


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A			:						List[Any]        =  logging.get_logger(__name__)


A			:						Union[str, Any]        =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A			:						str        =  {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A			:						Union[str, Any]        =  {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






@lru_cache()
def 					UpperCamelCase   (      )  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						(
		    list(range(ord("""!"""	)       ,       ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)       ,       ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)       ,       ord("""ÿ"""	) + 1	)	)
		)
		lowercase__	     =						bs[:]
		lowercase__	     =						0
		for b in range(2**8	):
				if b not in bs:
						bs.append(__magic_name__	)
						cs.append(2**8 + n	)
						n += 1
		lowercase__	     =						[chr(__magic_name__	) for n in cs]
		return dict(zip(__magic_name__       ,       __magic_name__	)	)






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						set()
		lowercase__	     =						word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				lowercase__	     =						char
		return pairs







class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					['''input_ids''', '''attention_mask''']

					def __init__(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any]="replace" ,   _UpperCAmelCase       :		Tuple="<s>" ,   _UpperCAmelCase       :		Dict="</s>" ,   _UpperCAmelCase       :		List[str]="</s>" ,   _UpperCAmelCase       :		Dict="<s>" ,   _UpperCAmelCase       :		int="<unk>" ,   _UpperCAmelCase       :		Any="<pad>" ,   _UpperCAmelCase       :		int="<mask>" ,   _UpperCAmelCase       :		Optional[int]=False ,   **_UpperCAmelCase       :		int ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else bos_token
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else eos_token
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else sep_token
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else cls_token
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else unk_token
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else mask_token

							super().__init__(
							    errors=_UpperCAmelCase ,   bos_token=_UpperCAmelCase ,   eos_token=_UpperCAmelCase ,   unk_token=_UpperCAmelCase ,   sep_token=_UpperCAmelCase ,   cls_token=_UpperCAmelCase ,   pad_token=_UpperCAmelCase ,   mask_token=_UpperCAmelCase ,   add_prefix_space=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

							with open(_UpperCAmelCase ,   encoding="""utf-8"""						) as vocab_handle:
									lowercase__	     =						json.load(_UpperCAmelCase						)
							lowercase__	     =						{v: k for k, v in self.encoder.items()}
							lowercase__	     =						errors  # how to handle errors in decoding
							lowercase__	     =						bytes_to_unicode()
							lowercase__	     =						{v: k for k, v in self.byte_encoder.items()}
							with open(_UpperCAmelCase ,   encoding="""utf-8"""						) as merges_handle:
									lowercase__	     =						merges_handle.read().split("""\n"""						)[1:-1]
							lowercase__	     =						[tuple(merge.split()						) for merge in bpe_merges]
							lowercase__	     =						dict(zip(_UpperCAmelCase ,   range(len(_UpperCAmelCase						)						)						)						)
							lowercase__	     =						{}
							lowercase__	     =						add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowercase__	     =						re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)

					@property
					def     lowerCamelCase__	(self       :		Tuple						)		->  Tuple:




							"""simple docstring"""


							return len(self.encoder						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Dict:




							"""simple docstring"""


							return dict(self.encoder ,   **self.added_tokens_encoder						)

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		List[Any]						)		->  str:




							"""simple docstring"""


							if token in self.cache:
									return self.cache[token]
							lowercase__	     =						tuple(_UpperCAmelCase						)
							lowercase__	     =						get_pairs(_UpperCAmelCase						)

							if not pairs:
									return token

							while True:
									lowercase__	     =						min(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: self.bpe_ranks.get(_UpperCAmelCase ,   float("""inf"""						)						)						)
									if bigram not in self.bpe_ranks:
											break
									lowercase__     ,      lowercase__	     =						bigram
									lowercase__	     =						[]
									lowercase__	     =						0
									while i < len(_UpperCAmelCase						):
											try:
													lowercase__	     =						word.index(_UpperCAmelCase ,   _UpperCAmelCase						)
											except ValueError:
													new_word.extend(word[i:]						)
													break
											else:
													new_word.extend(word[i:j]						)
													lowercase__	     =						j

											if word[i] == first and i < len(_UpperCAmelCase						) - 1 and word[i + 1] == second:
													new_word.append(first + second						)
													i += 2
											else:
													new_word.append(word[i]						)
													i += 1
									lowercase__	     =						tuple(_UpperCAmelCase						)
									lowercase__	     =						new_word
									if len(_UpperCAmelCase						) == 1:
											break
									else:
											lowercase__	     =						get_pairs(_UpperCAmelCase						)
							lowercase__	     =						""" """.join(_UpperCAmelCase						)
							lowercase__	     =						word
							return word

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Any						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						[]
							for token in re.findall(self.pat ,   _UpperCAmelCase						):
									lowercase__	     =						"""""".join(
									    self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase						).split(""" """						)						)
							return bpe_tokens

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		int						)		->  Any:




							"""simple docstring"""


							return self.encoder.get(_UpperCAmelCase ,   self.encoder.get(self.unk_token						)						)

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[Any]						)		->  Dict:




							"""simple docstring"""


							return self.decoder.get(_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						"""""".join(_UpperCAmelCase						)
							lowercase__	     =						bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8""" ,   errors=self.errors						)
							return text

