




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       a					(       _A      ):

					'''simple docstring'''





					def __init__(     self       :      Tuple ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[Any]=13 ,   __snake_case       :      int=7 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Optional[Any]=True ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      int=True ,   __snake_case       :      Union[str, Any]=99 ,   __snake_case       :      Optional[int]=32 ,   __snake_case       :      Union[str, Any]=5 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      Union[str, Any]=64 ,   __snake_case       :      Dict="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      List[Any]=0.1 ,   __snake_case       :      int=5_12 ,   __snake_case       :      Optional[int]=16 ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      Any=0.02 ,   __snake_case       :      List[str]=3 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      str=None ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      Optional[int]=2 ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      List[str]=2 ,   __snake_case       :      Dict=4 ,   __snake_case       :      Any=1 ,   ):
									UpperCAmelCase_										=					parent
									UpperCAmelCase_										=					batch_size
									UpperCAmelCase_										=					seq_length
									UpperCAmelCase_										=					is_training
									UpperCAmelCase_										=					use_input_mask
									UpperCAmelCase_										=					use_token_type_ids
									UpperCAmelCase_										=					use_labels
									UpperCAmelCase_										=					vocab_size
									UpperCAmelCase_										=					hidden_size
									UpperCAmelCase_										=					num_hidden_layers
									UpperCAmelCase_										=					num_attention_heads
									UpperCAmelCase_										=					intermediate_size
									UpperCAmelCase_										=					hidden_act
									UpperCAmelCase_										=					hidden_dropout_prob
									UpperCAmelCase_										=					attention_probs_dropout_prob
									UpperCAmelCase_										=					max_position_embeddings
									UpperCAmelCase_										=					type_vocab_size
									UpperCAmelCase_										=					type_sequence_label_size
									UpperCAmelCase_										=					initializer_range
									UpperCAmelCase_										=					num_labels
									UpperCAmelCase_										=					num_choices
									UpperCAmelCase_										=					scope
									UpperCAmelCase_										=					q_groups
									UpperCAmelCase_										=					k_groups
									UpperCAmelCase_										=					v_groups
									UpperCAmelCase_										=					post_attention_groups
									UpperCAmelCase_										=					intermediate_groups
									UpperCAmelCase_										=					output_groups
					def   lowerCamelCase_       (     self       :      List[str]    ):
									UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    )

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

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

									UpperCAmelCase_										=					self.get_config()

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

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




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



@require_torch
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

					'''simple docstring'''





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




					@slow
					def   lowerCamelCase_       (     self       :      int    ):
									for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase_										=					SqueezeBertModel.from_pretrained(__snake_case    )
													self.assertIsNotNone(__snake_case    )



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

					'''simple docstring'''





					@slow
					def   lowerCamelCase_       (     self       :      List[str]    ):
									UpperCAmelCase_										=					SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''    )

									UpperCAmelCase_										=					torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]    )
									UpperCAmelCase_										=					model(__snake_case    )[0]
									UpperCAmelCase_										=					torch.Size((1, 3)    )
									self.assertEqual(output.shape ,   __snake_case    )
									UpperCAmelCase_										=					torch.tensor([[0.6_401, -0.0_349, -0.6_041]]    )
									self.assertTrue(torch.allclose(__snake_case ,   __snake_case ,   atol=1E-4    )    )











import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





