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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
 from ...tokenization_utils_base import TextInput
from ...utils import logging


_snake_case      = logging.get_logger(__name__)

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

_snake_case      = {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_snake_case      = {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

_snake_case      = '''▁'''
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_				:					Dict       						=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_				:					Any       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_				:					int       						=["input_ids", "attention_mask"]

 def __init__(						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]="</s>"					, SCREAMING_SNAKE_CASE__			:			List[str]="<unk>"					, SCREAMING_SNAKE_CASE__			:			str="<pad>"					, SCREAMING_SNAKE_CASE__			:			Any=1_00					, SCREAMING_SNAKE_CASE__			:			Dict=None					, SCREAMING_SNAKE_CASE__			:			Optional[Dict[str, Any]] = None					, SCREAMING_SNAKE_CASE__			:			Any=True					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase						    =					[F'<extra_id_{i}>' for i in range(SCREAMING_SNAKE_CASE__   )]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase						    =					len(set(filter(lambda SCREAMING_SNAKE_CASE__   : bool('extra_id' in str(SCREAMING_SNAKE_CASE__   )   )					, SCREAMING_SNAKE_CASE__   )   )   )
           if extra_tokens != extra_ids:
                raise ValueError(
                    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                    ' tokens'   )
      if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )

      UpperCamelCase						    =					legacy
      UpperCamelCase						    =					{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=SCREAMING_SNAKE_CASE__					, unk_token=SCREAMING_SNAKE_CASE__					, pad_token=SCREAMING_SNAKE_CASE__					, extra_ids=SCREAMING_SNAKE_CASE__					, additional_special_tokens=SCREAMING_SNAKE_CASE__					, sp_model_kwargs=self.sp_model_kwargs					, legacy=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					vocab_file
      UpperCamelCase						    =					extra_ids

      UpperCamelCase						    =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(SCREAMING_SNAKE_CASE__   )

 @staticmethod
 def      __lowerCAmelCase  (						SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase						    =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    'This tokenizer was incorrectly instantiated with a model max length of'
                    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                    F' {pretrained_model_name_or_path} automatically truncating your input to'
                    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'					, SCREAMING_SNAKE_CASE__					, )

      return max_model_length

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






      """simple docstring"""



      return self.sp_model.get_piece_size() + self._extra_ids

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None					, SCREAMING_SNAKE_CASE__			:			bool = False   ):






      """simple docstring"""



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

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]
      return ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return list(
          set(filter(lambda SCREAMING_SNAKE_CASE__   : bool(re.search(r'<extra_id_\d+>'					, SCREAMING_SNAKE_CASE__   )   ) is not None					, self.additional_special_tokens   )   )   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      return [self._convert_token_to_id(SCREAMING_SNAKE_CASE__   ) for token in self.get_sentinel_tokens()]

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			List[int]   ):






      """simple docstring"""



      if len(SCREAMING_SNAKE_CASE__   ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'   )
           return token_ids
      else:
           return token_ids + [self.eos_token_id]

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None   ):






      """simple docstring"""



      UpperCamelCase						    =					[self.eos_token_id]

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

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






      """simple docstring"""



      UpperCamelCase						    =					self._add_eos_if_not_present(SCREAMING_SNAKE_CASE__   )
      if token_ids_a is None:
           return token_ids_a
      else:
           UpperCamelCase						    =					self._add_eos_if_not_present(SCREAMING_SNAKE_CASE__   )
           return token_ids_a + token_ids_a

 def __getstate__(						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.__dict__.copy()
      UpperCamelCase						    =					None
      return state

 def __setstate__(						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					d

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

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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			"TextInput"					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      if not self.legacy:
           UpperCamelCase						    =					SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE__					, ' '   )
      return super().tokenize(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, **SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      if not self.legacy:
           UpperCamelCase						    =					text.startswith(SCREAMING_SNAKE_CASE__   )
           if is_first:
                UpperCamelCase						    =					text[1:]

      UpperCamelCase						    =					self.sp_model.encode(SCREAMING_SNAKE_CASE__					, out_type=SCREAMING_SNAKE_CASE__   )

      if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(SCREAMING_SNAKE_CASE__   ):
           UpperCamelCase						    =					([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
      return tokens

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      if token.startswith('<extra_id_'   ):
           UpperCamelCase						    =					re.match(r'<extra_id_(\d+)>'					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					int(match.group(1   )   )
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      if index < self.sp_model.get_piece_size():
           UpperCamelCase						    =					self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__   )
      else:
           UpperCamelCase						    =					F'<extra_id_{self.vocab_size - 1 - index}>'
      return token

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					[]
      UpperCamelCase						    =					''
      UpperCamelCase						    =					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(SCREAMING_SNAKE_CASE__   ) + token
                UpperCamelCase						    =					True
                UpperCamelCase						    =					[]
           else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__   )
      return out_string.strip()






 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[str] = None   ):






      """simple docstring"""



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

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

      return (out_vocab_file,)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


