


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TFMobileBertForMaskedLM,
       TFMobileBertForMultipleChoice,
       TFMobileBertForNextSentencePrediction,
       TFMobileBertForPreTraining,
       TFMobileBertForQuestionAnswering,
       TFMobileBertForSequenceClassification,
       TFMobileBertForTokenClassification,
       TFMobileBertModel,
   )


@require_tf
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Optional[Any]    =		(
         (
             TFMobileBertModel,
             TFMobileBertForMaskedLM,
             TFMobileBertForNextSentencePrediction,
             TFMobileBertForPreTraining,
             TFMobileBertForQuestionAnswering,
             TFMobileBertForSequenceClassification,
             TFMobileBertForTokenClassification,
             TFMobileBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     A_       :	Union[str, Any]    =		(
         {
             'feature-extraction': TFMobileBertModel,
             'fill-mask': TFMobileBertForMaskedLM,
             'question-answering': TFMobileBertForQuestionAnswering,
             'text-classification': TFMobileBertForSequenceClassification,
             'token-classification': TFMobileBertForTokenClassification,
             'zero-shot': TFMobileBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_       :	int    =		False
     A_       :	Dict    =		False


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							str:
          _a    	=				super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase							)

          if return_labels:
               if model_class in get_values(__UpperCAmelCase							):
                    _a    	=				tf.zeros(self.model_tester.batch_size    ,				dtype=tf.intaa							)

          return inputs_dict


     class     __lowerCamelCase  (					a__				):

          '''simple docstring'''


          def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=99    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=37    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=512    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=None    ,				)       ->							Union[str, Any]:
               _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    	=				embedding_size


          def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
               _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
               if self.use_token_type_ids:
                    _a    	=				ids_tensor([self.batch_size, self.seq_length]    ,				self.type_vocab_size							)

               _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    	=				MobileBertConfig(
                   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    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				max_position_embeddings=self.max_position_embeddings    ,				type_vocab_size=self.type_vocab_size    ,				initializer_range=self.initializer_range    ,				embedding_size=self.embedding_size    ,				)

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


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[Any]:
               _a    	=				TFMobileBertModel(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)

               _a    	=				[input_ids, input_mask]
               _a    	=				model(__UpperCAmelCase							)

               _a    	=				model(__UpperCAmelCase							)

               self.parent.assertEqual(
                   result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)							)
               self.parent.assertEqual(result.pooler_output.shape    ,				(self.batch_size, self.hidden_size)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[Any]:
               _a    	=				TFMobileBertForMaskedLM(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
               _a    	=				TFMobileBertForNextSentencePrediction(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, 2)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[Any]:
               _a    	=				TFMobileBertForPreTraining(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(
                   result.prediction_logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)
               self.parent.assertEqual(result.seq_relationship_logits.shape    ,				(self.batch_size, 2)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Any:
               _a    	=				self.num_labels
               _a    	=				TFMobileBertForSequenceClassification(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							int:
               _a    	=				self.num_choices
               _a    	=				TFMobileBertForMultipleChoice(config=__UpperCAmelCase							)
               _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
               _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
               _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
               _a    	=				{
                   '''input_ids''': multiple_choice_inputs_ids,
                   '''attention_mask''': multiple_choice_input_mask,
                   '''token_type_ids''': multiple_choice_token_type_ids,
               }
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							str:
               _a    	=				self.num_labels
               _a    	=				TFMobileBertForTokenClassification(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__UpperCAmelCase							)
               self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)							)


          def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Tuple:
               _a    	=				TFMobileBertForQuestionAnswering(config=__UpperCAmelCase							)
               _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               _a    	=				model(__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 							_UpperCAmelCase      (      self							)       ->							List[Any]:
               _a    	=				self.prepare_config_and_inputs()
               (
                   (
                   _a
               )	,      (
                   _a
               )	,      (
                   _a
               )	,      (
                   _a
               )	,      (
                   _a
               )	,      (
                   _a
               )	,      (
                   _a
               )	,      
               )    	=				config_and_inputs
               _a    	=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
               return config, inputs_dict







     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				TFMobileBertModelTest.TFMobileBertModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				hidden_size=37							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["google/mobilebert-uncased"]:
               _a    	=				TFMobileBertModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







@require_tf
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''							)
          _a    	=				tf.constant([[0, 1, 2, 3, 4, 5]]							)
          _a    	=				model(__UpperCAmelCase							)[0]

          _a    	=				[1, 6, 30522]
          self.assertEqual(output.shape    ,				__UpperCAmelCase							)

          _a    	=				tf.constant(
              [
                  [
                      [-4.5919547, -9.248295, -9.645256],
                      [-6.7306175, -6.440284, -6.6052837],
                      [-7.2743506, -6.7847915, -6.024673],
                  ]
              ]							)
          tf.debugging.assert_near(output[:, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)



"""simple docstring"""






from pathlib import Path

import fire
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				Path(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       )
     dest_dir.mkdir(exist_ok=_lowerCAmelCase       )
     for path in src_dir.iterdir():
          _a    	=				[x.rstrip() for x in list(path.open().readlines()       )][:n]
          _a    	=				dest_dir.joinpath(path.name       )
          print(_lowerCAmelCase       )
          dest_path.open('''w'''       ).write('''\n'''.join(_lowerCAmelCase       )       )


if __name__ == "__main__":
   fire.Fire(minify)