


"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

_lowercase							   =				logging.get_logger(__name__)






def       _snake_case				(		snake_case__   :     Optional[Any]     ):

	if isinstance(snake_case__	,      (list, tuple)     ) and isinstance(videos[0]	,      (list, tuple)     ) and is_valid_image(videos[0][0]     ):
		return videos

	elif isinstance(snake_case__	,      (list, tuple)     ) and is_valid_image(videos[0]     ):
		return [videos]

	elif is_valid_image(snake_case__     ):
		return [[videos]]

	raise ValueError(F'Could not make batched video from {videos}'     )







class 				lowerCAmelCase_				(      _a    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    ["""pixel_values"""]


						def __init__( self     :      str  ,A_     :      List[Any] = True  ,A_     :      List[Any] = None  ,A_     :      Union[str, Any] = PILImageResampling.BILINEAR  ,A_     :      Tuple = True  ,A_     :      List[str] = None  ,A_     :      Optional[Any] = True  ,A_     :      Dict = 1 / 255  ,A_     :      List[str] = True  ,A_     :      Union[str, Any] = True  ,A_     :      Dict = None  ,A_     :      int = None  ,**A_     :      int  ,)     ->   None:
							super().__init__(**snake_case_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(snake_case_  ,default_to_square=snake_case_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(snake_case_  ,param_name='crop_size'      )

							A						   =   do_resize
							A						   =   size
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   resample
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   offset
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Dict = PILImageResampling.BILINEAR  ,A_     :      Tuple = None  ,**A_     :      Union[str, Any]  ,)     ->   np.ndarray:
							A						   =   get_size_dict(snake_case_  ,default_to_square=snake_case_      )
							if "shortest_edge" in size:
								A						   =   get_resize_output_image_size(snake_case_  ,size['shortest_edge']  ,default_to_square=snake_case_      )
							elif "height" in size and "width" in size:
								A						   =   (size['height'], size['width'])
							else:
								raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
							return resize(snake_case_  ,size=snake_case_  ,resample=snake_case_  ,data_format=snake_case_  ,**snake_case_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Union[str, Any]  ,A_     :      Any = None  ,**A_     :      Optional[Any]  ,)     ->   np.ndarray:
							A						   =   get_size_dict(snake_case_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'      )
							return center_crop(snake_case_  ,size=(size['height'], size['width'])  ,data_format=snake_case_  ,**snake_case_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]  ,A_     :      str  ,A_     :      Dict = True  ,A_     :      int = None  ,**A_     :      List[Any]  ,)     ->   str:
							A						   =   image.astype(np.floataa      )
							if offset:
								A						   =   image - (scale / 2)
							return rescale(snake_case_  ,scale=snake_case_  ,data_format=snake_case_  ,**snake_case_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      int  ,A_     :      int  ,A_     :      int  ,A_     :      Optional[int] = None  ,**A_     :      Optional[int]  ,)     ->   np.ndarray:
							return normalize(snake_case_  ,mean=snake_case_  ,std=snake_case_  ,data_format=snake_case_  ,**snake_case_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]  ,A_     :      Tuple = None  ,A_     :      Optional[Any] = None  ,A_     :      Dict = None  ,A_     :      Tuple = None  ,A_     :      Dict = None  ,A_     :      Optional[int] = None  ,A_     :      Dict = None  ,A_     :      List[str] = None  ,A_     :      Any = None  ,A_     :      List[Any] = None  ,A_     :      Union[str, Any] = None  ,A_     :      Dict = ChannelDimension.FIRST  ,)     ->   np.ndarray:
							if do_resize and size is None or resample is None:
								raise ValueError('Size and resample must be specified if do_resize is True.'      )

							if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.'      )

							if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

							if offset and not do_rescale:
								raise ValueError('For offset, do_rescale must also be set to True.'      )

							# All transformations expect numpy arrays.
							A						   =   to_numpy_array(snake_case_      )

							if do_resize:
								A						   =   self.resize(image=snake_case_  ,size=snake_case_  ,resample=snake_case_      )

							if do_center_crop:
								A						   =   self.center_crop(snake_case_  ,size=snake_case_      )

							if do_rescale:
								A						   =   self.rescale(image=snake_case_  ,scale=snake_case_  ,offset=snake_case_      )

							if do_normalize:
								A						   =   self.normalize(image=snake_case_  ,mean=snake_case_  ,std=snake_case_      )

