




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

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


__lowerCAmelCase	:       Optional[Any]     =	logging.get_logger(__name__)


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

__lowerCAmelCase	:       Dict     =	{
    '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[Any]     =	'</w>'
__lowerCAmelCase	:       Optional[int]     =	'@@ '




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			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
__lowerCAmelCase	:       Optional[Any]     =	{'facebook/s2t-wav2vec2-large-en-de': 1024}






class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							str											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							int											=      ["""input_ids""", """attention_mask"""]





				def __init__(   self       :  List[str]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Optional[Any]="<s>"      ,   __lowerCamelCase       :  int="<pad>"      ,   __lowerCamelCase       :  Optional[int]="</s>"      ,   __lowerCamelCase       :  List[str]="<unk>"      ,   __lowerCamelCase       :  List[str]=False      ,   __lowerCamelCase       :  List[Any]=None      ,   **__lowerCamelCase       :  Dict      ,   )      ->  str:
							super().__init__(
							    unk_token=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   do_lower_case=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

							a					=    do_lower_case

							with open(__lowerCamelCase      ,   encoding="utf-8"							) as vocab_handle:
										a					=    json.load(__lowerCamelCase							)
							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(__lowerCamelCase      ,   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(__lowerCamelCase      ,   range(len(__lowerCamelCase							)							)							)							)
										a					=    {}





				@property
				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							return len(self.decoder							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							return dict(self.encoder      ,   **self.added_tokens_encoder							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  List[str]							)      ->  Optional[int]:
							a					=    tuple(token[:-1]							) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
										return self.cache[token]
							a					=    get_pairs(__lowerCamelCase							)

							if not pairs:
										return token

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

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

							if word.endswith(__lowerCamelCase							):
										a					=    word.replace(__lowerCamelCase      ,   ""							)

							a					=    word.replace(" "      ,   __lowerCamelCase							)
							a					=    word
							return word





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Any:

							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(__lowerCamelCase							).split(" "							)							)							)

							return split_tokens





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  str							)      ->  int:
							return self.encoder.get(__lowerCamelCase      ,   self.encoder.get(self.unk_token							)							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  int							)      ->  str:
							a					=    self.decoder.get(__lowerCamelCase      ,   self.unk_token							)
							return result





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[str]							)      ->  str:
							a					=    " ".join(__lowerCamelCase							)

							# make sure @@ tokens are concatenated
							a					=    "".join(string.split(__lowerCamelCase							)							)

							return string





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a					=    os.path.join(
							    __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
							a					=    os.path.join(
							    __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

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

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

							with open(__lowerCamelCase      ,   "w"      ,   encoding="utf-8"							) as writer:
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,   key=lambda __lowerCamelCase							: 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(__lowerCamelCase							) + "\n"							)
													index += 1

							return (vocab_file, merges_file)












from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


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


__lowerCAmelCase	:       List[Any]     =	TypeVar('DatasetType', Dataset, IterableDataset)




def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[List[float]] = None,						A	:      Optional[int] = None,						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      Literal["first_exhausted", "all_exhausted"] = "first_exhausted",						):






			'''simple docstring'''







			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )
			else:
						return _interleave_iterable_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )






def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      int = 0,						):






			'''simple docstring'''







			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(A,						info=A,						split=A,						axis=A     )
			else:
						return _concatenate_iterable_datasets(A,						info=A,						split=A,						axis=A     )






