





"""simple docstring"""






import os
from collections import deque

import torch
from torch.utils.data import Dataset




class 			__lowercase     (     __lowerCAmelCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase=""		,     _UpperCAmelCase="train"       ):
									assert os.path.isdir(lowerCamelCase__       )

									__a	:  Tuple         =					[]
									__a	:  List[Any]         =					os.listdir(lowerCamelCase__       )
									for story_filename in story_filenames_list:
													if "summary" in story_filename:
																	continue
													__a	:  Any         =					os.path.join(lowerCamelCase__		,     lowerCamelCase__       )
													if not os.path.isfile(lowerCamelCase__       ):
																	continue
													self.documents.append(lowerCamelCase__       )







					def __len__(  self       ):
									return len(self.documents       )







					def __getitem__(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					self.documents[idx]
									__a	:  List[Any]         =					document_path.split('''/'''       )[-1]
									with open(lowerCamelCase__		,     encoding='''utf-8'''       ) as source:
													__a	:  Tuple         =					source.read()
													__a	:  Any         =					process_story(lowerCamelCase__       )
									return document_name, story_lines, summary_lines






def       __A (     a_  :List[Any])						->						Optional[Any]:
				__a	:  Dict         =					list(filter(lambda a_: len(a_) != 0				,    [line.strip() for line in raw_story.split('''\n''')]))

				# for some unknown reason some lines miss a period, add it
				__a	:  Dict         =					[_add_missing_period(a_) for line in nonempty_lines]

				# gather article lines
				__a	:  List[str]         =					[]
				__a	:  Any         =					deque(a_)
				while True:
								try:
												__a	:  Tuple         =					lines.popleft()
												if element.startswith('''@highlight'''):
																break
												story_lines.append(a_)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

												 # gather summary lines
				__a	:  str         =					list(filter(lambda a_: not t.startswith('''@highlight''')				,    a_))

				return story_lines, summary_lines

def       __A (     a_  :Optional[int])						->						List[Any]:
				__a	:  Dict         =					['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
				if line.startswith('''@highlight'''):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."

def       __A (     a_  :Any				,    a_  :Dict				,    a_  :Union[str, Any])						->						List[Any]:



				if len(a_) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(a_)))
								return sequence

def       __A (     a_  :List[Any]				,    a_  :Optional[int])						->						Union[str, Any]:
				__a	:  Dict         =					torch.ones_like(a_)
				__a	:  str         =					sequence == pad_token_id
				__a	:  str         =					0
				return mask

def       __A (     a_  :Any				,    a_  :Optional[int]				,    a_  :Dict)						->						Tuple:
				__a	:  Tuple         =					[tokenizer.encode(a_) for line in story_lines]
				__a	:  str         =					[token for sentence in story_lines_token_ids for token in sentence]
				__a	:  List[Any]         =					[tokenizer.encode(a_) for line in summary_lines]
				__a	:  Tuple         =					[token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids






def       __A (     a_  :Optional[int]				,    a_  :Dict)						->						Union[str, Any]:
				__a	:  Tuple         =					[]
				for sequence in batch:
								__a	:  Optional[Any]         =					-1
								__a	:  int         =					[]
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2)
								batch_embeddings.append(a_)
				return torch.tensor(a_)






"""simple docstring"""






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, 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 (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=7		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=False		,     _UpperCAmelCase=True		,     _UpperCAmelCase=99		,     _UpperCAmelCase=32		,     _UpperCAmelCase=5		,     _UpperCAmelCase=4		,     _UpperCAmelCase=64		,     _UpperCAmelCase="gelu"		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=512		,     _UpperCAmelCase=16		,     _UpperCAmelCase=2		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=3		,     _UpperCAmelCase=4		,     _UpperCAmelCase=None		,     _UpperCAmelCase=2		,     _UpperCAmelCase=2		,     _UpperCAmelCase=2		,     _UpperCAmelCase=2		,     _UpperCAmelCase=4		,     _UpperCAmelCase=1		,     ):
									__a	:  Dict         =					parent
									__a	:  str         =					batch_size
									__a	:  Union[str, Any]         =					seq_length
									__a	:  Any         =					is_training
									__a	:  int         =					use_input_mask
									__a	:  Optional[int]         =					use_token_type_ids
									__a	:  int         =					use_labels
									__a	:  int         =					vocab_size
									__a	:  int         =					hidden_size
									__a	:  str         =					num_hidden_layers
									__a	:  str         =					num_attention_heads
									__a	:  Any         =					intermediate_size
									__a	:  Union[str, Any]         =					hidden_act
									__a	:  Optional[int]         =					hidden_dropout_prob
									__a	:  str         =					attention_probs_dropout_prob
									__a	:  int         =					max_position_embeddings
									__a	:  Union[str, Any]         =					type_vocab_size
									__a	:  List[str]         =					type_sequence_label_size
									__a	:  List[str]         =					initializer_range
									__a	:  Optional[int]         =					num_labels
									__a	:  List[str]         =					num_choices
									__a	:  int         =					scope
									__a	:  Union[str, Any]         =					q_groups
									__a	:  Dict         =					k_groups
									__a	:  List[str]         =					v_groups
									__a	:  Any         =					post_attention_groups
									__a	:  Optional[int]         =					intermediate_groups
									__a	:  List[str]         =					output_groups







