"""simple docstring"""



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


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


SCREAMING_SNAKE_CASE__:Dict									=			"""▁"""

SCREAMING_SNAKE_CASE__:List[Any]									=			{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__:Tuple									=			{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__:int									=			{
    """google/reformer-crime-and-punishment""": 524288,
}






class  snake_case__      (       UpperCamelCase_       ):
 _snake_case       :					int							  =						VOCAB_FILES_NAMES
 _snake_case       :					List[str]							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					Dict							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case       :					int							  =						["""input_ids""", """attention_mask"""]


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=[]    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=__SCREAMING_SNAKE_CASE    ,		unk_token=__SCREAMING_SNAKE_CASE    ,		additional_special_tokens=__SCREAMING_SNAKE_CASE    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**__SCREAMING_SNAKE_CASE    ,		)

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


 @property
 def 						a__   ( self						):
        return self.sp_model.get_piece_size()


 def 						a__   ( self						):
        __a       =    {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 __getstate__( self						):
        __a       =    self.__dict__.copy()
        __a       =    None
        return state


 def __setstate__( self    ,		lowerCamelCase						):
        __a       =    d

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

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


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,		out_type=__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self    ,		lowerCamelCase						):







        if index < self.sp_model.get_piece_size():
               __a       =    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE						)
        return token


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    []
        __a       =    ''''''
        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(__SCREAMING_SNAKE_CASE						) + token
                      __a       =    []
               else:
                      current_sub_tokens.append(__SCREAMING_SNAKE_CASE						)
        out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE						)
        return out_string.strip()






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):







        if not os.path.isdir(__SCREAMING_SNAKE_CASE						):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
               return
        __a       =    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:
                      __a       =    self.sp_model.serialized_model_proto()
                      fi.write(__SCREAMING_SNAKE_CASE						)

        return (out_vocab_file,)


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

lowerCamelCase  =logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_			)
class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           super().__init__(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{}
           UpperCamelCase__     : int											=					{}

           if prompt is not None:
                 UpperCamelCase__     : int											=					prompt

           if generate_kwargs is not None:
                 UpperCamelCase__     : int											=					generate_kwargs
           if max_new_tokens is not None:
                 if "generate_kwargs" not in forward_kwargs:
                       UpperCamelCase__     : Union[str, Any]											=					{}
                 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'''	)
                 UpperCamelCase__     : Dict											=					max_new_tokens
           return preprocess_params, forward_kwargs, {}






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return super().__call__(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					load_image(__SCREAMING_SNAKE_CASE	)

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

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

                 if model_type == "git":
                       UpperCamelCase__     : Optional[int]											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)
                       UpperCamelCase__     : str											=					self.tokenizer(text=__SCREAMING_SNAKE_CASE    ,    add_special_tokens=__SCREAMING_SNAKE_CASE	).input_ids
                       UpperCamelCase__     : Dict											=					[self.tokenizer.cls_token_id] + input_ids
                       UpperCamelCase__     : int											=					torch.tensor(__SCREAMING_SNAKE_CASE	).unsqueeze(0	)
                       model_inputs.update({'''input_ids''': input_ids}	)

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

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

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

           else:
                 UpperCamelCase__     : str											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)

           if self.model.config.model_type == "git" and prompt is None:
                 UpperCamelCase__     : Optional[Any]											=					None

           return model_inputs






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> Optional[Any]:




           """simple docstring"""







           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']    ,    __SCREAMING_SNAKE_CASE	)
               and all(x is None for x in model_inputs['''input_ids''']	)
           ):
                 UpperCamelCase__     : Union[str, Any]											=					None

           if generate_kwargs is None:
                 UpperCamelCase__     : Optional[Any]											=					{}
           # 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.
           UpperCamelCase__     : Any											=					model_inputs.pop(self.model.main_input_name	)
           UpperCamelCase__     : Any											=					self.model.generate(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return model_outputs






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					[]
           for output_ids in model_outputs:
                 UpperCamelCase__     : str											=					{
                     '''generated_text''': self.tokenizer.decode(
                         __SCREAMING_SNAKE_CASE    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE    ,    )
                 }
                 records.append(__SCREAMING_SNAKE_CASE	)
           return records


