


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class        __lowerCamelCase      (				lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







				a_:		int					      =	BartTokenizer
				a_:		Tuple					      =	BartTokenizerFast
				a_:		str					      =	True
				a_:		List[Any]					      =	filter_roberta_detectors






				def        lowerCAmelCase__			(				self     :    List[Any]					):
								super().setUp()
								_lowerCAmelCase  						=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								_lowerCAmelCase  						=dict(zip(lowerCamelCase_	,							range(len(lowerCamelCase_					)					)					)					)
								_lowerCAmelCase  						=["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								_lowerCAmelCase  						={"""unk_token""": """<unk>"""}

								_lowerCAmelCase  						=os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase  						=os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""merges_file"""]					)
								with open(self.vocab_file	,							"""w"""	,							encoding="""utf-8"""					) as fp:
												fp.write(json.dumps(lowerCamelCase_					) + """\n"""					)
								with open(self.merges_file	,							"""w"""	,							encoding="""utf-8"""					) as fp:
												fp.write("""\n""".join(lowerCamelCase_					)					)






				def        lowerCAmelCase__			(				self     :    Optional[Any]	,							**lowerCamelCase_     :    Tuple					):
								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname	,							**lowerCamelCase_					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							**lowerCamelCase_     :    List[str]					):
								kwargs.update(self.special_tokens_map					)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,							**lowerCamelCase_					)






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    int					):
								return "lower newer", "lower newer"






				@cached_property
				def        lowerCAmelCase__			(				self     :    Tuple					):
								return BartTokenizer.from_pretrained("""facebook/bart-large"""					)






				@cached_property
				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								return BartTokenizerFast.from_pretrained("""facebook/bart-large"""					)






				@require_torch
				def        lowerCAmelCase__			(				self     :    List[Any]					):
								_lowerCAmelCase  						=["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								_lowerCAmelCase  						=[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_lowerCAmelCase  						=tokenizer(lowerCamelCase_	,							max_length=len(lowerCamelCase_					)	,							padding=lowerCamelCase_	,							return_tensors="""pt"""					)
												self.assertIsInstance(lowerCamelCase_	,							lowerCamelCase_					)

												self.assertEqual((2, 9)	,							batch.input_ids.shape					)
												self.assertEqual((2, 9)	,							batch.attention_mask.shape					)
												_lowerCAmelCase  						=batch.input_ids.tolist()[0]
												self.assertListEqual(lowerCamelCase_	,							lowerCamelCase_					)
												# Test that special tokens are reset






				@require_torch
				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								_lowerCAmelCase  						=["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_lowerCAmelCase  						=tokenizer(lowerCamelCase_	,							padding=lowerCamelCase_	,							return_tensors="""pt"""					)
												# check if input_ids are returned and no labels
												self.assertIn("""input_ids"""	,							lowerCamelCase_					)
												self.assertIn("""attention_mask"""	,							lowerCamelCase_					)
												self.assertNotIn("""labels"""	,							lowerCamelCase_					)
												self.assertNotIn("""decoder_attention_mask"""	,							lowerCamelCase_					)






				@require_torch
				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=[
								    """Summary of the text.""",
								    """Another summary.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_lowerCAmelCase  						=tokenizer(text_target=lowerCamelCase_	,							max_length=32	,							padding="""max_length"""	,							return_tensors="""pt"""					)
												self.assertEqual(32	,							targets["""input_ids"""].shape[1]					)






				@require_torch
				def        lowerCAmelCase__			(				self     :    Any					):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_lowerCAmelCase  						=tokenizer(
												    ["""I am a small frog""" * 1024, """I am a small frog"""]	,							padding=lowerCamelCase_	,							truncation=lowerCamelCase_	,							return_tensors="""pt"""					)
												self.assertIsInstance(lowerCamelCase_	,							lowerCamelCase_					)
												self.assertEqual(batch.input_ids.shape	,							(2, 1024)					)






				@require_torch
				def        lowerCAmelCase__			(				self     :    str					):
								_lowerCAmelCase  						=["""A long paragraph for summarization."""]
								_lowerCAmelCase  						=[
								    """Summary of the text.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_lowerCAmelCase  						=tokenizer(lowerCamelCase_	,							return_tensors="""pt"""					)
												_lowerCAmelCase  						=tokenizer(text_target=lowerCamelCase_	,							return_tensors="""pt"""					)
												_lowerCAmelCase  						=inputs["""input_ids"""]
												_lowerCAmelCase  						=targets["""input_ids"""]
												self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
												self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
												self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
												self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								pass






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"					):
																_lowerCAmelCase  						=self.rust_tokenizer_class.from_pretrained(lowerCamelCase_	,							**lowerCamelCase_					)
																_lowerCAmelCase  						=self.tokenizer_class.from_pretrained(lowerCamelCase_	,							**lowerCamelCase_					)
																_lowerCAmelCase  						="""A, <mask> AllenNLP sentence."""
																_lowerCAmelCase  						=tokenizer_r.encode_plus(lowerCamelCase_	,							add_special_tokens=lowerCamelCase_	,							return_token_type_ids=lowerCamelCase_					)
																_lowerCAmelCase  						=tokenizer_p.encode_plus(lowerCamelCase_	,							add_special_tokens=lowerCamelCase_	,							return_token_type_ids=lowerCamelCase_					)

																# token_type_ids should put 0 everywhere
																self.assertEqual(sum(tokens_r["""token_type_ids"""]					)	,							sum(tokens_p["""token_type_ids"""]					)					)

																# attention_mask should put 1 everywhere, so sum over length should be 1
																self.assertEqual(
																    sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)	,							sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)	,							)

																_lowerCAmelCase  						=tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
																_lowerCAmelCase  						=tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

																# Rust correctly handles the space before the mask while python doesnt
																self.assertSequenceEqual(tokens_p["""input_ids"""]	,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
																self.assertSequenceEqual(tokens_r["""input_ids"""]	,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

																self.assertSequenceEqual(
																    lowerCamelCase_	,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
																self.assertSequenceEqual(
																    lowerCamelCase_	,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

