'''simple docstring'''




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
      from transformers import (
          TFAutoModel,
          TFAutoModelForCausalLM,
          TFAutoModelForMaskedLM,
          TFAutoModelForPreTraining,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSequenceClassification,
          TFAutoModelForTableQuestionAnswering,
          TFAutoModelForTokenClassification,
          TFAutoModelWithLMHead,
          TFBertForMaskedLM,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertModel,
          TFFunnelBaseModel,
          TFFunnelModel,
          TFGPTaLMHeadModel,
          TFRobertaForMaskedLM,
          TFTaForConditionalGeneration,
          TFTapasForQuestionAnswering,
      )
      from transformers.models.auto.modeling_tf_auto import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TF_MODEL_MAPPING,
      )
      from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					"""new-model"""


if is_tf_available():






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:str      	=					NewModelConfig






@require_tf
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @slow
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    '''bert-base-cased'''
      __magic_name__						       =    AutoConfig.from_pretrained(a__      )
      self.assertIsNotNone(a__      )
      self.assertIsInstance(a__			,				a__      )

      __magic_name__						       =    TFAutoModel.from_pretrained(a__      )
      self.assertIsNotNone(a__      )
      self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    '''bert-base-cased'''
      __magic_name__						       =    AutoConfig.from_pretrained(a__      )
      self.assertIsNotNone(a__      )
      self.assertIsInstance(a__			,				a__      )

      __magic_name__						       =    TFAutoModelForPreTraining.from_pretrained(a__      )
      self.assertIsNotNone(a__      )
      self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   Optional[int]      ):
      for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForCausalLM.from_pretrained(a__      )
           __magic_name__       ,  __magic_name__						       =    TFAutoModelForCausalLM.from_pretrained(a__			,				output_loading_info=a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   Any      ):
      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelWithLMHead.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   str      ):
      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForMaskedLM.from_pretrained(a__      )
           __magic_name__       ,  __magic_name__						       =    TFAutoModelForMaskedLM.from_pretrained(a__			,				output_loading_info=a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   Any      ):
      for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForSeqaSeqLM.from_pretrained(a__      )
           __magic_name__       ,  __magic_name__						       =    TFAutoModelForSeqaSeqLM.from_pretrained(a__			,				output_loading_info=a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   Tuple      ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForSequenceClassification.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForQuestionAnswering.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 @slow
 @require_tensorflow_probability
 def 			snake_case__  (   self		:   Optional[Any]      ):
      for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
           __magic_name__						       =    AutoConfig.from_pretrained(a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )

           __magic_name__						       =    TFAutoModelForTableQuestionAnswering.from_pretrained(a__      )
           __magic_name__       ,  __magic_name__						       =    TFAutoModelForTableQuestionAnswering.from_pretrained(
               a__			,				output_loading_info=a__      )
           self.assertIsNotNone(a__      )
           self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    TFAutoModelWithLMHead.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )
      self.assertEqual(model.num_parameters()			,				1_4410      )
      self.assertEqual(model.num_parameters(only_trainable=a__      )			,				1_4410      )



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    TFAutoModelWithLMHead.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )
      self.assertEqual(model.num_parameters()			,				1_4410      )
      self.assertEqual(model.num_parameters(only_trainable=a__      )			,				1_4410      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
      __magic_name__						       =    TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''      )
      self.assertIsInstance(a__			,				a__      )

      __magic_name__						       =    copy.deepcopy(model.config      )
      __magic_name__						       =    ['''FunnelBaseModel''']
      __magic_name__						       =    TFAutoModel.from_config(a__      )
      self.assertIsInstance(a__			,				a__      )

      with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(a__      )
           __magic_name__						       =    TFAutoModel.from_pretrained(a__      )
           self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Tuple      ):
      try:
           AutoConfig.register('''new-model'''			,				a__      )

           __magic_name__						       =    [
               TFAutoModel,
               TFAutoModelForCausalLM,
               TFAutoModelForMaskedLM,
               TFAutoModelForPreTraining,
               TFAutoModelForQuestionAnswering,
               TFAutoModelForSequenceClassification,
               TFAutoModelForTokenClassification,
           ]

           for auto_class in auto_classes:
                with self.subTest(auto_class.__name__      ):
                     # Wrong config class will raise an error
                     with self.assertRaises(a__      ):
                          auto_class.register(a__			,				a__      )
                     auto_class.register(a__			,				a__      )
                     # Trying to register something existing in the Transformers library will raise an error
                     with self.assertRaises(a__      ):
                          auto_class.register(a__			,				a__      )

                     # Now that the config is registered, it can be used as any other config with the auto-API
                     __magic_name__						       =    BertModelTester(self      ).get_config()
                     __magic_name__						       =    NewModelConfig(**tiny_config.to_dict()      )
                     __magic_name__						       =    auto_class.from_config(a__      )
                     self.assertIsInstance(a__			,				a__      )

                     with tempfile.TemporaryDirectory() as tmp_dir:
                          model.save_pretrained(a__      )
                          __magic_name__						       =    auto_class.from_pretrained(a__      )
                          self.assertIsInstance(a__			,				a__      )

      finally:
           if "new-model" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["new-model"]
           for mapping in (
               TF_MODEL_MAPPING,
               TF_MODEL_FOR_PRETRAINING_MAPPING,
               TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
               TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
               TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
               TF_MODEL_FOR_CAUSAL_LM_MAPPING,
               TF_MODEL_FOR_MASKED_LM_MAPPING,
           ):
                if NewModelConfig in mapping._extra_content:
                     del mapping._extra_content[NewModelConfig]



 def 			snake_case__  (   self		:   Optional[int]      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    TFAutoModel.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   List[Any]      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    TFAutoModel.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   int      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''			,				):
           __magic_name__						       =    TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Any      ):
      with self.assertRaisesRegex(a__			,				'''Use `from_pt=True` to load this model'''      ):
           __magic_name__						       =    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Make sure we have cached the model.
      __magic_name__						       =    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
      with RequestCounter() as counter:
           __magic_name__						       =    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
           self.assertEqual(counter.get_request_count			,				0      )
           self.assertEqual(counter.head_request_count			,				1      )
           self.assertEqual(counter.other_request_count			,				0      )

      # With a sharded checkpoint
      __magic_name__						       =    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''      )
      with RequestCounter() as counter:
           __magic_name__						       =    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''      )
           self.assertEqual(counter.get_request_count			,				0      )
           self.assertEqual(counter.head_request_count			,				1      )
           self.assertEqual(counter.other_request_count			,				0      )









'''simple docstring'''




from random import randint, random




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






