

import os
import unicodedata
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 SPIECE_UNDERLINE, logging


UpperCamelCase__  :      Tuple			=    logging.get_logger(__name__)

UpperCamelCase__  :      int			=    {"vocab_file": "spiece.model"}

UpperCamelCase__  :      str			=    {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

UpperCamelCase__  :      Union[str, Any]			=    {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
UpperCamelCase__  :      List[Any]			=    0
UpperCamelCase__  :      Optional[Any]			=    1
UpperCamelCase__  :      Dict			=    2
UpperCamelCase__  :      Tuple			=    3
UpperCamelCase__  :      Optional[int]			=    4



class       __snake_case  (    lowerCAmelCase__       ):
		__lowerCAmelCase     :	Tuple						 =  VOCAB_FILES_NAMES
		__lowerCAmelCase     :	Union[str, Any]						 =  PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase     :	Union[str, Any]						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase     :	Optional[int]						 =  'left'

		def __init__(					self						,		_A						,		_A=False						,		_A=True						,		_A=False						,		_A="<s>"						,		_A="</s>"						,		_A="<unk>"						,		_A="<sep>"						,		_A="<pad>"						,		_A="<cls>"						,		_A="<mask>"						,		_A=["<eop>", "<eod>"]						,		_A = None						,		**_A						,		):
						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else mask_token

						SCREAMING_SNAKE_CASE_									=						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=_A						,		remove_space=_A						,		keep_accents=_A						,		bos_token=_A						,		eos_token=_A						,		unk_token=_A						,		sep_token=_A						,		pad_token=_A						,		cls_token=_A						,		mask_token=_A						,		additional_special_tokens=_A						,		sp_model_kwargs=self.sp_model_kwargs						,		**_A						,		)

						SCREAMING_SNAKE_CASE_									=						3

						SCREAMING_SNAKE_CASE_									=						do_lower_case
						SCREAMING_SNAKE_CASE_									=						remove_space
						SCREAMING_SNAKE_CASE_									=						keep_accents
						SCREAMING_SNAKE_CASE_									=						vocab_file

						SCREAMING_SNAKE_CASE_									=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(_A)

		@property
		def 						lowerCAmelCase__     (					self):
						return len(self.sp_model)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						{self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab

		def __getstate__(					self):
						SCREAMING_SNAKE_CASE_									=						self.__dict__.copy()
						SCREAMING_SNAKE_CASE_									=						None
						return state

		def __setstate__(					self						,		_A):
						SCREAMING_SNAKE_CASE_									=						d

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

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

		def 						lowerCAmelCase__     (					self						,		_A):
						if self.remove_space:
										SCREAMING_SNAKE_CASE_									=						' '.join(inputs.strip().split())
						else:
										SCREAMING_SNAKE_CASE_									=						inputs
						SCREAMING_SNAKE_CASE_									=						outputs.replace('``'						,		'"').replace('\'\''						,		'"')

						if not self.keep_accents:
										SCREAMING_SNAKE_CASE_									=						unicodedata.normalize('NFKD'						,		_A)
										SCREAMING_SNAKE_CASE_									=						''.join([c for c in outputs if not unicodedata.combining(_A)])
						if self.do_lower_case:
										SCREAMING_SNAKE_CASE_									=						outputs.lower()

						return outputs

		def 						lowerCAmelCase__     (					self						,		_A):
						SCREAMING_SNAKE_CASE_									=						self.preprocess_text(_A)
						SCREAMING_SNAKE_CASE_									=						self.sp_model.encode(_A						,		out_type=_A)
						SCREAMING_SNAKE_CASE_									=						[]
						for piece in pieces:
										if len(_A) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
														SCREAMING_SNAKE_CASE_									=						self.sp_model.EncodeAsPieces(piece[:-1].replace(_A						,		''))
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]) == 1:
																						SCREAMING_SNAKE_CASE_									=						cur_pieces[1:]
																		else:
																						SCREAMING_SNAKE_CASE_									=						cur_pieces[0][1:]
														cur_pieces.append(piece[-1])
														new_pieces.extend(_A)
										else:
														new_pieces.append(_A)

						return new_pieces

		def 						lowerCAmelCase__     (					self						,		_A):
						return self.sp_model.PieceToId(_A)

		def 						lowerCAmelCase__     (					self						,		_A):
						return self.sp_model.IdToPiece(_A)

		def 						lowerCAmelCase__     (					self						,		_A):
						SCREAMING_SNAKE_CASE_									=						''.join(_A).replace(_A						,		' ').strip()
						return out_string

		def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = None						,		_A = True						,		**_A						,		):
						SCREAMING_SNAKE_CASE_									=						kwargs.pop('use_source_tokenizer'						,		_A)

						SCREAMING_SNAKE_CASE_									=						self.convert_ids_to_tokens(_A						,		skip_special_tokens=_A)

						# 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
						SCREAMING_SNAKE_CASE_									=						[]
						SCREAMING_SNAKE_CASE_									=						[]
						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(_A))
																		SCREAMING_SNAKE_CASE_									=						[]
														sub_texts.append(_A)
										else:
														current_sub_text.append(_A)
						if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(_A))

						# Mimic the behavior of the Rust tokenizer:
						# By default, there are no spaces between special tokens
						SCREAMING_SNAKE_CASE_									=						''.join(_A)

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

		def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
						SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
						SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]
						if token_ids_a is None:
										return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls

		def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):

						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_A						,		token_ids_a=_A						,		already_has_special_tokens=_A)

						if token_ids_a is not None:
										return ([0] * len(_A)) + [1] + ([0] * len(_A)) + [1, 1]
						return ([0] * len(_A)) + [1, 1]

		def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
						SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
						SCREAMING_SNAKE_CASE_									=						[2]

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


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

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

						return (out_vocab_file,)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


