"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
						lowerCAmelCase__  :  Union[str, Any]											=     None


lowerCAmelCase__  :  Tuple											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Dict											=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__  :  List[str]											=     {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__  :  Any											=     {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			VOCAB_FILES_NAMES
					snake_case__       =			PRETRAINED_VOCAB_FILES_MAP
					snake_case__       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__       =			["input_ids", "attention_mask"]
					snake_case__       =			XLMRobertaTokenizer







					def __init__(    self       :     str   ,lowerCamelCase__       :     Dict=None   ,lowerCamelCase__       :     Tuple=None   ,lowerCamelCase__       :     List[str]="<s>"   ,lowerCamelCase__       :     int="</s>"   ,lowerCamelCase__       :     Any="</s>"   ,lowerCamelCase__       :     Optional[int]="<s>"   ,lowerCamelCase__       :     Tuple="<unk>"   ,lowerCamelCase__       :     List[Any]="<pad>"   ,lowerCamelCase__       :     Optional[int]="<mask>"   ,**lowerCamelCase__       :     str   ,):
									# Mask token behave like a normal word, i.e. include the space before it
									UpperCAmelCase__					=							AddedToken(lowerCamelCase__   ,lstrip=lowerCamelCase__   ,rstrip=lowerCamelCase__      ) if isinstance(lowerCamelCase__   ,lowerCamelCase__      ) else mask_token

									super().__init__(
									    lowerCamelCase__   ,tokenizer_file=lowerCamelCase__   ,bos_token=lowerCamelCase__   ,eos_token=lowerCamelCase__   ,sep_token=lowerCamelCase__   ,cls_token=lowerCamelCase__   ,unk_token=lowerCamelCase__   ,pad_token=lowerCamelCase__   ,mask_token=lowerCamelCase__   ,**lowerCamelCase__   ,)

									UpperCAmelCase__					=							vocab_file
									UpperCAmelCase__					=							False if not self.vocab_file else True







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     List[int]   ,lowerCamelCase__       :     Optional[List[int]] = None      ):

									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									UpperCAmelCase__					=							[self.cls_token_id]
									UpperCAmelCase__					=							[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







					def 		__lowerCAmelCase						(    self       :     Tuple   ,lowerCamelCase__       :     List[int]   ,lowerCamelCase__       :     Optional[List[int]] = None      ):
									UpperCAmelCase__					=							[self.sep_token_id]
									UpperCAmelCase__					=							[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 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Optional[str] = None      ):
									if not self.can_save_slow_tokenizer:
													raise ValueError(
													    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
													    'tokenizer.'      )

									if not os.path.isdir(lowerCamelCase__      ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''      )
													return
									UpperCAmelCase__					=							os.path.join(
									    lowerCamelCase__   ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCamelCase__      ):
													copyfile(self.vocab_file   ,lowerCamelCase__      )

									return (out_vocab_file,)




"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__  :  Any											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"gpt2"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(    self       :     Any   ,lowerCamelCase__       :     List[Any]=50_257   ,lowerCamelCase__       :     Union[str, Any]=1_024   ,lowerCamelCase__       :     Dict=768   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=12   ,lowerCamelCase__       :     List[str]=None   ,lowerCamelCase__       :     List[str]="gelu_new"   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=1e-5   ,lowerCamelCase__       :     Optional[Any]=0.0_2   ,lowerCamelCase__       :     str="cls_index"   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[Any]=None   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]=50_256   ,lowerCamelCase__       :     Union[str, Any]=50_256   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     List[Any]=False   ,**lowerCamelCase__       :     Tuple   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							n_positions
									UpperCAmelCase__					=							n_embd
									UpperCAmelCase__					=							n_layer
									UpperCAmelCase__					=							n_head
									UpperCAmelCase__					=							n_inner
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							resid_pdrop
									UpperCAmelCase__					=							embd_pdrop
									UpperCAmelCase__					=							attn_pdrop
									UpperCAmelCase__					=							layer_norm_epsilon
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							scale_attn_weights
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							scale_attn_by_inverse_layer_idx
									UpperCAmelCase__					=							reorder_and_upcast_attn

									UpperCAmelCase__					=							bos_token_id
									UpperCAmelCase__					=							eos_token_id

									super().__init__(bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,**lowerCamelCase__      )






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     PretrainedConfig   ,lowerCamelCase__       :     str = "default"   ,lowerCamelCase__       :     List[PatchingSpec] = None   ,lowerCamelCase__       :     bool = False   ,):
									super().__init__(lowerCamelCase__   ,task=lowerCamelCase__   ,patching_specs=lowerCamelCase__   ,use_past=lowerCamelCase__      )
									if not getattr(self._config   ,'pad_token_id'   ,lowerCamelCase__      ):
													# TODO: how to do that better?
													UpperCAmelCase__					=							0







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
									if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__   ,direction='inputs'      )
													UpperCAmelCase__					=							{0: 'batch', 1: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}

									return common_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self._config.n_layer







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self._config.n_head







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     PreTrainedTokenizer   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[TensorType] = None   ,):
									UpperCAmelCase__					=							super(lowerCamelCase__   ,self      ).generate_dummy_inputs(
									    lowerCamelCase__   ,batch_size=lowerCamelCase__   ,seq_length=lowerCamelCase__   ,is_pair=lowerCamelCase__   ,framework=lowerCamelCase__      )

									# We need to order the input in the way they appears in the forward()
									UpperCAmelCase__					=							OrderedDict({'input_ids': common_inputs['input_ids']}      )

									# Need to add the past_keys
									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
													else:
																	import torch

																	UpperCAmelCase__				,     UpperCAmelCase__					=							common_inputs['input_ids'].shape
																	# Not using the same length for past_key_values
																	UpperCAmelCase__					=							seqlen + 2
																	UpperCAmelCase__					=							(
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	UpperCAmelCase__					=							[
																	    (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
																	]

									UpperCAmelCase__					=							common_inputs['attention_mask']
									if self.use_past:
													UpperCAmelCase__					=							ordered_inputs['attention_mask'].dtype
													UpperCAmelCase__					=							torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__   ,lowerCamelCase__   ,dtype=lowerCamelCase__      )]   ,dim=1      )

									return ordered_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 13



