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

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


_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)


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

_lowerCamelCase   :				Tuple     			=						{
    '''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'''
        ),
    },
}

_lowerCamelCase   :				List[str]     			=						'''</w>'''
_lowerCamelCase   :				Tuple     			=						'''@@ '''





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any]					):
					SCREAMING_SNAKE_CASE           =    set()
					SCREAMING_SNAKE_CASE           =    word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)					)
										SCREAMING_SNAKE_CASE           =    char
					return pairs


# Speech2Text2 has no max input length
_lowerCamelCase   :				Union[str, Any]     			=						{'''facebook/s2t-wav2vec2-large-en-de''': 10_24}


class   lowercase      (       a       ):
							lowercase__  :    Any									=							VOCAB_FILES_NAMES
							lowercase__  :    List[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Union[str, Any]									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[str]="<s>"		,							_UpperCamelCase				:   Any="<pad>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   List[str]="<unk>"		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   List[str]=None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			List[str]:



												'''simple docstring'''

												super().__init__(
												    unk_token=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							do_lower_case=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    do_lower_case

												with open(_UpperCamelCase		,							encoding="utf-8" ) as vocab_handle:
																	SCREAMING_SNAKE_CASE           =    json.load(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    {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." )

																	SCREAMING_SNAKE_CASE           =    None
																	SCREAMING_SNAKE_CASE           =    None
												else:
																	with open(_UpperCamelCase		,							encoding="utf-8" ) as merges_handle:
																						SCREAMING_SNAKE_CASE           =    merges_handle.read().split("\n" )[:-1]

																	SCREAMING_SNAKE_CASE           =    [tuple(merge.split()[:2] ) for merge in merges]
																	SCREAMING_SNAKE_CASE           =    dict(zip(_UpperCamelCase		,							range(len(_UpperCamelCase ) ) ) )
																	SCREAMING_SNAKE_CASE           =    {}




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

												return len(self.decoder )




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												return dict(self.encoder		,							**self.added_tokens_encoder )




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   List[str] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
												if token in self.cache:
																	return self.cache[token]
												SCREAMING_SNAKE_CASE           =    get_pairs(_UpperCamelCase )

												if not pairs:
																	return token

												while True:
																	SCREAMING_SNAKE_CASE           =    min(_UpperCamelCase		,							key=lambda _UpperCamelCase : self.bpe_ranks.get(_UpperCamelCase		,							float("inf" ) ) )
																	if bigram not in self.bpe_ranks:
																						break
																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    bigram
																	SCREAMING_SNAKE_CASE           =    []
																	SCREAMING_SNAKE_CASE           =    0
																	while i < len(_UpperCamelCase ):
																						try:
																											SCREAMING_SNAKE_CASE           =    word.index(_UpperCamelCase		,							_UpperCamelCase )
																						except ValueError:
																											new_word.extend(word[i:] )
																											break
																						else:
																											new_word.extend(word[i:j] )
																											SCREAMING_SNAKE_CASE           =    j

																						if word[i] == first and i < len(_UpperCamelCase ) - 1 and word[i + 1] == second:
																											new_word.append(first + second )
																											i += 2
																						else:
																											new_word.append(word[i] )
																											i += 1
																	SCREAMING_SNAKE_CASE           =    tuple(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    new_word
																	if len(_UpperCamelCase ) == 1:
																						break
																	else:
																						SCREAMING_SNAKE_CASE           =    get_pairs(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    " ".join(_UpperCamelCase )
												if word == "\n  " + BPE_TOKEN_MERGES:
																	SCREAMING_SNAKE_CASE           =    "\n" + BPE_TOKEN_MERGES

												if word.endswith(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    word.replace(_UpperCamelCase		,							"" )

												SCREAMING_SNAKE_CASE           =    word.replace(" "		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    word
												return word




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												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:
																	SCREAMING_SNAKE_CASE           =    text.lower()

												SCREAMING_SNAKE_CASE           =    text.split()

												SCREAMING_SNAKE_CASE           =    []
												for token in text:
																	if token:
																						split_tokens.extend(list(self.bpe(_UpperCamelCase ).split(" " ) ) )

												return split_tokens




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

												return self.encoder.get(_UpperCamelCase		,							self.encoder.get(self.unk_token ) )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   int )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.decoder.get(_UpperCamelCase		,							self.unk_token )
												return result




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[str] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    " ".join(_UpperCamelCase )

												# make sure @@ tokens are concatenated
												SCREAMING_SNAKE_CASE           =    "".join(string.split(_UpperCamelCase ) )

												return string





							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
																	return
												SCREAMING_SNAKE_CASE           =    os.path.join(
												    _UpperCamelCase		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
												SCREAMING_SNAKE_CASE           =    os.path.join(
												    _UpperCamelCase		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

												SCREAMING_SNAKE_CASE           =    0
												if self.bpe_ranks is None:
																	return (vocab_file,)

												with open(_UpperCamelCase		,							"w"		,							encoding="utf-8" ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,							key=lambda _UpperCamelCase : 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!" )
																											SCREAMING_SNAKE_CASE           =    token_index
																						writer.write(" ".join(_UpperCamelCase ) + "\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,
    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_vision_available, logging


if is_vision_available():
							import PIL


_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[int, float] = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BICUBIC		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[int, float]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			List[Any]:



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   List[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return normalize(_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   Any		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												if not valid_images(_UpperCamelCase ):
																	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 or resample is None:
																	raise ValueError("Size and resample must be specified if do_resize is True." )

												if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												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           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )




