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)





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


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


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




