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


@add_end_docstrings(_a)
class      UpperCAmelCase_	(    _a):


      def __init__(   self		,			*a		,			**a							)       ->					Union[str, Any]:
             super().__init__(*a		,			**a							)
             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 		_UpperCAmelCase       (   self		,			a=None		,			a=None		,			a=None							)       ->					Dict:
             lowercase__			:		int       =	{}
             lowercase__			:		List[str]       =	{}

             if prompt is not None:
                    lowercase__			:		Any       =	prompt

             if generate_kwargs is not None:
                    lowercase__			:		Dict       =	generate_kwargs
             if max_new_tokens is not None:
                    if "generate_kwargs" not in forward_kwargs:
                           lowercase__			:		str       =	{}
                    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'							)
                    lowercase__			:		List[str]       =	max_new_tokens
             return preprocess_params, forward_kwargs, {}


      def __call__(   self		,			a		,			**a							)       ->					List[str]:
             return super().__call__(a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a=None							)       ->					Optional[Any]:
             lowercase__			:		List[Any]       =	load_image(a							)

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

                    lowercase__			:		Optional[Any]       =	self.model.config.model_type

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

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

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

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

             else:
                    lowercase__			:		Any       =	self.image_processor(images=a		,			return_tensors=self.framework							)

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

             return model_inputs


      def 		_UpperCAmelCase       (   self		,			a		,			a=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']		,			a							)
                 and all(x is None for x in model_inputs['input_ids']							)
             ):
                    lowercase__			:		Tuple       =	None

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


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Optional[Any]:
             lowercase__			:		List[str]       =	[]
             for output_ids in model_outputs:
                    lowercase__			:		str       =	{
                        'generated_text': self.tokenizer.decode(
                            a		,			skip_special_tokens=a		,			)
                    }
                    records.append(a							)
             return records




def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






