






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


A        =			logging.get_logger(__name__)


class 							__a							(  lowerCamelCase__						):





	'''simple docstring'''



	UpperCAmelCase__      :				List[Any]				    =						["""pixel_values"""]


	def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PILImageResampling.BILINEAR ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = 1 / 255 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					super().__init__(**__lowerCamelCase       )
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   size if size is not None else {'''shortest_edge''': 256}
					SCREAMING_SNAKE_CASE_   :	Any													=   get_size_dict(__lowerCamelCase ,    default_to_square=__lowerCamelCase       )
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
					SCREAMING_SNAKE_CASE_   :	Any													=   get_size_dict(__lowerCamelCase ,    param_name='crop_size'       )
					SCREAMING_SNAKE_CASE_   :	Dict													=   do_resize
					SCREAMING_SNAKE_CASE_   :	str													=   size
					SCREAMING_SNAKE_CASE_   :	Tuple													=   resample
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_center_crop
					SCREAMING_SNAKE_CASE_   :	Dict													=   crop_size
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   do_rescale
					SCREAMING_SNAKE_CASE_   :	List[str]													=   rescale_factor
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_normalize
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					SCREAMING_SNAKE_CASE_   :	str													=   image_std if image_std is not None else IMAGENET_STANDARD_STD


	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = PILImageResampling.BICUBIC ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					SCREAMING_SNAKE_CASE_   :	Dict													=   get_size_dict(__lowerCamelCase ,    default_to_square=__lowerCamelCase       )
					if "shortest_edge" not in size:
									raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
					SCREAMING_SNAKE_CASE_   :	str													=   get_resize_output_image_size(__lowerCamelCase ,    size=size['shortest_edge'] ,    default_to_square=__lowerCamelCase       )
					return resize(__lowerCamelCase ,    size=__lowerCamelCase ,    resample=__lowerCamelCase ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )


	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_size_dict(__lowerCamelCase       )
					if "height" not in size or "width" not in size:
									raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''       )
					return center_crop(__lowerCamelCase ,    size=(size['height'], size['width']) ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )


	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__       ):
					return rescale(__lowerCamelCase ,    scale=__lowerCamelCase ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )


	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					return normalize(__lowerCamelCase ,    mean=__lowerCamelCase ,    std=__lowerCamelCase ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )


	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   size if size is not None else self.size
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_size_dict(__lowerCamelCase ,    default_to_square=__lowerCamelCase       )
					SCREAMING_SNAKE_CASE_   :	Dict													=   resample if resample is not None else self.resample
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_center_crop if do_center_crop is not None else self.do_center_crop
					SCREAMING_SNAKE_CASE_   :	Any													=   crop_size if crop_size is not None else self.crop_size
					SCREAMING_SNAKE_CASE_   :	List[str]													=   get_size_dict(__lowerCamelCase ,    param_name='crop_size'       )
					SCREAMING_SNAKE_CASE_   :	List[str]													=   do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE_   :	str													=   rescale_factor if rescale_factor is not None else self.rescale_factor
					SCREAMING_SNAKE_CASE_   :	int													=   do_normalize if do_normalize is not None else self.do_normalize
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   image_mean if image_mean is not None else self.image_mean
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_std if image_std is not None else self.image_std

					SCREAMING_SNAKE_CASE_   :	Any													=   make_list_of_images(__lowerCamelCase       )

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

					if do_resize and size is None:
									raise ValueError('Size 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.'       )

					# All transformations expect numpy arrays.
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [to_numpy_array(__lowerCamelCase       ) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE_   :	str													=   [self.resize(image=__lowerCamelCase ,    size=__lowerCamelCase ,    resample=__lowerCamelCase       ) for image in images]

					if do_center_crop:
									SCREAMING_SNAKE_CASE_   :	List[Any]													=   [self.center_crop(image=__lowerCamelCase ,    size=__lowerCamelCase       ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE_   :	Any													=   [self.rescale(image=__lowerCamelCase ,    scale=__lowerCamelCase       ) for image in images]

					if do_normalize:
									SCREAMING_SNAKE_CASE_   :	Tuple													=   [self.normalize(image=__lowerCamelCase ,    mean=__lowerCamelCase ,    std=__lowerCamelCase       ) for image in images]

					SCREAMING_SNAKE_CASE_   :	int													=   [to_channel_dimension_format(__lowerCamelCase ,    __lowerCamelCase       ) for image in images]

					SCREAMING_SNAKE_CASE_   :	int													=   {'''pixel_values''': images}
					return BatchFeature(data=__lowerCamelCase ,    tensor_type=__lowerCamelCase       )






	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(__lowerCamelCase       ) != len(__lowerCamelCase       ):
													raise ValueError(
													    'Make sure that you pass in as many target sizes as the batch dimension of the logits'       )

									if is_torch_tensor(__lowerCamelCase       ):
													SCREAMING_SNAKE_CASE_   :	str													=   target_sizes.numpy()

									SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []

									for idx in range(len(__lowerCamelCase       )       ):
													SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0       ) ,    size=target_sizes[idx] ,    mode='bilinear' ,    align_corners=__lowerCamelCase       )
													SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   resized_logits[0].argmax(dim=0       )
													semantic_segmentation.append(__lowerCamelCase       )
					else:
									SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   logits.argmax(dim=1       )
									SCREAMING_SNAKE_CASE_   :	int													=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

					return semantic_segmentation







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


A        =			logging.get_logger(__name__)

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

A        =			{
    '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',
    }
}

A        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

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

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab


		def __getstate__(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   []
						SCREAMING_SNAKE_CASE_   :	int													=   ''
						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(UpperCamelCase__       ) + token
														SCREAMING_SNAKE_CASE_   :	Any													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + suffix_ones
						return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   os.path.join(
						    UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

						return (out_vocab_file,)