




"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def      UpperCAmelCase				(			A__:    Any     )  ->     Optional[int]:
			__lowerCamelCase					:	Any	       =				np.inf

			def set_batch_size(A__:    Optional[Any]     ) -> None:
						nonlocal batch_size
						if isinstance(__UpperCAmelCase							,				__UpperCAmelCase     ):
									__lowerCamelCase					:	Optional[Any]	       =				min(__UpperCAmelCase							,				config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
						elif isinstance(__UpperCAmelCase							,				__UpperCAmelCase     ):
									__lowerCamelCase					:	List[Any]	       =				min(__UpperCAmelCase							,				config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
						elif isinstance(__UpperCAmelCase							,				__UpperCAmelCase     ) and feature.dtype == "binary":
									__lowerCamelCase					:	Any	       =				min(__UpperCAmelCase							,				config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

			_visit(__UpperCAmelCase							,				__UpperCAmelCase     )

			return None if batch_size is np.inf else batch_size

class __lowercase(      _snake_case				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	str	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	Union[str, Any]	       =				_PACKAGED_DATASETS_MODULES['parquet'][1]
								__lowerCamelCase					:	Union[str, Any]	       =				Parquet(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	hash=__a						,	**__a						,	)





					def     snake_case_				(    self     ):

								if self.streaming:
											__lowerCamelCase					:	Union[str, Any]	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Tuple	       =				None
											__lowerCamelCase					:	Optional[int]	       =				None
											__lowerCamelCase					:	Tuple	       =				None
											__lowerCamelCase					:	Tuple	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[str]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				dataset
								__lowerCamelCase					:	Any	       =				path_or_buf
								__lowerCamelCase					:	Union[str, Any]	       =				batch_size or get_writer_batch_size(dataset.features     )
								__lowerCamelCase					:	List[str]	       =				parquet_writer_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with open(self.path_or_buf						,	'wb+'     ) as buffer:
														__lowerCamelCase					:	List[Any]	       =				self._write(file_obj=__a						,	batch_size=__a						,	**self.parquet_writer_kwargs     )
								else:
											__lowerCamelCase					:	List[Any]	       =				self._write(file_obj=self.path_or_buf						,	batch_size=__a						,	**self.parquet_writer_kwargs     )
								return written





					def     snake_case_				(    self						,	__a						,	__a						,	**__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				0
								__lowerCamelCase					:	str	       =				parquet_writer_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	List[Any]	       =				self.dataset.features.arrow_schema

								__lowerCamelCase					:	List[str]	       =				pq.ParquetWriter(__a						,	schema=__a						,	**__a     )

								for offset in logging.tqdm(
								    range(0						,	len(self.dataset     )						,	__a     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating parquet from Arrow format'						,	):
											__lowerCamelCase					:	int	       =				query_table(
											    table=self.dataset._data						,	key=slice(__a						,	offset + batch_size     )						,	indices=self.dataset._indices if self.dataset._indices is not None else None						,	)
											writer.write_table(__a     )
											written += batch.nbytes
								writer.close()
								return written









"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

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


a_				:    Dict										=     logging.get_logger(__name__)


a_				:    List[Any]										=     {
    '''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_				:    Optional[Any]										=     '''</w>'''
a_				:    Dict										=     '''@@ '''





def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Optional[int]:
			__lowerCamelCase					:	Any	       =				set()
			__lowerCamelCase					:	Optional[int]	       =				word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						__lowerCamelCase					:	Optional[int]	       =				char
			return pairs


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






class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						Union[str, Any]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Dict     		= ['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						,	)

								__lowerCamelCase					:	List[Any]	       =				do_lower_case

								with open(__a						,	encoding='utf-8'     ) as vocab_handle:
											__lowerCamelCase					:	str	       =				json.load(__a     )
								__lowerCamelCase					:	int	       =				{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.'''     )

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

											__lowerCamelCase					:	List[str]	       =				[tuple(merge.split()[:2]     ) for merge in merges]
											__lowerCamelCase					:	Any	       =				dict(zip(__a						,	range(len(__a     )     )     )     )
											__lowerCamelCase					:	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     ):
								__lowerCamelCase					:	Optional[Any]	       =				tuple(token[:-1]     ) + (token[-1] + BPE_TOKEN_MERGES,)
								if token in self.cache:
											return self.cache[token]
								__lowerCamelCase					:	Union[str, Any]	       =				get_pairs(__a     )

								if not pairs:
											return token

								while True:
											__lowerCamelCase					:	Dict	       =				min(__a						,	key=lambda __a     : self.bpe_ranks.get(__a						,	float('inf'     )     )     )
											if bigram not in self.bpe_ranks:
														break
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				bigram
											__lowerCamelCase					:	Tuple	       =				[]
											__lowerCamelCase					:	Union[str, Any]	       =				0
											while i < len(__a     ):
														try:
																	__lowerCamelCase					:	Union[str, Any]	       =				word.index(__a						,	__a     )
														except ValueError:
																	new_word.extend(word[i:]     )
																	break
														else:
																	new_word.extend(word[i:j]     )
																	__lowerCamelCase					:	Any	       =				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
											__lowerCamelCase					:	List[str]	       =				tuple(__a     )
											__lowerCamelCase					:	Any	       =				new_word
											if len(__a     ) == 1:
														break
											else:
														__lowerCamelCase					:	Tuple	       =				get_pairs(__a     )
								__lowerCamelCase					:	Optional[int]	       =				' '.join(__a     )
								if word == "\n  " + BPE_TOKEN_MERGES:
											__lowerCamelCase					:	str	       =				'\n' + BPE_TOKEN_MERGES

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

								__lowerCamelCase					:	Optional[Any]	       =				word.replace(' '						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				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:
											__lowerCamelCase					:	Any	       =				text.lower()

								__lowerCamelCase					:	Union[str, Any]	       =				text.split()

								__lowerCamelCase					:	str	       =				[]
								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     ):
								__lowerCamelCase					:	int	       =				self.decoder.get(__a						,	self.unk_token     )
								return result
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Any	       =				' '.join(__a     )

								# make sure @@ tokens are concatenated
								__lowerCamelCase					:	Union[str, Any]	       =				''.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
								__lowerCamelCase					:	int	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
								__lowerCamelCase					:	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'     )

								__lowerCamelCase					:	Optional[int]	       =				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!'     )
																	__lowerCamelCase					:	Optional[int]	       =				token_index
														writer.write(' '.join(__a     ) + '\n'     )
														index += 1

								return (vocab_file, merges_file)



