






"""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

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: List[Any]	,	*lowerCAmelCase_					: List[str]	,	**lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				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      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[str]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: str=None						)						->			str:
				UpperCAmelCase_     : Any			     =					{}
				UpperCAmelCase_     : List[Any]			     =					{}

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

				if generate_kwargs is not None:
							UpperCAmelCase_     : str			     =					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_     : Union[str, Any]			     =					max_new_tokens
				return preprocess_params, forward_kwargs, {}





	def __call__(      self					: Tuple	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: int						)						->			List[Any]:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: List[str]=None						)						->			Union[str, Any]:
				UpperCAmelCase_     : Union[str, 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_     : List[str]			     =					self.model.config.model_type

							if model_type == "git":
										UpperCAmelCase_     : Optional[int]			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
										UpperCAmelCase_     : Any			     =					self.tokenizer(text=lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						).input_ids
										UpperCAmelCase_     : Optional[int]			     =					[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_     : Union[str, Any]			     =					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_     : Optional[int]			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
										UpperCAmelCase_     : 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_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	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					: Any	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: List[str]=None						)						->			Dict:
				# 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"]	,	lowerCAmelCase_						)
				    and all(x is None for x in model_inputs["input_ids"]						)
				):
							UpperCAmelCase_     : str			     =					None

				if generate_kwargs is None:
							UpperCAmelCase_     : 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_     : Optional[Any]			     =					model_inputs.pop(self.model.main_input_name						)
				UpperCAmelCase_     : List[Any]			     =					self.model.generate(lowerCAmelCase_	,	**lowerCAmelCase_	,	**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[Any]						)						->			int:
				UpperCAmelCase_     : List[Any]			     =					[]
				for output_ids in model_outputs:
							UpperCAmelCase_     : int			     =					{
							    "generated_text": self.tokenizer.decode(
							        lowerCAmelCase_	,	skip_special_tokens=lowerCAmelCase_	,	)
							}
							records.append(lowerCAmelCase_						)
				return records











"""simple docstring"""
def 				snake_case      (   A__	,A__	,A__	,A__	,A__						):
			if index == number_of_items:
						return 0
			UpperCAmelCase_     : str			     =					0
			UpperCAmelCase_     : Dict			     =					0
			UpperCAmelCase_     : Tuple			     =					knapsack(A__	,A__	,A__	,A__	,index + 1						)
			if weights[index] <= max_weight:
						UpperCAmelCase_     : Union[str, Any]			     =					values[index] + knapsack(
						    A__	,A__	,A__	,max_weight - weights[index]	,index + 1						)
			return max(A__	,A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



