'''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

UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)


@add_end_docstrings(__UpperCAmelCase   )
class 				_lowercase	(      __UpperCAmelCase   ):
						'''simple docstring'''


						def __init__(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Optional[Any]:



									'''simple docstring'''



									super().__init__(*_lowerCamelCase					,**_lowerCamelCase							)
									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 		lowerCAmelCase__					(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None							)			->			int:



									'''simple docstring'''



									UpperCAmelCase__							:				Any   				=  {}
									UpperCAmelCase__							:				Any   				=  {}

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

									if generate_kwargs is not None:
												UpperCAmelCase__							:				Tuple   				=  generate_kwargs
									if max_new_tokens is not None:
												if "generate_kwargs" not in forward_kwargs:
															UpperCAmelCase__							:				List[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__							:				str   				=  max_new_tokens
									return preprocess_params, forward_kwargs, {}




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



									'''simple docstring'''



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




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None							)			->			Tuple:



									'''simple docstring'''



									UpperCAmelCase__							:				Any   				=  load_image(_lowerCamelCase							)

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

												UpperCAmelCase__							:				int   				=  self.model.config.model_type

												if model_type == "git":
															UpperCAmelCase__							:				Tuple   				=  self.image_processor(images=_lowerCamelCase					,return_tensors=self.framework							)
															UpperCAmelCase__							:				List[Any]   				=  self.tokenizer(text=_lowerCamelCase					,add_special_tokens=_lowerCamelCase							).input_ids
															UpperCAmelCase__							:				Tuple   				=  [self.tokenizer.cls_token_id] + input_ids
															UpperCAmelCase__							:				Dict   				=  torch.tensor(_lowerCamelCase							).unsqueeze(0							)
															model_inputs.update({'''input_ids''': input_ids}							)

												elif model_type == "pix2struct":
															UpperCAmelCase__							:				Dict   				=  self.image_processor(images=_lowerCamelCase					,header_text=_lowerCamelCase					,return_tensors=self.framework							)

												elif model_type != "vision-encoder-decoder":
															# vision-encoder-decoder does not support conditional generation
															UpperCAmelCase__							:				Tuple   				=  self.image_processor(images=_lowerCamelCase					,return_tensors=self.framework							)
															UpperCAmelCase__							:				List[str]   				=  self.tokenizer(_lowerCamelCase					,return_tensors=self.framework							)
															model_inputs.update(_lowerCamelCase							)

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

									else:
												UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(images=_lowerCamelCase					,return_tensors=self.framework							)

									if self.model.config.model_type == "git" and prompt is None:
												UpperCAmelCase__							:				List[str]   				=  None

									return model_inputs




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



									'''simple docstring'''



									if (
									    "input_ids" in model_inputs
									    and isinstance(model_inputs['''input_ids''']					,_lowerCamelCase							)
									    and all(x is None for x in model_inputs['''input_ids''']							)
									):
												UpperCAmelCase__							:				Optional[int]   				=  None

									if generate_kwargs is None:
												UpperCAmelCase__							:				List[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__							:				Tuple   				=  model_inputs.pop(self.model.main_input_name							)
									UpperCAmelCase__							:				Optional[Any]   				=  self.model.generate(_lowerCamelCase					,**_lowerCamelCase					,**_lowerCamelCase							)
									return model_outputs




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Union[str, Any]:



									'''simple docstring'''



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





'''simple docstring'''

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

import sentencepiece as spm

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


UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			{'vocab_file': 'sentencepiece.model'}

UpperCamelCase__     :	int				=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCamelCase__     :	Any				=			{
    'google/rembert': 256,
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      List[Any]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=False					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[UNK]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[PAD]"					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[MASK]"					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(
             do_lower_case=lowerCamelCase_					,remove_space=lowerCamelCase_					,keep_accents=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Union[str, Any]   				=  do_lower_case
         UpperCAmelCase__							:				str   				=  remove_space
         UpperCAmelCase__							:				Optional[Any]   				=  keep_accents
         UpperCAmelCase__							:				List[Any]   				=  vocab_file

         UpperCAmelCase__							:				Dict   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         return len(self.sp_model							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def __getstate__(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Any   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  d
         UpperCAmelCase__							:				Any   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.sp_model.EncodeAsPieces(lowerCamelCase_							)
         return pieces




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.sp_model.PieceToId(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         return self.sp_model.IdToPiece(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.sp_model.decode_pieces(lowerCamelCase_							)
         return out_string




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   '''You should not supply a second sequence if the provided sequence of '''
                   '''ids is already formatted with special tokens for the model.'''							)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCamelCase_							):
            copyfile(self.vocab_file					,lowerCamelCase_							)

         return (out_vocab_file,)




