"""simple docstring"""






from typing import Any


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list					,		lowerCAmelCase__     :list					,		lowerCAmelCase__     :dict					,		lowerCAmelCase__     :dict					,		lowerCAmelCase__     :dict					,		) ->    int:
      '''simple docstring'''


      _validation(
          lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		)
      # Creates data structures and fill initial step
      lowercase														=	{}
      lowercase														=	{}
      for state in states_space:
            lowercase														=	observations_space[0]
            lowercase														=	(
                initial_probabilities[state] * emission_probabilities[state][observation]
            )
            lowercase														=	None

      # Fills the data structure with the probabilities of
      # different transitions and pointers to previous states
      for o in range(1					,		len(lowerCAmelCase__		)		):
            lowercase														=	observations_space[o]
            lowercase														=	observations_space[o - 1]
            for state in states_space:
                  # Calculates the argmax for probability function
                  lowercase														=	''
                  lowercase														=	-1
                  for k_state in states_space:
                        lowercase														=	(
                            probabilities[(k_state, prior_observation)]
                            * transition_probabilities[k_state][state]
                            * emission_probabilities[state][observation]
                        )
                        if probability > max_probability:
                              lowercase														=	probability
                              lowercase														=	k_state

                                     # Update probabilities and pointers dicts
                  lowercase														=	(
                      probabilities[(arg_max, prior_observation)]
                      * transition_probabilities[arg_max][state]
                      * emission_probabilities[state][observation]
                  )

                  lowercase														=	arg_max

                   # The final observation
      lowercase														=	observations_space[len(lowerCAmelCase__		) - 1]

      # argmax for given final observation
      lowercase														=	''
      lowercase														=	-1
      for k_state in states_space:
            lowercase														=	probabilities[(k_state, final_observation)]
            if probability > max_probability:
                  lowercase														=	probability
                  lowercase														=	k_state
      lowercase														=	arg_max

      # Process pointers backwards
      lowercase														=	last_state
      lowercase														=	[]
      for o in range(len(lowerCAmelCase__		) - 1					,		-1					,		-1		):
            result.append(lowerCAmelCase__		)
            lowercase														=	pointers[previous, observations_space[o]]
      result.reverse()

      return result


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		) ->    Optional[Any]:
      '''simple docstring'''


      _validate_not_empty(
          lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		)
      _validate_lists(lowerCAmelCase__					,		lowerCAmelCase__		)
      _validate_dicts(
          lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		) ->    Optional[Any]:
      '''simple docstring'''


      if not all(
          [
              observations_space,
              states_space,
              initial_probabilities,
              transition_probabilities,
              emission_probabilities,
          ]		):
            raise ValueError("""There\'s an empty parameter"""		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any		) ->    Union[str, Any]:
      '''simple docstring'''


      _validate_list(lowerCAmelCase__					,		"""observations_space"""		)
      _validate_list(lowerCAmelCase__					,		"""states_space"""		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str		) ->    Union[str, Any]:
      '''simple docstring'''


      if not isinstance(_object					,		lowerCAmelCase__		):
            lowercase														=	f'{var_name} must be a list'
            raise ValueError(lowerCAmelCase__		)
      else:
            for x in _object:
                  if not isinstance(lowerCAmelCase__					,		lowerCAmelCase__		):
                        lowercase														=	f'{var_name} must be a list of strings'
                        raise ValueError(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		) ->    Optional[int]:
      '''simple docstring'''


      _validate_dict(lowerCAmelCase__					,		"""initial_probabilities"""					,		lowerCAmelCase__		)
      _validate_nested_dict(lowerCAmelCase__					,		"""transition_probabilities"""		)
      _validate_nested_dict(lowerCAmelCase__					,		"""emission_probabilities"""		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str		) ->    List[str]:
      '''simple docstring'''


      _validate_dict(_object					,		lowerCAmelCase__					,		lowerCAmelCase__		)
      for x in _object.values():
            _validate_dict(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :type					,		lowerCAmelCase__     :bool = False		) ->    Union[str, Any]:
      '''simple docstring'''


      if not isinstance(_object					,		lowerCAmelCase__		):
            lowercase														=	f'{var_name} must be a dict'
            raise ValueError(lowerCAmelCase__		)
      if not all(isinstance(lowerCAmelCase__					,		lowerCAmelCase__		) for x in _object		):
            lowercase														=	f'{var_name} all keys must be strings'
            raise ValueError(lowerCAmelCase__		)
      if not all(isinstance(lowerCAmelCase__					,		lowerCAmelCase__		) for x in _object.values()		):
            lowercase														=	'nested dictionary ' if nested else ''
            lowercase														=	f'{var_name} {nested_text}all values must be {value_type.__name__}'
            raise ValueError(lowerCAmelCase__		)


if __name__ == "__main__":
       from doctest import testmod

       testmod()




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_result in results.values():
   for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']			):
    UpperCamelCase_					:						Optional[Any] =					model_result['result'][batch_size][sequence_length]
    self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Dict =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'sgugger/tiny-distilbert-classification'
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      only_pretrain_model=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						str =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Optional[int] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						str =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						str =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[Any] =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						int =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'patrickvonplaten/t5-tiny-random'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      configs=[config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'			)			) == 0   ,      'Cannot do xla on CPU.'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					'sshleifer/tiny-gpt2'
  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						List[Any] =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      save_to_csv=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      inference_time_csv_file=os.path.join(snake_case   ,      'inf_time.csv'			)   ,      inference_memory_csv_file=os.path.join(snake_case   ,      'inf_mem.csv'			)   ,      env_info_csv_file=os.path.join(snake_case   ,      'env.csv'			)   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
   benchmark.run()
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_time.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_mem.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'env.csv'			)			).exists()			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'

  def _check_summary_is_not_empty(snake_case	:							Union[str, Any]			):
   self.assertTrue(hasattr(snake_case   ,      'sequential'			)			)
   self.assertTrue(hasattr(snake_case   ,      'cumulative'			)			)
   self.assertTrue(hasattr(snake_case   ,      'current'			)			)
   self.assertTrue(hasattr(snake_case   ,      'total'			)			)

  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						str =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      log_filename=os.path.join(snake_case   ,      'log.txt'			)   ,      log_print=snake_case   ,      trace_memory_line_by_line=snake_case   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					benchmark.run()
   _check_summary_is_not_empty(result.inference_summary			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'log.txt'			)			).exists()			)



