"""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       snake_case__	(    __SCREAMING_SNAKE_CASE    ):


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						lowerCamelCase=99  ,						lowerCamelCase=32  ,						lowerCamelCase=5  ,						lowerCamelCase=4  ,						lowerCamelCase=64  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=512  ,						lowerCamelCase=16  ,						lowerCamelCase=2  ,						lowerCamelCase=0.02  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=None  ,						lowerCamelCase=2  ,						lowerCamelCase=2  ,						lowerCamelCase=2  ,						lowerCamelCase=2  ,						lowerCamelCase=4  ,						lowerCamelCase=1  ,						):
        __a	      =  parent
        __a	      =  batch_size
        __a	      =  seq_length
        __a	      =  is_training
        __a	      =  use_input_mask
        __a	      =  use_token_type_ids
        __a	      =  use_labels
        __a	      =  vocab_size
        __a	      =  hidden_size
        __a	      =  num_hidden_layers
        __a	      =  num_attention_heads
        __a	      =  intermediate_size
        __a	      =  hidden_act
        __a	      =  hidden_dropout_prob
        __a	      =  attention_probs_dropout_prob
        __a	      =  max_position_embeddings
        __a	      =  type_vocab_size
        __a	      =  type_sequence_label_size
        __a	      =  initializer_range
        __a	      =  num_labels
        __a	      =  num_choices
        __a	      =  scope
        __a	      =  q_groups
        __a	      =  k_groups
        __a	      =  v_groups
        __a	      =  post_attention_groups
        __a	      =  intermediate_groups
        __a	      =  output_groups


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

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

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

        __a	      =  self.get_config()

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


 def 		a__					(      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 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  SqueezeBertModel(config=_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()
        __a	      =  model(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
        __a	      =  model(_SCREAMING_SNAKE_CASE		)
        self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  SqueezeBertForMaskedLM(config=_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()
        __a	      =  model(_SCREAMING_SNAKE_CASE  ,						attention_mask=_SCREAMING_SNAKE_CASE  ,						labels=_SCREAMING_SNAKE_CASE		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  SqueezeBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()
        __a	      =  model(
            _SCREAMING_SNAKE_CASE  ,						attention_mask=_SCREAMING_SNAKE_CASE  ,						start_positions=_SCREAMING_SNAKE_CASE  ,						end_positions=_SCREAMING_SNAKE_CASE		)
        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 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  self.num_labels
        __a	      =  SqueezeBertForSequenceClassification(_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()
        __a	      =  model(_SCREAMING_SNAKE_CASE  ,						attention_mask=_SCREAMING_SNAKE_CASE  ,						labels=_SCREAMING_SNAKE_CASE		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  self.num_labels
        __a	      =  SqueezeBertForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()

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


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  self.num_choices
        __a	      =  SqueezeBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE		)
        model.to(_SCREAMING_SNAKE_CASE		)
        model.eval()
        __a	      =  input_ids.unsqueeze(1		).expand(-1  ,						self.num_choices  ,						-1		).contiguous()
        __a	      =  input_mask.unsqueeze(1		).expand(-1  ,						self.num_choices  ,						-1		).contiguous()
        __a	      =  model(
            _SCREAMING_SNAKE_CASE  ,						attention_mask=_SCREAMING_SNAKE_CASE  ,						labels=_SCREAMING_SNAKE_CASE  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices)		)






 def 		a__					(      self		):
        __a	      =  self.prepare_config_and_inputs()
        (__a)	      =  config_and_inputs
        __a	      =  {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_torch
class       snake_case__	(    __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		unittest.TestCase    ):
 _snake_case    : List[str]				   =		(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _snake_case    : List[Any]				   =		(
     {
         """feature-extraction""": SqueezeBertModel,
         """fill-mask""": SqueezeBertForMaskedLM,
         """question-answering""": SqueezeBertForQuestionAnswering,
         """text-classification""": SqueezeBertForSequenceClassification,
         """token-classification""": SqueezeBertForTokenClassification,
         """zero-shot""": SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _snake_case    : List[str]				   =		False
 _snake_case    : Optional[Any]				   =		True
 _snake_case    : Any				   =		False


 def 		a__					(      self		):
        __a	      =  SqueezeBertModelTester(self		)
        __a	      =  ConfigTester(self  ,						config_class=_SCREAMING_SNAKE_CASE  ,						dim=37		)


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


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_model(*_SCREAMING_SNAKE_CASE		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_masked_lm(*_SCREAMING_SNAKE_CASE		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*_SCREAMING_SNAKE_CASE		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_token_classification(*_SCREAMING_SNAKE_CASE		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_SCREAMING_SNAKE_CASE		)






 @slow
 def 		a__					(      self		):

        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a	      =  SqueezeBertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)






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


 @slow
 def 		a__					(      self		):
        __a	      =  SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"		)

        __a	      =  torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]		)
        __a	      =  model(_SCREAMING_SNAKE_CASE		)[0]
        __a	      =  torch.Size((1, 3)		)
        self.assertEqual(output.shape  ,						_SCREAMING_SNAKE_CASE		)
        __a	      =  torch.tensor([[0.6401, -0.0349, -0.6041]]		)
        self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						atol=1E-4		)		)





"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  Optional[int]												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
			    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
			    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       func(
									    __snake_case					,**__snake_case	)

			with open(__snake_case					,"wb"	) as f:
						f.write(json.dumps(__snake_case	).encode("utf-8"	)	)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_iterating()