




'''simple docstring'''


import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_snake_case : Any  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							'AutoTokenizer'
							lowercase_							     =							['tokenizer']

							lowercase_							     =							{
							    'semantic_prompt': 1,
							    'coarse_prompt': 2,
							    'fine_prompt': 2,
							}







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]=None   )     ->				Dict:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_   )

														_a      	=							speaker_embeddings







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Any				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    List[Any]="speaker_embeddings_path.json"				,     **lowerCAmelCase_			:    Any   )     ->				str:


														"""simple docstring"""


														if speaker_embeddings_dict_path is not None:
																					_a      	=							get_file_from_repo(
																					    lowerCAmelCase_				,     lowerCAmelCase_				,     subfolder=kwargs.pop('''subfolder'''				,     lowerCAmelCase_   )				,     cache_dir=kwargs.pop('''cache_dir'''				,     lowerCAmelCase_   )				,     force_download=kwargs.pop('''force_download'''				,     lowerCAmelCase_   )				,     proxies=kwargs.pop('''proxies'''				,     lowerCAmelCase_   )				,     resume_download=kwargs.pop('''resume_download'''				,     lowerCAmelCase_   )				,     local_files_only=kwargs.pop('''local_files_only'''				,     lowerCAmelCase_   )				,     use_auth_token=kwargs.pop('''use_auth_token'''				,     lowerCAmelCase_   )				,     revision=kwargs.pop('''revision'''				,     lowerCAmelCase_   )				,     )
																					if speaker_embeddings_path is None:
																												logger.warning(
																												    F'`{os.path.join(lowerCAmelCase_				,     lowerCAmelCase_   )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'   )
																												_a      	=							None
																					else:
																												with open(lowerCAmelCase_   ) as speaker_embeddings_json:
																																			_a      	=							json.load(lowerCAmelCase_   )
														else:
																					_a      	=							None

														_a      	=							AutoTokenizer.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )

														return cls(tokenizer=lowerCAmelCase_				,     speaker_embeddings=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Optional[Any]="speaker_embeddings_path.json"				,     lowerCAmelCase_			:    Dict="speaker_embeddings"				,     lowerCAmelCase_			:    bool = False				,     **lowerCAmelCase_			:    List[str]				,     )     ->				str:


														"""simple docstring"""


														if self.speaker_embeddings is not None:
																					os.makedirs(os.path.join(lowerCAmelCase_				,     lowerCAmelCase_				,     '''v2'''   )				,     exist_ok=lowerCAmelCase_   )

																					_a      	=							{}

																					_a      	=							save_directory

																					for prompt_key in self.speaker_embeddings:
																												if prompt_key != "repo_or_path":
																																			_a      	=							self._load_voice_preset(lowerCAmelCase_   )

																																			_a      	=							{}
																																			for key in self.speaker_embeddings[prompt_key]:
																																										np.save(
																																										    os.path.join(
																																										        embeddings_dict['''repo_or_path''']				,     lowerCAmelCase_				,     F'{prompt_key}_{key}'   )				,     voice_preset[key]				,     allow_pickle=lowerCAmelCase_				,     )
																																										_a      	=							os.path.join(lowerCAmelCase_				,     F'{prompt_key}_{key}.npy'   )

																																			_a      	=							tmp_dict

																					with open(os.path.join(lowerCAmelCase_				,     lowerCAmelCase_   )				,     '''w'''   ) as fp:
																												json.dump(lowerCAmelCase_				,     lowerCAmelCase_   )

														super().save_pretrained(lowerCAmelCase_				,     lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    str = None				,     **lowerCAmelCase_			:    Union[str, Any]   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.speaker_embeddings[voice_preset]

														_a      	=							{}
														for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																					if key not in voice_preset_paths:
																												raise ValueError(
																												    F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'   )

																					_a      	=							get_file_from_repo(
																					    self.speaker_embeddings.get('''repo_or_path'''				,     '''/'''   )				,     voice_preset_paths[key]				,     subfolder=kwargs.pop('''subfolder'''				,     lowerCAmelCase_   )				,     cache_dir=kwargs.pop('''cache_dir'''				,     lowerCAmelCase_   )				,     force_download=kwargs.pop('''force_download'''				,     lowerCAmelCase_   )				,     proxies=kwargs.pop('''proxies'''				,     lowerCAmelCase_   )				,     resume_download=kwargs.pop('''resume_download'''				,     lowerCAmelCase_   )				,     local_files_only=kwargs.pop('''local_files_only'''				,     lowerCAmelCase_   )				,     use_auth_token=kwargs.pop('''use_auth_token'''				,     lowerCAmelCase_   )				,     revision=kwargs.pop('''revision'''				,     lowerCAmelCase_   )				,     )
																					if path is None:
																												raise ValueError(
																												    F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"				,     "/"   )				,     voice_preset_paths[key]   )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'   )

																					_a      	=							np.load(lowerCAmelCase_   )

														return voice_preset_dict







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Optional[dict] = None   )     ->				Any:


														"""simple docstring"""


														for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																					if key not in voice_preset:
																												raise ValueError(F'Voice preset unrecognized, missing {key} as a key.'   )

																					if not isinstance(voice_preset[key]				,     np.ndarray   ):
																												raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray.'   )

																					if len(voice_preset[key].shape   ) != self.preset_shape[key]:
																												raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray.'   )







							def __call__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int=None				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Dict="pt"				,     lowerCAmelCase_			:    Dict=2_56				,     lowerCAmelCase_			:    Tuple=False				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    Union[str, Any]=False				,     **lowerCAmelCase_			:    int				,     )     ->				str:


														"""simple docstring"""


														if voice_preset is not None and not isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																					if (
																					    isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																					    and self.speaker_embeddings is not None
																					    and voice_preset in self.speaker_embeddings
																					):
																												_a      	=							self._load_voice_preset(lowerCAmelCase_   )

																					else:
																												if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ) and not voice_preset.endswith('''.npz'''   ):
																																			_a      	=							voice_preset + '''.npz'''

																												_a      	=							np.load(lowerCAmelCase_   )

														if voice_preset is not None:
																					self._validate_voice_preset_dict(lowerCAmelCase_				,     **lowerCAmelCase_   )
																					_a      	=							BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )

														_a      	=							self.tokenizer(
														    lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     padding='''max_length'''				,     max_length=lowerCAmelCase_				,     return_attention_mask=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     **lowerCAmelCase_				,     )

														if voice_preset is not None:
																					_a      	=							voice_preset

														return encoded_text



'''simple docstring'''




import numpy

# List of input, output pairs
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__="train"		):




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]		)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]		)
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    for i in range(len(snake_case__		)		):
        print(('''Actual output value:''', output(snake_case__						,							'''test'''		))		)
        print(('''Hypothesis output:''', calculate_hypothesis_value(snake_case__						,							'''test'''		))		)


if __name__ == "__main__":
      run_gradient_descent()
      print('\nTesting gradient descent for a linear hypothesis function.\n')
      test_gradient_descent()




