

'''simple docstring'''



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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


__snake_case					:      Optional[Any] 		=     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case					:      Dict 		=     {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__snake_case					:      Dict 		=     {
    'allenai/led-base-16384': 1_6384,
}



class lowerCamelCase					(    snake_case__		):



			'''simple docstring'''






			__snake_case             =			VOCAB_FILES_NAMES
			__snake_case             =			PRETRAINED_VOCAB_FILES_MAP
			__snake_case             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case             =			LEDTokenizer
			__snake_case             =			["""input_ids""", """attention_mask"""]


			def __init__( self			:		Union[str, Any]						,     lowerCAmelCase_			:		Optional[Any]=None						,     lowerCAmelCase_			:		List[Any]=None						,     lowerCAmelCase_			:		Union[str, Any]=None						,     lowerCAmelCase_			:		Dict="replace"						,     lowerCAmelCase_			:		Optional[Any]="<s>"						,     lowerCAmelCase_			:		Dict="</s>"						,     lowerCAmelCase_			:		Dict="</s>"						,     lowerCAmelCase_			:		Union[str, Any]="<s>"						,     lowerCAmelCase_			:		int="<unk>"						,     lowerCAmelCase_			:		List[str]="<pad>"						,     lowerCAmelCase_			:		Optional[Any]="<mask>"						,     lowerCAmelCase_			:		Tuple=False						,     lowerCAmelCase_			:		Optional[int]=True						,     **lowerCAmelCase_			:		int						,     )     ->						Optional[Any]:





									'''simple docstring'''
									super().__init__(
									    __lowercase						,     __lowercase						,     tokenizer_file=__lowercase						,     errors=__lowercase						,     bos_token=__lowercase						,     eos_token=__lowercase						,     sep_token=__lowercase						,     cls_token=__lowercase						,     unk_token=__lowercase						,     pad_token=__lowercase						,     mask_token=__lowercase						,     add_prefix_space=__lowercase						,     trim_offsets=__lowercase						,     **__lowercase						,     )

									A__	:  Dict           =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
									if pre_tok_state.get("""add_prefix_space"""						,     __lowercase					) != add_prefix_space:
															A__	:  str           =getattr(__lowercase						,     pre_tok_state.pop("""type"""					)					)
															A__	:  int           =add_prefix_space
															A__	:  Dict           =pre_tok_class(**__lowercase					)

									A__	:  List[str]           =add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A__	:  int           ='''post_processor'''
									A__	:  Union[str, Any]           =getattr(self.backend_tokenizer						,     __lowercase						,     __lowercase					)
									if tokenizer_component_instance:
															A__	:  int           =json.loads(tokenizer_component_instance.__getstate__()					)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																					A__	:  List[str]           =tuple(state["""sep"""]					)
															if "cls" in state:
																					A__	:  Any           =tuple(state["""cls"""]					)

															A__	:  List[str]           =False

															if state.get("""add_prefix_space"""						,     __lowercase					) != add_prefix_space:
																					A__	:  Dict           =add_prefix_space
																					A__	:  int           =True

															if state.get("""trim_offsets"""						,     __lowercase					) != trim_offsets:
																					A__	:  List[Any]           =trim_offsets
																					A__	:  str           =True

															if changes_to_apply:
																					A__	:  Optional[Any]           =getattr(__lowercase						,     state.pop("""type"""					)					)
																					A__	:  int           =component_class(**__lowercase					)
																					setattr(self.backend_tokenizer						,     __lowercase						,     __lowercase					)


			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def lowercase__		( self			:		Tuple					)     ->						Dict:





									'''simple docstring'''
									if self._mask_token is None:
															if self.verbose:
																					logger.error("""Using mask_token, but it is not set yet."""					)
															return None
									return str(self._mask_token					)


			@mask_token.setter
			def lowercase__		( self			:		Optional[Any]						,     lowerCAmelCase_			:		Union[str, Any]					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  str           =AddedToken(__lowercase						,     lstrip=__lowercase						,     rstrip=__lowercase					) if isinstance(__lowercase						,     __lowercase					) else value
									A__	:  Union[str, Any]           =value


			def lowercase__		( self			:		Tuple						,     *lowerCAmelCase_			:		Optional[int]						,     **lowerCAmelCase_			:		List[Any]					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  List[Any]           =kwargs.get("""is_split_into_words"""						,     __lowercase					)

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""					)

									return super()._batch_encode_plus(*__lowercase						,     **__lowercase					)


			def lowercase__		( self			:		Dict						,     *lowerCAmelCase_			:		Union[str, Any]						,     **lowerCAmelCase_			:		Optional[int]					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  Dict           =kwargs.get("""is_split_into_words"""						,     __lowercase					)

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""					)

									return super()._encode_plus(*__lowercase						,     **__lowercase					)


			def lowercase__		( self			:		List[Any]						,     lowerCAmelCase_			:		str						,     lowerCAmelCase_			:		Optional[str] = None					)     ->						int:





									'''simple docstring'''
									A__	:  Optional[Any]           =self._tokenizer.model.save(__lowercase						,     name=__lowercase					)
									return tuple(__lowercase					)


			def lowercase__		( self			:		str						,     lowerCAmelCase_			:		Optional[Any]						,     lowerCAmelCase_			:		List[Any]=None					)     ->						Union[str, Any]:





									'''simple docstring'''
									A__	:  Tuple           =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def lowercase__		( self			:		Optional[int]						,     lowerCAmelCase_			:		List[int]						,     lowerCAmelCase_			:		Optional[List[int]] = None					)     ->						Dict:





									'''simple docstring'''
									A__	:  Tuple           =[self.sep_token_id]
									A__	:  List[str]           =[self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]



			def lowercase__		( self			:		List[Any]						,     lowerCAmelCase_			:		Union[Dict[str, EncodedInput], BatchEncoding]						,     lowerCAmelCase_			:		Optional[int] = None						,     lowerCAmelCase_			:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,     lowerCAmelCase_			:		Optional[int] = None						,     lowerCAmelCase_			:		Optional[bool] = None						,     )     ->						Any:





									'''simple docstring'''
									A__	:  Optional[int]           =super()._pad(
									    encoded_inputs=__lowercase						,     max_length=__lowercase						,     padding_strategy=__lowercase						,     pad_to_multiple_of=__lowercase						,     return_attention_mask=__lowercase						,     )

									# Load from model defaults
									if return_attention_mask is None:
															A__	:  int           ='''attention_mask''' in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
															A__	:  Dict           =encoded_inputs[self.model_input_names[0]]
															# `global_attention_mask` need to have the same length as other (sequential) inputs.
															A__	:  List[str]           =len(encoded_inputs["""global_attention_mask"""]					) != len(__lowercase					)

															if needs_to_be_padded:
																					A__	:  List[str]           =len(__lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

																					if self.padding_side == "right":
																											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																											A__	:  str           =(
																											    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																											)
																					elif self.padding_side == "left":
																											A__	:  Optional[Any]           =[-1] * difference + encoded_inputs[
																											    '''global_attention_mask'''
																											]
																					else:
																											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

									return encoded_inputs








"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   Union[str, Any]       =os.path.sep.join(
											    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']      )

											from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




