

import json
import os
from typing import Dict, List, Optional, Tuple

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

snake_case_        =			{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

snake_case_        =			'''</w>'''
snake_case_        =			'''@@ '''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	set()
     lowercase__   :       Optional[Any]				=	word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)      )
          lowercase__   :       Optional[int]				=	char
     return pairs


# Speech2Text2 has no max input length
snake_case_        =			{'''facebook/s2t-wav2vec2-large-en-de''': 1_024}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Dict														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	Union[str, Any]														=					["""input_ids""", """attention_mask"""]





    def __init__(				self						,       a						,       a="<s>"						,       a="<pad>"						,       a="</s>"						,       a="<unk>"						,       a=False						,       a=None						,       **a						,       ):
         super().__init__(
             unk_token=a						,       bos_token=a						,       eos_token=a						,       pad_token=a						,       do_lower_case=a						,       **a						,       )

         lowercase__   :       str				=	do_lower_case

         with open(a						,       encoding='utf-8') as vocab_handle:
              lowercase__   :       Tuple				=	json.load(a)
         lowercase__   :       Union[str, Any]				=	{v: k for k, v in self.encoder.items()}

         if merges_file is None:
              logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""")

              lowercase__   :       int				=	None
              lowercase__   :       List[Any]				=	None
         else:
              with open(a						,       encoding='utf-8') as merges_handle:
                   lowercase__   :       List[Any]				=	merges_handle.read().split('\n')[:-1]

              lowercase__   :       Optional[int]				=	[tuple(merge.split()[:2]) for merge in merges]
              lowercase__   :       Tuple				=	dict(zip(a						,       range(len(a))))
              lowercase__   :       List[str]				=	{}





    @property
    def 				snake_case_    (				self):
         return len(self.decoder)





    def 				snake_case_    (				self):
         return dict(self.encoder						,       **self.added_tokens_encoder)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
              return self.cache[token]
         lowercase__   :       Any				=	get_pairs(a)

         if not pairs:
              return token

         while True:
              lowercase__   :       List[str]				=	min(a						,       key=lambda a: self.bpe_ranks.get(a						,       float('inf')))
              if bigram not in self.bpe_ranks:
                   break
              lowercase__				,			lowercase__   :       Dict				=	bigram
              lowercase__   :       Union[str, Any]				=	[]
              lowercase__   :       int				=	0
              while i < len(a):
                   try:
                        lowercase__   :       Dict				=	word.index(a						,       a)
                   except ValueError:
                        new_word.extend(word[i:])
                        break
                   else:
                        new_word.extend(word[i:j])
                        lowercase__   :       Optional[int]				=	j

                   if word[i] == first and i < len(a) - 1 and word[i + 1] == second:
                        new_word.append(first + second)
                        i += 2
                   else:
                        new_word.append(word[i])
                        i += 1
              lowercase__   :       str				=	tuple(a)
              lowercase__   :       Union[str, Any]				=	new_word
              if len(a) == 1:
                   break
              else:
                   lowercase__   :       Optional[Any]				=	get_pairs(a)
         lowercase__   :       List[str]				=	' '.join(a)
         if word == "\n  " + BPE_TOKEN_MERGES:
              lowercase__   :       Optional[int]				=	'\n' + BPE_TOKEN_MERGES

         if word.endswith(a):
              lowercase__   :       Dict				=	word.replace(a						,       '')

         lowercase__   :       int				=	word.replace(' '						,       a)
         lowercase__   :       List[str]				=	word
         return word





    def 				snake_case_    (				self						,       a):

         if self.bpe_ranks is None:
              raise ValueError(
                  'This tokenizer was instantiated without a `merges.txt` file, so'
                  ' that it can only be used for decoding, not for encoding.'
                  'Make sure to provide `merges.txt` file at instantiation to enable '
                  'encoding.')

         if self.do_lower_case:
              lowercase__   :       int				=	text.lower()

         lowercase__   :       Optional[int]				=	text.split()

         lowercase__   :       Optional[int]				=	[]
         for token in text:
              if token:
                   split_tokens.extend(list(self.bpe(a).split(' ')))

         return split_tokens





    def 				snake_case_    (				self						,       a):
         return self.encoder.get(a						,       self.encoder.get(self.unk_token))





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	self.decoder.get(a						,       self.unk_token)
         return result





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	' '.join(a)

         # make sure @@ tokens are concatenated
         lowercase__   :       Optional[int]				=	''.join(string.split(a))

         return string





    def 				snake_case_    (				self						,       a						,       a = None):
         if not os.path.isdir(a):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__   :       Optional[Any]				=	os.path.join(
             a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__   :       List[Any]				=	os.path.join(
             a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

         with open(a						,       'w'						,       encoding='utf-8') as f:
              f.write(json.dumps(self.encoder						,       indent=2						,       sort_keys=a						,       ensure_ascii=a) + '\n')

         lowercase__   :       Optional[Any]				=	0
         if self.bpe_ranks is None:
              return (vocab_file,)

         with open(a						,       'w'						,       encoding='utf-8') as writer:
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,       key=lambda a: kv[1]):
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                            ' Please check that the tokenizer is not corrupted!')
                        lowercase__   :       Dict				=	token_index
                   writer.write(' '.join(a) + '\n')
                   index += 1

         return (vocab_file, merges_file)




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_        =			logging.get_logger(__name__)


if is_vision_available():
       import PIL


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[Any]														=					["""pixel_values"""]





    def __init__(				self						,       a = True						,       a = None						,       a = PILImageResampling.BICUBIC						,       a = True						,       a = None						,       a = True						,       a = 1 / 255						,       a = True						,       a = None						,       a = None						,       a = True						,       **a						,       ):
         super().__init__(**a)
         lowercase__   :       List[str]				=	size if size is not None else {'shortest_edge': 224}
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         lowercase__   :       Optional[int]				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       default_to_square=a						,       param_name='crop_size')

         lowercase__   :       List[str]				=	do_resize
         lowercase__   :       List[Any]				=	size
         lowercase__   :       Tuple				=	resample
         lowercase__   :       int				=	do_center_crop
         lowercase__   :       Union[str, Any]				=	crop_size
         lowercase__   :       int				=	do_rescale
         lowercase__   :       List[str]				=	rescale_factor
         lowercase__   :       Tuple				=	do_normalize
         lowercase__   :       str				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__   :       Optional[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__   :       List[Any]				=	do_convert_rgb





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





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         lowercase__   :       List[str]				=	get_size_dict(a)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
         return center_crop(a						,       size=(size['height'], size['width'])						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         return rescale(a						,       scale=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a = None						,       **a						,       ):
         return normalize(a						,       mean=a						,       std=a						,       data_format=a						,       **a)





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

         lowercase__   :       str				=	make_list_of_images(a)

         if not valid_images(a):
              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.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowercase__   :       str				=	[convert_to_rgb(a) for image in images]

         # All transformations expect numpy arrays.
         lowercase__   :       Dict				=	[to_numpy_array(a) for image in images]

         if do_resize:
              lowercase__   :       Tuple				=	[self.resize(image=a						,       size=a						,       resample=a) for image in images]

         if do_center_crop:
              lowercase__   :       List[str]				=	[self.center_crop(image=a						,       size=a) for image in images]

         if do_rescale:
              lowercase__   :       str				=	[self.rescale(image=a						,       scale=a) for image in images]

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

         lowercase__   :       Optional[int]				=	[to_channel_dimension_format(a						,       a) for image in images]

         lowercase__   :       Dict				=	{'pixel_values': images}
         return BatchFeature(data=a						,       tensor_type=a)

