






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      :       List[str]				  =   logging.get_logger(__name__)


@add_end_docstrings(lowercase_  )
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def __init__(				self       :			List[str] ,	*UpperCamelCase_       :			List[str] ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         super().__init__(*UpperCamelCase_ ,	**UpperCamelCase_				)
         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       :			Union[str, Any] ,	UpperCamelCase_       :			str=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			Union[str, Any]=None				):


         """simple docstring"""



         __A         =	{}
         __A         =	{}

         if prompt is not None:
             __A         =	prompt

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




     def __call__(				self       :			Optional[int] ,	UpperCamelCase_       :			Union[str, List[str], "Image.Image", List["Image.Image"]] ,	**UpperCamelCase_       :			Union[str, Any]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			int=None				):


         """simple docstring"""



         __A         =	load_image(UpperCamelCase_				)

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

             __A         =	self.model.config.model_type

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

             elif model_type == "pix2struct":
                 __A         =	self.image_processor(images=UpperCamelCase_ ,	header_text=UpperCamelCase_ ,	return_tensors=self.framework				)

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

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

         else:
             __A         =	self.image_processor(images=UpperCamelCase_ ,	return_tensors=self.framework				)

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

         return model_inputs




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			str=None				):


         """simple docstring"""



         if (
             "input_ids" in model_inputs
             and isinstance(model_inputs["""input_ids"""] ,	UpperCamelCase_				)
             and all(x is None for x in model_inputs["""input_ids"""]				)
         ):
             __A         =	None

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





     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



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












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