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

									__a	:  Optional[Any]         =					None
									if self.use_input_mask:
													__a	:  int         =					random_attention_mask([self.batch_size, self.seq_length]       )

									__a	:  List[str]         =					None
									__a	:  Union[str, Any]         =					None
									__a	:  int         =					None
									if self.use_labels:
													__a	:  List[str]         =					ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )
													__a	:  int         =					ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels       )
													__a	:  Dict         =					ids_tensor([self.batch_size]		,     self.num_choices       )

									__a	:  int         =					self.get_config()

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







					def 	_lowerCamelCase						(  self       ):
									return SqueezeBertConfig(
									    embedding_size=self.hidden_size		,     vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     attention_probs_dropout_prob=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     initializer_range=self.initializer_range		,     q_groups=self.q_groups		,     k_groups=self.k_groups		,     v_groups=self.v_groups		,     post_attention_groups=self.post_attention_groups		,     intermediate_groups=self.intermediate_groups		,     output_groups=self.output_groups		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Dict         =					SqueezeBertModel(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Optional[Any]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )
									__a	:  Dict         =					model(_UpperCAmelCase       )
									self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Optional[Any]         =					SqueezeBertForMaskedLM(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  int         =					model(_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  int         =					SqueezeBertForQuestionAnswering(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Optional[int]         =					model(
									    _UpperCAmelCase		,     attention_mask=_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		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Any         =					self.num_labels
									__a	:  List[Any]         =					SqueezeBertForSequenceClassification(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  int         =					model(_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[Any]         =					self.num_labels
									__a	:  List[str]         =					SqueezeBertForTokenClassification(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  Any         =					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		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					self.num_choices
									__a	:  Union[str, Any]         =					SqueezeBertForMultipleChoice(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  int         =					input_ids.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Optional[Any]         =					input_mask.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Union[str, Any]         =					model(
									    _UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase		,     )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					self.prepare_config_and_inputs()
									((__a)    ,  (__a)    ,  (__a)    ,  (__a)    ,  (__a)    ,  (__a))	:  Optional[Any]         =					config_and_inputs
									__a	:  List[Any]         =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict






@require_torch
class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					__lowerCAmelCase					=					(
					    {
					        '''feature-extraction''': SqueezeBertModel,
					        '''fill-mask''': SqueezeBertForMaskedLM,
					        '''question-answering''': SqueezeBertForQuestionAnswering,
					        '''text-classification''': SqueezeBertForSequenceClassification,
					        '''token-classification''': SqueezeBertForTokenClassification,
					        '''zero-shot''': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__lowerCAmelCase					=					False
					__lowerCAmelCase					=					True
					__lowerCAmelCase					=					False







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					SqueezeBertModelTester(self       )
									__a	:  Dict         =					ConfigTester(self		,     config_class=_UpperCAmelCase		,     dim=37       )







					def 	_lowerCamelCase						(  self       ):
									self.config_tester.run_common_tests()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a	:  Any         =					SqueezeBertModel.from_pretrained(_UpperCAmelCase       )
													self.assertIsNotNone(_UpperCAmelCase       )






@require_sentencepiece
@require_tokenizers
@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''       )

									__a	:  Tuple         =					torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]       )
									__a	:  List[str]         =					model(_UpperCAmelCase       )[0]
									__a	:  int         =					torch.Size((1, 3)       )
									self.assertEqual(output.shape		,     _UpperCAmelCase       )
									__a	:  int         =					torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]       )
									self.assertTrue(torch.allclose(_UpperCAmelCase		,     _UpperCAmelCase		,     atol=1e-4       )       )