType(*__UpperCamelCase  )
                      elif len(__UpperCamelCase  ) == 1_5:
                             return CodeType(args[0]  ,						0  ,						*args[1:]  )
                      return CodeType(args[0]  ,						0  ,						0  ,						*args[1:]  )
               elif hasattr(__UpperCamelCase  ,						"""co_kwonlyargcount"""  ):
                      if len(__UpperCamelCase  ) == 1_6:
                             return CodeType(args[0]  ,						*args[2:]  )
                      elif len(__UpperCamelCase  ) == 1_5:
                             return CodeType(*__UpperCamelCase  )
                      return CodeType(args[0]  ,						0  ,						*args[1:]  )
               if len(__UpperCamelCase  ) == 1_6:
                      return CodeType(args[0]  ,						*args[3:]  )
               elif len(__UpperCamelCase  ) == 1_5:
                      return CodeType(args[0]  ,						*args[2:]  )
               return CodeType(*__UpperCamelCase  )

        def create_ipython_func(__UpperCamelCase  ,						__UpperCamelCase  ):
               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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							[0], [1]

        def func():
               return foo, bar

        UpperCamelCase_											=							"""__main__"""

        def globalvars_mocka_side_effect(__UpperCamelCase  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):
               return {"foo": foo, "bar": bar}

        def globalvars_mocka_side_effect(__UpperCamelCase  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):
               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_							(							__SCREAMING_SNAKE_CASE					):

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							Hasher.hash("""hello"""  )
        UpperCamelCase_											=							Hasher.hash("""hello"""  )
        UpperCamelCase_											=							Hasher.hash("""there"""  )
        self.assertEqual(__UpperCamelCase  ,						__UpperCamelCase  )
        self.assertNotEqual(__UpperCamelCase  ,						__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ):




        """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__					(    a__	:	Path  )		->						Dict:
       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(a__  )
       with script_patha.open("""w"""  ) as f:
              f.write(a__  )
       UpperCamelCase_											=							subprocess.check_output(["""python""", str(a__  )]  )
       UpperCamelCase_											=							subprocess.check_output(["""python""", str(a__  )]  )
       assert fingerprinta == fingerprinta




def 					lowerCamelCase__					(    )		->						Dict:
       UpperCamelCase_											=							{"""a""": [0, 1, 2]}
       UpperCamelCase_											=							DatasetChild(InMemoryTable.from_pydict(a__  )  )
       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__					(    )		->						Dict:
       UpperCamelCase_											=							{"""a""": [0, 1, 2]}

       class 		lowercase_							(							datasets.Dataset					):

        @fingerprint_transform(inplace=__UpperCamelCase  )
        def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




               """simple docstring"""


               return DummyDatasetChild(self.data  ,						fingerprint=__UpperCamelCase  )



       UpperCamelCase_											=							DummyDatasetChild(InMemoryTable.from_pydict(a__  )  ).func()

       class 		lowercase_							(							datasets.Dataset					):

        @fingerprint_transform(inplace=__UpperCamelCase  ,						version="""1.0.0"""  )
        def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




               """simple docstring"""


               return DummyDatasetChild(self.data  ,						fingerprint=__UpperCamelCase  )



       UpperCamelCase_											=							DummyDatasetChild(InMemoryTable.from_pydict(a__  )  ).func()

       class 		lowercase_							(							datasets.Dataset					):

        @fingerprint_transform(inplace=__UpperCamelCase  ,						version="""2.0.0"""  )
        def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




               """simple docstring"""


               return DummyDatasetChild(self.data  ,						fingerprint=__UpperCamelCase  )



       UpperCamelCase_											=							DummyDatasetChild(InMemoryTable.from_pydict(a__  )  ).func()

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







def 					lowerCamelCase__					(    )		->						int:
       # AttributeError: module 'dill._dill' has no attribute 'stack'
       UpperCamelCase_											=							Hasher()
       hasher.update(lambda a__  : x  )





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )