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							if not os.path.isdir(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

							with open(_UpperCAmelCase ,   """w""" ,   encoding="""utf-8"""						) as f:
									f.write(json.dumps(self.encoder ,   indent=2 ,   sort_keys=_UpperCAmelCase ,   ensure_ascii=_UpperCAmelCase						) + """\n"""						)

							lowercase__	     =						0
							with open(_UpperCAmelCase ,   """w""" ,   encoding="""utf-8"""						) as writer:
									writer.write("""#version: 0.2\n"""						)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,   key=lambda _UpperCAmelCase						: kv[1]						):
											if index != token_index:
													logger.warning(
													    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
													    """ Please check that the tokenizer is not corrupted!"""						)
													lowercase__	     =						token_index
											writer.write(""" """.join(_UpperCAmelCase						) + """\n"""						)
											index += 1

							return vocab_file, merge_file

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None						)		->  List[int]:




							"""simple docstring"""


							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase__	     =						[self.cls_token_id]
							lowercase__	     =						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_UpperCAmelCase ,   token_ids_a=_UpperCAmelCase ,   already_has_special_tokens=_UpperCAmelCase						)

							if token_ids_a is None:
									return [1] + ([0] * len(_UpperCAmelCase						)) + [1]
							return [1] + ([0] * len(_UpperCAmelCase						)) + [1, 1] + ([0] * len(_UpperCAmelCase						)) + [1]

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None						)		->  List[int]:




							"""simple docstring"""


							lowercase__	     =						[self.sep_token_id]
							lowercase__	     =						[self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		List[str]=False ,   **_UpperCAmelCase       :		List[Any]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						kwargs.pop("""add_prefix_space""" ,   self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase						) > 0 and not text[0].isspace()):
									lowercase__	     =						""" """ + text
							return (text, kwargs)





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]=False ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Optional[int]=99 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		Dict=32 ,   _UpperCAmelCase       :		Tuple=5 ,   _UpperCAmelCase       :		Union[str, Any]=4 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=512 ,   _UpperCAmelCase       :		Tuple=12 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Tuple=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict="last" ,   _UpperCAmelCase       :		Any=None ,   _UpperCAmelCase       :		int=None ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_lengths
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						gelu_activation
							lowercase__	     =						sinusoidal_embeddings
							lowercase__	     =						causal
							lowercase__	     =						asm
							lowercase__	     =						n_langs
							lowercase__	     =						vocab_size
							lowercase__	     =						n_special
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						summary_type
							lowercase__	     =						use_proj
							lowercase__	     =						scope

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size						)
							lowercase__	     =						random_attention_mask([self.batch_size, self.seq_length]						)

							lowercase__	     =						None
							if self.use_input_lengths:
									lowercase__	     =						(
									    ids_tensor([self.batch_size] ,   vocab_size=2						) + self.seq_length - 2
									)  # small variation of seq_length

							lowercase__	     =						None
							if self.use_token_type_ids:
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.n_langs						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   2						).float()
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)

					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							return FlaubertConfig(
							    vocab_size=self.vocab_size ,   n_special=self.n_special ,   emb_dim=self.hidden_size ,   n_layers=self.num_hidden_layers ,   n_heads=self.num_attention_heads ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   gelu_activation=self.gelu_activation ,   sinusoidal_embeddings=self.sinusoidal_embeddings ,   asm=self.asm ,   causal=self.causal ,   n_langs=self.n_langs ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   summary_type=self.summary_type ,   use_proj=self.use_proj ,   )

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   lengths=_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[str] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						FlaubertWithLMHeadModel(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnsweringSimple(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)
							self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)						)

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Optional[int] ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnswering(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   p_mask=_UpperCAmelCase ,   )

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   )

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape ,   ()						)
							self.parent.assertEqual(result.start_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(result.start_top_index.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(
							    result.end_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(
							    result.end_top_index.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(result.cls_logits.shape ,   (self.batch_size,)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   labels=_UpperCAmelCase						)

							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.type_sequence_label_size)						)

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						FlaubertForTokenClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						FlaubertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						token_type_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      
							)	     =						config_and_inputs
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """token_type_ids""": token_type_ids,
							    """lengths""": input_lengths,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					A__     =					(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int						)		->  str:




							"""simple docstring"""


							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("""Fast"""						)
							):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

							return False

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any]=False						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

							if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)

							return inputs_dict

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   emb_dim=37						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase						)

					@slow
					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						FlaubertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)


					@slow
					@require_torch_gpu
					def     lowerCamelCase__	(self       :		Dict						)		->  int:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
											return

									lowercase__	     =						True
									lowercase__	     =						model_class(config=_UpperCAmelCase						)

									lowercase__	     =						self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)
									lowercase__	     =						torch.jit.trace(
									    _UpperCAmelCase ,   (inputs_dict["""input_ids"""].to("""cpu"""						), inputs_dict["""attention_mask"""].to("""cpu"""						))						)

									with tempfile.TemporaryDirectory() as tmp:
											torch.jit.save(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						)						)
											lowercase__	     =						torch.jit.load(os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						) ,   map_location=_UpperCAmelCase						)
											loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase						) ,   inputs_dict["""attention_mask"""].to(_UpperCAmelCase						)						)






@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""						)
							lowercase__	     =						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
							with torch.no_grad():
									lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 11, 768)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor(
							    [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)

