




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

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


__lowerCamelCase   :						int				=			logging.get_logger(__name__)


__lowerCamelCase   :						Dict				=			{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

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







def 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	str    							=							set()
					__magic_name__							:	Dict    							=							word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)				)
										__magic_name__							:	List[Any]    							=							char
					return pairs


# Speech2Text2 has no max input length
__lowerCamelCase   :						str				=			{'''facebook/s2t-wav2vec2-large-en-de''': 10_24}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=VOCAB_FILES_NAMES
			UpperCamelCase__								=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__								=['''input_ids''', '''attention_mask''']

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

								__magic_name__							:	int    							=							do_lower_case

								with open(lowerCamelCase_       ,						encoding='''utf-8'''  ) as vocab_handle:
													__magic_name__							:	str    							=							json.load(lowerCamelCase_  )
								__magic_name__							:	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.'''  )

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

													__magic_name__							:	List[Any]    							=							[tuple(merge.split()[:2]  ) for merge in merges]
													__magic_name__							:	str    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )
													__magic_name__							:	int    							=							{}

			@property
			def 							UpperCAmelCase__       ( self			:   Any  )						->   int:
								return len(self.decoder  )

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								return dict(self.encoder       ,						**self.added_tokens_encoder  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Any  )						->   Optional[int]:
								__magic_name__							:	str    							=							tuple(token[:-1]  ) + (token[-1] + BPE_TOKEN_MERGES,)
								if token in self.cache:
													return self.cache[token]
								__magic_name__							:	List[Any]    							=							get_pairs(lowerCamelCase_  )

								if not pairs:
													return token

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

								if word.endswith(lowerCamelCase_  ):
													__magic_name__							:	str    							=							word.replace(lowerCamelCase_       ,						''''''  )

								__magic_name__							:	Union[str, Any]    							=							word.replace(''' '''       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[int]    							=							word
								return word

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Tuple  )						->   Dict:

								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:
													__magic_name__							:	Optional[Any]    							=							text.lower()

								__magic_name__							:	Tuple    							=							text.split()

								__magic_name__							:	Tuple    							=							[]
								for token in text:
													if token:
																		split_tokens.extend(list(self.bpe(lowerCamelCase_  ).split(''' '''  )  )  )

								return split_tokens

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   str  )						->   int:
								return self.encoder.get(lowerCamelCase_       ,						self.encoder.get(self.unk_token  )  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int  )						->   str:
								__magic_name__							:	Tuple    							=							self.decoder.get(lowerCamelCase_       ,						self.unk_token  )
								return result

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]  )						->   str:
								__magic_name__							:	Any    							=							''' '''.join(lowerCamelCase_  )

								# make sure @@ tokens are concatenated
								__magic_name__							:	List[Any]    							=							''''''.join(string.split(lowerCamelCase_  )  )

								return string


			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						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
								__magic_name__							:	List[str]    							=							os.path.join(
								    lowerCamelCase_       ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[Any]    							=							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'''  )

								__magic_name__							:	List[Any]    							=							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!'''  )
																							__magic_name__							:	Union[str, Any]    							=							token_index
																		writer.write(''' '''.join(lowerCamelCase_  ) + '''\n'''  )
																		index += 1

								return (vocab_file, merges_file)









import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Any    							=							os.getenv('''SM_HP_MP_PARAMETERS'''							,'''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__magic_name__							:	Tuple    							=							json.loads(__A				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__magic_name__							:	int    							=							os.getenv('''SM_FRAMEWORK_PARAMS'''							,'''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__magic_name__							:	List[Any]    							=							json.loads(__A				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''							,__A				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
	import smdistributed.modelparallel.torch as smp

	smp.init()


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=field(
			    default=''''''				,metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}				,)

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''       ,						lowerCamelCase_       ,						)

			@cached_property
			def 							UpperCAmelCase__       ( self			:   int  )						->   "torch.device":
								logger.info('''PyTorch: setting up devices'''  )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''  )
								if self.no_cuda:
													__magic_name__							:	Optional[int]    							=							torch.device('''cpu'''  )
													__magic_name__							:	int    							=							0
								elif is_sagemaker_model_parallel_available():
													__magic_name__							:	Any    							=							smp.local_rank()
													__magic_name__							:	int    							=							torch.device('''cuda'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	Optional[int]    							=							int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
													__magic_name__							:	Optional[int]    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	str    							=							1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													__magic_name__							:	List[str]    							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													__magic_name__							:	Dict    							=							torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	str    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	Dict    							=							1

								if device.type == "cuda":
													torch.cuda.set_device(lowerCamelCase_  )

								return device

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   int:
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[str]:
								return not is_sagemaker_model_parallel_available()


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return False



