"""simple docstring"""




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


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

_a      :		Any           =   {'vocab_file': 'spiece.model'}

_a      :		Dict           =   {
    '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
_a      :		List[str]           =   {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

_a      :		str           =   '▁'
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Any							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[int]							=		["input_ids", "attention_mask"]






     def __init__(		self				,     a__				,     a__="</s>"				,     a__="<unk>"				,     a__="<pad>"				,     a__=100				,     a__=None				,     a__ = None				,     a__=True				,     **a__				,     ):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         _lowerCAmelCase						:       List[Any]  			=     [F"<extra_id_{i}>" for i in range(a__  )]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra_id special tokens
         _lowerCAmelCase						:       Union[str, Any]  			=     len(set(filter(lambda a__  : bool("""extra_id""" in str(a__  )  )				,     a__  )  )  )
         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"""  )

       _lowerCAmelCase						:       str  			=     legacy
       _lowerCAmelCase						:       List[str]  			=     {} if sp_model_kwargs is None else sp_model_kwargs

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

       _lowerCAmelCase						:       Any  			=     vocab_file
       _lowerCAmelCase						:       Any  			=     extra_ids

       _lowerCAmelCase						:       Dict  			=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(a__  )






     @staticmethod
     def     __A  (		a__				,     a__				,     a__  ):
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
         _lowerCAmelCase						:       List[Any]  			=     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."""				,     a__				,     )

       return max_model_length






     @property
     def     __A  (		self  ):
       return self.sp_model.get_piece_size() + self._extra_ids






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     {self.convert_ids_to_tokens(a__  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab






     def     __A  (		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__  )

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






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






     def     __A  (		self  ):
       return [self._convert_token_to_id(a__  ) for token in self.get_sentinel_tokens()]






     def     __A  (		self				,     a__  ):
       if len(a__  ) > 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     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Tuple  			=     [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     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Tuple  			=     self._add_eos_if_not_present(a__  )
       if token_ids_a is None:
         return token_ids_a
       else:
         _lowerCAmelCase						:       Optional[Any]  			=     self._add_eos_if_not_present(a__  )
         return token_ids_a + token_ids_a






     def __getstate__(		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.__dict__.copy()
       _lowerCAmelCase						:       List[str]  			=     None
       return state






     def __setstate__(		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     d

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

       _lowerCAmelCase						:       Any  			=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )






     def     __A  (		self				,     a__				,     **a__  ):
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
         _lowerCAmelCase						:       Union[str, Any]  			=     SPIECE_UNDERLINE + text.replace(a__				,     """ """  )
       return super().tokenize(a__				,     **a__  )






     def     __A  (		self				,     a__				,     **a__  ):
       if not self.legacy:
         _lowerCAmelCase						:       int  			=     text.startswith(a__  )
         if is_first:
           _lowerCAmelCase						:       Tuple  			=     text[1:]

       _lowerCAmelCase						:       List[Any]  			=     self.sp_model.encode(a__				,     out_type=a__  )

       if not self.legacy and not is_first and not text.startswith(""" """  ) and tokens[0].startswith(a__  ):
         _lowerCAmelCase						:       Optional[int]  			=     ([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
       return tokens






     def     __A  (		self				,     a__  ):
       if token.startswith("""<extra_id_"""  ):
         _lowerCAmelCase						:       Any  			=     re.match(r"""<extra_id_(\d+)>"""				,     a__  )
         _lowerCAmelCase						:       Dict  			=     int(match.group(1  )  )
         return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(a__  )






     def     __A  (		self				,     a__  ):
       if index < self.sp_model.get_piece_size():
         _lowerCAmelCase						:       Union[str, Any]  			=     self.sp_model.IdToPiece(a__  )
       else:
         _lowerCAmelCase						:       Union[str, Any]  			=     F"<extra_id_{self.vocab_size - 1 - index}>"
       return token






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     []
       _lowerCAmelCase						:       List[str]  			=     """"""
       _lowerCAmelCase						:       Tuple  			=     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(a__  ) + token
           _lowerCAmelCase						:       Optional[int]  			=     True
           _lowerCAmelCase						:       Union[str, Any]  			=     []
         else:
           current_sub_tokens.append(a__  )
           _lowerCAmelCase						:       Tuple  			=     False
       out_string += self.sp_model.decode(a__  )
       return out_string.strip()






     def     __A  (		self				,     a__				,     a__ = None  ):
       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:       Optional[Any]  			=     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:
           _lowerCAmelCase						:       Optional[Any]  			=     self.sp_model.serialized_model_proto()
           fi.write(a__  )

       return (out_vocab_file,)





"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