							A						   =   to_channel_dimension_format(snake_case_  ,snake_case_      )
							return image




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      Any  ,A_     :      str = None  ,A_     :      Optional[Any] = None  ,A_     :      Dict = None  ,A_     :      str = None  ,A_     :      Any = None  ,A_     :      Optional[Any] = None  ,A_     :      Optional[Any] = None  ,A_     :      Tuple = None  ,A_     :      List[Any] = None  ,A_     :      Any = None  ,A_     :      List[Any] = None  ,A_     :      int = None  ,A_     :      str = ChannelDimension.FIRST  ,**A_     :      Any  ,)     ->   PIL.Image.Image:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   offset if offset is not None else self.offset
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(snake_case_  ,default_to_square=snake_case_      )
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(snake_case_  ,param_name='crop_size'      )

							if not valid_images(snake_case_      ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

							A						   =   make_batched(snake_case_      )

							A						   =   [
							    [
							        self._preprocess_image(
							            image=snake_case_  ,do_resize=snake_case_  ,size=snake_case_  ,resample=snake_case_  ,do_center_crop=snake_case_  ,crop_size=snake_case_  ,do_rescale=snake_case_  ,rescale_factor=snake_case_  ,offset=snake_case_  ,do_normalize=snake_case_  ,image_mean=snake_case_  ,image_std=snake_case_  ,data_format=snake_case_  ,)
							        for img in video
							    ]
							    for video in videos
							]

							A						   =   {'pixel_values': videos}
							return BatchFeature(data=snake_case_  ,tensor_type=snake_case_      )

"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{'''vocab_file''': '''spm_char.model'''}

_lowercase							   =				{
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_lowercase							   =				{
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Tuple      =    ['''input_ids''', '''attention_mask''']


						def __init__( self     :      List[str]  ,A_     :      int  ,A_     :      List[str]="<s>"  ,A_     :      Optional[Any]="</s>"  ,A_     :      Optional[Any]="<unk>"  ,A_     :      str="<pad>"  ,A_     :      Optional[Dict[str, Any]] = None  ,**A_     :      List[str]  ,)     ->   None:
							A						   =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_  ,eos_token=A_  ,unk_token=A_  ,pad_token=A_  ,sp_model_kwargs=self.sp_model_kwargs  ,**A_  ,)

							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							return self.sp_model.get_piece_size()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   {self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab


						def __getstate__( self     :      str      )     ->   Any:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[int]  ,A_     :      str      )     ->   Tuple:
							A						   =   d

							# for backward compatibility
							if not hasattr(self  ,'sp_model_kwargs'      ):
								A						   =   {}

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(self.vocab_file      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str      )     ->   List[str]:
							return self.sp_model.encode(A_  ,out_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							return self.sp_model.piece_to_id(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   List[Any]:
							A						   =   self.sp_model.IdToPiece(A_      )
							return token


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Optional[Any]      )     ->   List[str]:
							A						   =   []
							A						   =   ''
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									out_string += self.sp_model.decode(A_      ) + token
									A						   =   []
								else:
									current_sub_tokens.append(A_      )
							out_string += self.sp_model.decode(A_      )
							return out_string.strip()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      Optional[int]=None      )     ->   List[int]:
							if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_  ,token_ids_a=A_  ,already_has_special_tokens=A_      )

							A						   =   [1]
							if token_ids_a is None:
								return ([0] * len(A_      )) + suffix_ones
							return ([0] * len(A_      )) + ([0] * len(A_      )) + suffix_ones




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
								return
							A						   =   os.path.join(
							    A_  ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

							if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ) and os.path.isfile(self.vocab_file      ):
								copyfile(self.vocab_file  ,A_      )
							elif not os.path.isfile(self.vocab_file      ):
								with open(A_  ,'wb'      ) as fi:
									A						   =   self.sp_model.serialized_model_proto()
									fi.write(A_      )

							return (out_vocab_file,)