
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE							:Any 			=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Tuple 			=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							:Optional[int] 			=    {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE							:List[str] 			=    {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''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_    =					RobertaTokenizer






	def __init__(	self :			Tuple							,A :			int=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			Tuple="replace"							,A :			str="<s>"							,A :			str="</s>"							,A :			Union[str, Any]="</s>"							,A :			int="<s>"							,A :			str="<unk>"							,A :			int="<pad>"							,A :			Union[str, Any]="<mask>"							,A :			List[Any]=False							,A :			Tuple=True							,**A :			Dict							,):
					super().__init__(
					    A							,A							,tokenizer_file=A							,errors=A							,bos_token=A							,eos_token=A							,sep_token=A							,cls_token=A							,unk_token=A							,pad_token=A							,mask_token=A							,add_prefix_space=A							,trim_offsets=A							,**A							,)

					__A             =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
					if pre_tok_state.get("add_prefix_space"							,A			) != add_prefix_space:
									__A             =			getattr(A							,pre_tok_state.pop("type"			)			)
									__A             =			add_prefix_space
									__A             =			pre_tok_class(**A			)

					__A             =			add_prefix_space

					__A             =			"post_processor"
					__A             =			getattr(self.backend_tokenizer							,A							,A			)
					if tokenizer_component_instance:
									__A             =			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             =			tuple(state["sep"]			)
									if "cls" in state:
													__A             =			tuple(state["cls"]			)

									__A             =			False

									if state.get("add_prefix_space"							,A			) != add_prefix_space:
													__A             =			add_prefix_space
													__A             =			True

									if state.get("trim_offsets"							,A			) != trim_offsets:
													__A             =			trim_offsets
													__A             =			True

									if changes_to_apply:
													__A             =			getattr(A							,state.pop("type"			)			)
													__A             =			component_class(**A			)
													setattr(self.backend_tokenizer							,A							,A			)






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					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     UpperCamelCase_  (	self :			Tuple							,A :			List[Any]			):
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else value
					__A             =			value






	def     UpperCamelCase_  (	self :			Dict							,*A :			List[str]							,**A :			List[str]			):
					__A             =			kwargs.get("is_split_into_words"							,A			)
					assert self.add_prefix_space or not is_split_into_words, (
					    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
					    "to use it with pretokenized inputs."
					)

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






	def     UpperCamelCase_  (	self :			Union[str, Any]							,*A :			str							,**A :			int			):
					__A             =			kwargs.get("is_split_into_words"							,A			)

					assert self.add_prefix_space or not is_split_into_words, (
					    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
					    "to use it with pretokenized inputs."
					)

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






	def     UpperCamelCase_  (	self :			str							,A :			str							,A :			Optional[str] = None			):
					__A             =			self._tokenizer.model.save(A							,name=A			)
					return tuple(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple							,A :			Tuple=None			):
					__A             =			[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     UpperCamelCase_  (	self :			Any							,A :			List[int]							,A :			Optional[List[int]] = None			):
					__A             =			[self.sep_token_id]
					__A             =			[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]


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

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

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


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"bloom"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "num_hidden_layers": "n_layer",
	    "num_attention_heads": "n_head",
	}






	def __init__(	self :			Optional[Any]							,A :			List[Any]=25_08_80							,A :			Optional[int]=64							,A :			List[Any]=2							,A :			Optional[int]=8							,A :			str=1E-5							,A :			str=0.02							,A :			int=True							,A :			Optional[Any]=1							,A :			int=2							,A :			str=False							,A :			Dict=0.0							,A :			List[Any]=0.0							,A :			str=1							,A :			List[Any]=False							,**A :			List[Any]							,):
					__A             =			vocab_size
					# Backward compatibility with n_embed kwarg
					__A             =			kwargs.pop("n_embed"							,A			)
					__A             =			hidden_size if n_embed is None else n_embed
					__A             =			n_layer
					__A             =			n_head
					__A             =			layer_norm_epsilon
					__A             =			initializer_range
					__A             =			use_cache
					__A             =			pretraining_tp
					__A             =			apply_residual_connection_post_layernorm
					__A             =			hidden_dropout
					__A             =			attention_dropout

					__A             =			bos_token_id
					__A             =			eos_token_id
					__A             =			slow_but_exact

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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






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






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
					if self.use_past:
									# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
									self.fill_with_past_key_values_(A							,direction="inputs"							,inverted_values_shape=A			)
									__A             =			{0: "batch", 1: "past_sequence + sequence"}
					else:
									__A             =			{0: "batch", 1: "sequence"}

					return common_inputs






	@property
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return self._config.n_layer






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self._config.n_head






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return 1E-3






	def     UpperCamelCase_  (	self :			Any							,A :			"PreTrainedTokenizer"							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,):
					__A             =			super(A							,self			).generate_dummy_inputs(
					    A							,batch_size=A							,seq_length=A							,is_pair=A							,framework=A			)

					# We need to order the input in the way they appears in the forward()
					__A             =			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

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

					__A             =			common_inputs["attention_mask"]
					if self.use_past:
									__A             =			ordered_inputs["attention_mask"].dtype
									__A             =			torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(A							,A							,dtype=A			)]							,dim=1			)

					return ordered_inputs







	@property
	def     UpperCamelCase_  (	self :			int			):
					return 13
