

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_    :						Any    		=      "▁"

a_    :						List[str]    		=      {"vocab_file": "spiece.model"}

a_    :						List[Any]    		=      {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

a_    :						Any    		=      {
    "google/pegasus-xsum": 512,
}


a_    :						Optional[int]    		=      logging.get_logger(__name__)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
				__UpperCamelCase         =VOCAB_FILES_NAMES

				__UpperCamelCase         =VOCAB_FILES_NAMES
				__UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =["input_ids", "attention_mask"]

				def __init__(			self :      Optional[int]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[str]="<pad>"							,    snake_case__ :      Union[str, Any]="</s>"							,    snake_case__ :      int="<unk>"							,    snake_case__ :      str="<mask_2>"							,    snake_case__ :      Tuple="<mask_1>"							,    snake_case__ :      Tuple=None							,    snake_case__ :      Dict=1_0_3							,    snake_case__ :      Optional[Dict[str, Any]] = None							,    **snake_case__ :      Dict							,    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			offset
					if additional_special_tokens is not None:
						if not isinstance(snake_case__							,    snake_case__    ):
							raise TypeError(
							    F"""additional_special_tokens should be of type {type(snake_case__    )}, but is"""
							    F""" {type(snake_case__    )}"""    )

						SCREAMING_SNAKE_CASE								=			(
						    ([mask_token_sent] + additional_special_tokens)
						    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
						    else additional_special_tokens
						)
						# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
						additional_special_tokens_extended += [
						    F"""<unk_{i}>""" for i in range(len(snake_case__    )							,    self.offset - 1    )
						]

						if len(set(snake_case__    )    ) != len(snake_case__    ):
							raise ValueError(
							    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
							    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
						SCREAMING_SNAKE_CASE								=			additional_special_tokens_extended
					else:
						SCREAMING_SNAKE_CASE								=			[mask_token_sent] if mask_token_sent is not None else []
						additional_special_tokens += [F"""<unk_{i}>""" for i in range(2							,    self.offset    )]

					SCREAMING_SNAKE_CASE								=			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=snake_case__							,    unk_token=snake_case__							,    mask_token=snake_case__							,    pad_token=snake_case__							,    mask_token_sent=snake_case__							,    offset=snake_case__							,    additional_special_tokens=snake_case__							,    sp_model_kwargs=self.sp_model_kwargs							,    **snake_case__							,    )
					SCREAMING_SNAKE_CASE								=			mask_token_sent
					SCREAMING_SNAKE_CASE								=			vocab_file
					SCREAMING_SNAKE_CASE								=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(snake_case__    )

					# add special tokens to encoder dict
					SCREAMING_SNAKE_CASE								=			{
					    0: self.pad_token,
					    1: self.eos_token,
					}

					if self.mask_token_sent is not None:
						self.encoder.update(
						    {
						        2: self.mask_token_sent,
						        3: self.mask_token,
						    }    )

					if self.offset > 0:
						# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
						# mask_token_sent is already added to list -> so start at 1
						self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1							,    self.offset - 1    )}    )

					SCREAMING_SNAKE_CASE								=			{v: k for k, v in self.encoder.items()}

				@property
				def UpperCamelCase			(			self :      List[str]    ):

					"""simple docstring"""






					return len(self.sp_model    ) + self.offset

				def UpperCamelCase			(			self :      List[str]    ):

					"""simple docstring"""






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

				def __getstate__(			self :      Union[str, Any]    ):

					"""simple docstring"""






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

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

					"""simple docstring"""






					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 UpperCamelCase			(			self :      Any							,    snake_case__ :      str    ):

					"""simple docstring"""






					return self.sp_model.encode(snake_case__							,    out_type=snake_case__    )

				def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str    ):

					"""simple docstring"""






					if token in self.decoder:
						return self.decoder[token]
					elif token in self.added_tokens_decoder:
						return self.added_tokens_decoder[token]
					SCREAMING_SNAKE_CASE								=			self.sp_model.piece_to_id(snake_case__    )
					return sp_id + self.offset

				def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      int    ):

					"""simple docstring"""






					if index in self.encoder:
						return self.encoder[index]
					elif index in self.added_tokens_encoder:
						return self.added_tokens_encoder[index]
					else:
						SCREAMING_SNAKE_CASE								=			self.sp_model.IdToPiece(index - self.offset    )
					return token

				def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			[]
					SCREAMING_SNAKE_CASE								=			''
					for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							out_string += self.sp_model.decode(snake_case__    ) + token
							SCREAMING_SNAKE_CASE								=			[]
						else:
							current_sub_tokens.append(snake_case__    )
					out_string += self.sp_model.decode(snake_case__    )
					return out_string.strip()

				def UpperCamelCase			(			self :      Any							,    snake_case__ :      Any=False    ):

					"""simple docstring"""






					return 1

				def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      List[str]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			set(self.all_special_ids    )  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

					return [1 if x in all_special_ids else 0 for x in seq]

				def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List							,    snake_case__ :      Optional[List] = None							,    snake_case__ :      bool = False    ):

					"""simple docstring"""






					if already_has_special_tokens:
						return self._special_token_mask(snake_case__    )
					elif token_ids_a is None:
						return self._special_token_mask(snake_case__    ) + [1]
					else:
						return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]

				def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]=None    ):

					"""simple docstring"""






					if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]




				def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

					"""simple docstring"""






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

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

					return (out_vocab_file,)




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

