
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''spiece.model'''}

__A				={
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__A				={
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = []



	def __init__(    self						,  lowercase						,  lowercase="<unk>"						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="<pad>"						,  lowercase="[SEP]"						,  lowercase="[MASK]"						,  lowercase="[CLS]"						,  lowercase = None						,  **lowercase						,  )				->       None:
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else bos_token
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else eos_token
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else unk_token
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else pad_token
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else cls_token
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else sep_token

					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else mask_token

					lowerCamelCase_						=			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=lowercase						,  eos_token=lowercase						,  unk_token=lowercase						,  pad_token=lowercase						,  sep_token=lowercase						,  mask_token=lowercase						,  cls_token=lowercase						,  sp_model_kwargs=self.sp_model_kwargs						,  **lowercase						,  )

					lowerCamelCase_						=			vocab_file

					lowerCamelCase_						=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.sp_model.get_piece_size()



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab



	def __getstate__(    self )				->       Tuple:
					lowerCamelCase_						=			self.__dict__.copy()
					lowerCamelCase_						=			None
					return state



	def __setstate__(    self						,  lowercase )				->       Tuple:
					lowerCamelCase_						=			d

					# for backward compatibility
					if not hasattr(self						,  "sp_model_kwargs" ):
									lowerCamelCase_						=			{}

					lowerCamelCase_						=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[str]:
					return self.sp_model.encode(lowercase						,  out_type=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					return self.sp_model.piece_to_id(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			self.sp_model.IdToPiece(lowercase )
					return token



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Any:
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			""
					lowerCamelCase_						=			False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(lowercase ) + token
													lowerCamelCase_						=			True
													lowerCamelCase_						=			[]
									else:
													current_sub_tokens.append(lowercase )
													lowerCamelCase_						=			False
					out_string += self.sp_model.decode(lowercase )
					return out_string.strip()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = False						,  lowercase = None						,  lowercase = True						,  **lowercase						,  )				->       str:
					lowerCamelCase_						=			kwargs.pop("use_source_tokenizer"						,  lowercase )

					lowerCamelCase_						=			self.convert_ids_to_tokens(lowercase						,  skip_special_tokens=lowercase )

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[]
					for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
													continue
									if token in self.added_tokens_encoder:
													if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(lowercase ) )
																	lowerCamelCase_						=			[]
													sub_texts.append(lowercase )
									else:
													current_sub_text.append(lowercase )
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(lowercase ) )

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
									lowerCamelCase_						=			re.sub(R" (\[(MASK|SEP)\])"						,  R"\1"						,  " ".join(lowercase ) )
					else:
									lowerCamelCase_						=			"".join(lowercase )

					lowerCamelCase_						=			(
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
									lowerCamelCase_						=			self.clean_up_tokenization(lowercase )
									return clean_text
					else:
									return text



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					if not os.path.isdir(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file						,  lowercase )
					elif not os.path.isfile(self.vocab_file ):
									with open(lowercase						,  "wb" ) as fi:
													lowerCamelCase_						=			self.sp_model.serialized_model_proto()
													fi.write(lowercase )

					return (out_vocab_file,)



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]
					lowerCamelCase_						=			[self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = False )				->       List[int]:
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowercase						,  token_ids_a=lowercase						,  already_has_special_tokens=lowercase )

					if token_ids_a is None:
									return [1] + ([0] * len(lowercase )) + [1]
					return [1] + ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[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 ) * [0] + len(token_ids_a + sep ) * [1]




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


