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

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


A     :	Union[str, Any]    = logging.get_logger(__name__)


A     :	Optional[int]    = {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

A     :	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'
        ),
    },
}

A     :	int    = '</w>'
A     :	Dict    = '@@ '


def     __lowerCAmelCase			(			a__  )       ->  Any:
				__a										=    set()
				__a										=    word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								__a										=    char
				return pairs


# Speech2Text2 has no max input length
A     :	Optional[Any]    = {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case		,     _snake_case="<s>"		,     _snake_case="<pad>"		,     _snake_case="</s>"		,     _snake_case="<unk>"		,     _snake_case=False		,     _snake_case=None		,     **_snake_case		,     )    ->      List[Any]:


								'''simple docstring'''




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

								__a										=    do_lower_case

								with open(_snake_case		,     encoding='''utf-8'''				) as vocab_handle:
												__a										=    json.load(_snake_case				)
								__a										=    {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."""				)

												__a										=    None
												__a										=    None
								else:
												with open(_snake_case		,     encoding='''utf-8'''				) as merges_handle:
																__a										=    merges_handle.read().split('''\n'''				)[:-1]

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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return len(self.decoder				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




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

								if not pairs:
												return token

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

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

								if word.endswith(_snake_case				):
												__a										=    word.replace(_snake_case		,     ''''''				)

								__a										=    word.replace(''' '''		,     _snake_case				)
								__a										=    word
								return word




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[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:
												__a										=    text.lower()

								__a										=    text.split()

								__a										=    []
								for token in text:
												if token:
																split_tokens.extend(list(self.bpe(_snake_case				).split(''' '''				)				)				)

								return split_tokens




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    self.decoder.get(_snake_case		,     self.unk_token				)
								return result




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    ''' '''.join(_snake_case				)

								# make sure @@ tokens are concatenated
								__a										=    ''''''.join(string.split(_snake_case				)				)

								return string




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




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

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

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

								with open(_snake_case		,     '''w'''		,     encoding='''utf-8'''				) as writer:
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda _snake_case				: 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!'''				)
																				__a										=    token_index
																writer.write(''' '''.join(_snake_case				) + '''\n'''				)
																index += 1

								return (vocab_file, merges_file)

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A     :	List[str]    = logging.get_logger(__name__)

A     :	Optional[int]    = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 			__A( a			):
				snake_case_						   =    '''levit'''




				def __init__(     self		,     _snake_case=224		,     _snake_case=3		,     _snake_case=3		,     _snake_case=2		,     _snake_case=1		,     _snake_case=16		,     _snake_case=[128, 256, 384]		,     _snake_case=[4, 8, 12]		,     _snake_case=[4, 4, 4]		,     _snake_case=[16, 16, 16]		,     _snake_case=0		,     _snake_case=[2, 2, 2]		,     _snake_case=[2, 2, 2]		,     _snake_case=0.02		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    image_size
								__a										=    num_channels
								__a										=    kernel_size
								__a										=    stride
								__a										=    padding
								__a										=    hidden_sizes
								__a										=    num_attention_heads
								__a										=    depths
								__a										=    key_dim
								__a										=    drop_path_rate
								__a										=    patch_size
								__a										=    attention_ratio
								__a										=    mlp_ratio
								__a										=    initializer_range
								__a										=    [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4