





'''simple docstring'''

from typing import Any







def 					a__				(				lowercase					:     Dict,					lowercase					:     List[Any],					lowercase					:     int,					lowercase					:     Tuple,					lowercase					:     Dict,					)		->		list:





     """simple docstring"""

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

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

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

               _UpperCamelCase				    =       arg_max

    # The final observation
     _UpperCamelCase				    =       observations_space[len(snake_case__      ) - 1]

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

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

     return result







def 					a__				(				lowercase					:     str,					lowercase					:     Union[str, Any],					lowercase					:     List[str],					lowercase					:     Tuple,					lowercase					:     Tuple,					)		->		None:





     """simple docstring"""

     _validate_not_empty(
         snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__,					)
     _validate_lists(snake_case__,					snake_case__      )
     _validate_dicts(
         snake_case__,					snake_case__,					snake_case__      )







def 					a__				(				lowercase					:     Optional[int],					lowercase					:     Optional[int],					lowercase					:     List[Any],					lowercase					:     Optional[Any],					lowercase					:     int,					)		->		None:





     """simple docstring"""

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







def 					a__				(				lowercase					:     Optional[Any],					lowercase					:     List[Any]      )		->		None:





     """simple docstring"""

     _validate_list(snake_case__,					'''observations_space'''      )
     _validate_list(snake_case__,					'''states_space'''      )







def 					a__				(				lowercase					:     str,					lowercase					:     Tuple      )		->		None:





     """simple docstring"""

     if not isinstance(_object,					snake_case__      ):
          _UpperCamelCase				    =       F"""{var_name} must be a list"""
          raise ValueError(snake_case__      )
     else:
          for x in _object:
               if not isinstance(snake_case__,					snake_case__      ):
                    _UpperCamelCase				    =       F"""{var_name} must be a list of strings"""
                    raise ValueError(snake_case__      )







def 					a__				(				lowercase					:     List[str],					lowercase					:     Any,					lowercase					:     Union[str, Any],					)		->		None:





     """simple docstring"""

     _validate_dict(snake_case__,					'''initial_probabilities''',					snake_case__      )
     _validate_nested_dict(snake_case__,					'''transition_probabilities'''      )
     _validate_nested_dict(snake_case__,					'''emission_probabilities'''      )







def 					a__				(				lowercase					:     Optional[Any],					lowercase					:     Tuple      )		->		None:





     """simple docstring"""

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







def 					a__				(				lowercase					:     List[Any],					lowercase					:     List[str],					lowercase					:     Union[str, Any],					lowercase					:     Union[str, Any] = False      )		->		None:





     """simple docstring"""

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


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

   testmod()









"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



