






'''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							:							Tuple  							=		logging.get_logger(__name__)
@add_end_docstrings(_lowerCamelCase      )
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     *__lowerCamelCase     :     Union[str, Any]				,     **__lowerCamelCase     :     Tuple     )							->							Tuple:

     '''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    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Dict=None				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     Any=None     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     {}
     lowerCamelCase__  =     {}

     if prompt is not None:
      lowerCamelCase__  =     prompt

     if generate_kwargs is not None:
      lowerCamelCase__  =     generate_kwargs
     if max_new_tokens is not None:
      if "generate_kwargs" not in forward_kwargs:
       lowerCamelCase__  =     {}
      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__  =     max_new_tokens
     return preprocess_params, forward_kwargs, {}

    def __call__(					self     :     str				,     __lowerCamelCase     :     Union[str, List[str], "Image.Image", List["Image.Image"]]				,     **__lowerCamelCase     :     List[str]     )							->							Any:

     '''simple docstring'''




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

    def    a__				(					self     :     int				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]=None     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     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."     )

      lowerCamelCase__  =     self.model.config.model_type

      if model_type == "git":
       lowerCamelCase__  =     self.image_processor(images=__lowerCamelCase				,     return_tensors=self.framework     )
       lowerCamelCase__  =     self.tokenizer(text=__lowerCamelCase				,     add_special_tokens=__lowerCamelCase     ).input_ids
       lowerCamelCase__  =     [self.tokenizer.cls_token_id] + input_ids
       lowerCamelCase__  =     torch.tensor(__lowerCamelCase     ).unsqueeze(0     )
       model_inputs.update({"input_ids": input_ids}     )

      elif model_type == "pix2struct":
       lowerCamelCase__  =     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
       lowerCamelCase__  =     self.image_processor(images=__lowerCamelCase				,     return_tensors=self.framework     )
       lowerCamelCase__  =     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:
      lowerCamelCase__  =     self.image_processor(images=__lowerCamelCase				,     return_tensors=self.framework     )

     if self.model.config.model_type == "git" and prompt is None:
      lowerCamelCase__  =     None

     return model_inputs

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Optional[int]=None     )							->							Optional[int]:

     '''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"]     )
     ):
      lowerCamelCase__  =     None

     if generate_kwargs is None:
      lowerCamelCase__  =     {}
     # 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__  =     model_inputs.pop(self.model.main_input_name     )
     lowerCamelCase__  =     self.model.generate(__lowerCamelCase				,     **__lowerCamelCase				,     **__lowerCamelCase     )
     return model_outputs




    def    a__				(					self     :     Any				,     __lowerCamelCase     :     str     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     []
     for output_ids in model_outputs:
      lowerCamelCase__  =     {
          "generated_text": self.tokenizer.decode(
              __lowerCamelCase				,     skip_special_tokens=__lowerCamelCase				,     )
      }
      records.append(__lowerCamelCase     )
     return records









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							:							Union[str, Any]  							=		{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LayoutLMv2ForQuestionAnswering""",
     """LayoutLMv2ForSequenceClassification""",
     """LayoutLMv2ForTokenClassification""",
     """LayoutLMv2Layer""",
     """LayoutLMv2Model""",
     """LayoutLMv2PreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

