






'''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 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=64  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.02  ,      _lowercase=3  ,      _lowercase=4  ,      _lowercase=None  ,      _lowercase=2  ,      _lowercase=2  ,      _lowercase=2  ,      _lowercase=2  ,      _lowercase=4  ,      _lowercase=1  ,      )						->  Dict:
							lowercase_      :   Tuple        =			parent
							lowercase_      :   Any        =			batch_size
							lowercase_      :   Dict        =			seq_length
							lowercase_      :   int        =			is_training
							lowercase_      :   Tuple        =			use_input_mask
							lowercase_      :   Tuple        =			use_token_type_ids
							lowercase_      :   List[Any]        =			use_labels
							lowercase_      :   List[Any]        =			vocab_size
							lowercase_      :   List[str]        =			hidden_size
							lowercase_      :   Optional[int]        =			num_hidden_layers
							lowercase_      :   Dict        =			num_attention_heads
							lowercase_      :   Tuple        =			intermediate_size
							lowercase_      :   str        =			hidden_act
							lowercase_      :   str        =			hidden_dropout_prob
							lowercase_      :   List[Any]        =			attention_probs_dropout_prob
							lowercase_      :   List[str]        =			max_position_embeddings
							lowercase_      :   Any        =			type_vocab_size
							lowercase_      :   str        =			type_sequence_label_size
							lowercase_      :   Optional[Any]        =			initializer_range
							lowercase_      :   List[str]        =			num_labels
							lowercase_      :   Dict        =			num_choices
							lowercase_      :   Union[str, Any]        =			scope
							lowercase_      :   Any        =			q_groups
							lowercase_      :   Dict        =			k_groups
							lowercase_      :   Optional[int]        =			v_groups
							lowercase_      :   str        =			post_attention_groups
							lowercase_      :   Union[str, Any]        =			intermediate_groups
							lowercase_      :   Any        =			output_groups
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   int        =			ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size       )

							lowercase_      :   Dict        =			None
							if self.use_input_mask:
													lowercase_      :   Any        =			random_attention_mask([self.batch_size, self.seq_length]       )

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

							lowercase_      :   str        =			self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							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  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  List[Any]:
							lowercase_      :   Tuple        =			SqueezeBertModel(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[Any]        =			model(_lowercase  ,      _lowercase       )
							lowercase_      :   Tuple        =			model(_lowercase       )
							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  str:
							lowercase_      :   Dict        =			SqueezeBertForMaskedLM(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[str]        =			model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Optional[Any]        =			SqueezeBertForQuestionAnswering(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   Optional[int]        =			model(
							    _lowercase  ,      attention_mask=_lowercase  ,      start_positions=_lowercase  ,      end_positions=_lowercase       )
							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  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Tuple:
							lowercase_      :   Tuple        =			self.num_labels
							lowercase_      :   Any        =			SqueezeBertForSequenceClassification(_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[str]        =			model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   str        =			self.num_labels
							lowercase_      :   Any        =			SqueezeBertForTokenClassification(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()

							lowercase_      :   Dict        =			model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							lowercase_      :   Any        =			self.num_choices
							lowercase_      :   Any        =			SqueezeBertForMultipleChoice(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   Tuple        =			input_ids.unsqueeze(1       ).expand(-1  ,      self.num_choices  ,      -1       ).contiguous()
							lowercase_      :   Union[str, Any]        =			input_mask.unsqueeze(1       ).expand(-1  ,      self.num_choices  ,      -1       ).contiguous()
							lowercase_      :   Dict        =			model(
							    _lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase  ,      )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_choices)       )







	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   str        =			self.prepare_config_and_inputs()
							((lowercase_)  ,      (lowercase_)  ,      (lowercase_)  ,      (lowercase_)  ,      (lowercase_)  ,      (lowercase_))      :   Optional[int]        =			config_and_inputs
							lowercase_      :   str        =			{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      int        =  (
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	SCREAMING_SNAKE_CASE_					:      List[str]        =  (
	    {
	        'feature-extraction': SqueezeBertModel,
	        'fill-mask': SqueezeBertForMaskedLM,
	        'question-answering': SqueezeBertForQuestionAnswering,
	        'text-classification': SqueezeBertForSequenceClassification,
	        'token-classification': SqueezeBertForTokenClassification,
	        'zero-shot': SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_					:      Tuple        =  False
	SCREAMING_SNAKE_CASE_					:      int        =  True
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  False
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   Tuple        =			SqueezeBertModelTester(self       )
							lowercase_      :   Optional[int]        =			ConfigTester(self  ,      config_class=_lowercase  ,      dim=37       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							self.config_tester.run_common_tests()
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Dict        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   List[Any]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   List[Any]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   int        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_lowercase       )







	@slow
	def        lowerCamelCase__		(							self       )						->  Any:
							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_      :   Tuple        =			SqueezeBertModel.from_pretrained(_lowercase       )
													self.assertIsNotNone(_lowercase       )






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







	"""simple docstring"""



	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   Union[str, Any]        =			SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'       )

							lowercase_      :   Union[str, Any]        =			torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]       )
							lowercase_      :   Optional[int]        =			model(_lowercase       )[0]
							lowercase_      :   str        =			torch.Size((1, 3)       )
							self.assertEqual(output.shape  ,      _lowercase       )
							lowercase_      :   str        =			torch.tensor([[0.64_01, -0.03_49, -0.60_41]]       )
							self.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1E-4       )       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list[list[float]]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   list[list[float]]        =			[]
						for data in source_data:
												for i, el in enumerate(a							):
																		if len(a							) < i + 1:
																								data_lists.append([]							)
																		data_lists[i].append(float(a							)							)
						return data_lists






def 	_UpperCAmelCase    (  a		:	list[list[float]]						,			a		:	list[int]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   list[list[float]]        =			[]
						for dlist, weight in zip(a						,			a							):
												lowercase_      :   Tuple        =			min(a							)
												lowercase_      :   Any        =			max(a							)

												lowercase_      :   list[float]        =			[]
												# for weight 0 score is 1 - actual score
												if weight == 0:
																		for item in dlist:
																								try:
																														score.append(1 - ((item - mind) / (maxd - mind))							)
																								except ZeroDivisionError:
																														score.append(1							)

												elif weight == 1:
																		for item in dlist:
																								try:
																														score.append((item - mind) / (maxd - mind)							)
																								except ZeroDivisionError:
																														score.append(0							)

        # weight not 0 or 1
												else:
																		lowercase_      :   str        =			f"Invalid weight of {weight:f} provided"
																		raise ValueError(a							)

												score_lists.append(a							)

						return score_lists






def 	_UpperCAmelCase    (  a		:	list[list[float]]							)			->			list[float]:





						"""simple docstring"""
						lowercase_      :   list[float]        =			[0 for i in range(len(score_lists[0]							)							)]

						for slist in score_lists:
												for j, ele in enumerate(a							):
																		lowercase_      :   List[Any]        =			final_scores[j] + ele

						return final_scores






def 	_UpperCAmelCase    (  a		:	list[list[float]]						,			a		:	list[int]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   int        =			get_data(a							)
						lowercase_      :   Optional[int]        =			calculate_each_score(a						,			a							)
						lowercase_      :   Dict        =			generate_final_scores(a							)

						# append scores to source data
						for i, ele in enumerate(a							):
												source_data[i].append(a							)

						return source_data
