

'''simple docstring'''

from typing import Any

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	dict,       lowercase__    :	dict,       lowercase__    :	dict,       ):






     '''simple docstring'''





     _validation(
         lowercase__,       lowercase__,       lowercase__,       lowercase__,       lowercase__,       )
     # 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(lowercase__			)			):
          __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(lowercase__			) - 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(lowercase__			) - 1,       -1,       -1			):
          result.append(lowercase__			)
          __lowercase      		=pointers[previous, observations_space[o]]
     result.reverse()

     return result

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       ):






     '''simple docstring'''





     _validate_not_empty(
         lowercase__,       lowercase__,       lowercase__,       lowercase__,       lowercase__,       )
     _validate_lists(lowercase__,       lowercase__			)
     _validate_dicts(
         lowercase__,       lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	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				(						lowercase__    :	Any,       lowercase__    :	Any			):






     '''simple docstring'''





     _validate_list(lowercase__,       'observations_space'			)
     _validate_list(lowercase__,       'states_space'			)

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	str			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Any,       lowercase__    :	Any,       ):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	str			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	str,       lowercase__    :	type,       lowercase__    :	bool = False			):






     '''simple docstring'''





     if not isinstance(_object,       lowercase__			):
          __lowercase      		=F'''{var_name} must be a dict'''
          raise ValueError(lowercase__			)
     if not all(isinstance(lowercase__,       lowercase__			) for x in _object			):
          __lowercase      		=F'''{var_name} all keys must be strings'''
          raise ValueError(lowercase__			)
     if not all(isinstance(lowercase__,       lowercase__			) 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(lowercase__			)


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

       testmod()





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


