




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_mvp import MvpTokenizer


__lowerCAmelCase		:				List[str]       			=		logging.get_logger(__name__)


__lowerCAmelCase		:				Union[str, Any]       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCAmelCase		:				str       			=		{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase		:				Optional[int]       			=		{
    'RUCAIBox/mvp': 1024,
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Dict							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					MvpTokenizer





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

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

							a    =			add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							a    =			"post_processor"
							a    =			getattr(self.backend_tokenizer					,      __lowerCamelCase					,      __lowerCamelCase							)
							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"					,      __lowerCamelCase							) != add_prefix_space:
													a    =			add_prefix_space
													a    =			True

										if state.get("trim_offsets"					,      __lowerCamelCase							) != trim_offsets:
													a    =			trim_offsets
													a    =			True

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





				@property
				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							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					,      __lowerCamelCase				:     Dict							)					->     List[str]:
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else value
							a    =			value





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      *__lowerCamelCase				:     str					,      **__lowerCamelCase				:     int							)					->     BatchEncoding:
							a    =			kwargs.get("is_split_into_words"					,      __lowerCamelCase							)

							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(*__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      *__lowerCamelCase				:     Dict					,      **__lowerCamelCase				:     List[str]							)					->     BatchEncoding:
							a    =			kwargs.get("is_split_into_words"					,      __lowerCamelCase							)

							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(*__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							a    =			self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase							)
							return tuple(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict=None							)					->     List[Any]:
							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				:     List[Any]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							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]














import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								snake_case_				=							None
								if self.use_token_type_ids:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size					)

								snake_case_				=							RobertaConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			vocab_size=2					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





