


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      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            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)			->				Dict:
            _A      :       Optional[int]				    = {}
            _A      :       Tuple				    = {}

            if prompt is not None:
                   _A      :       Dict				    = prompt

            if generate_kwargs is not None:
                   _A      :       Union[str, Any]				    = generate_kwargs
            if max_new_tokens is not None:
                   if "generate_kwargs" not in forward_kwargs:
                          _A      :       int				    = {}
                   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      :       Dict				    = max_new_tokens
            return preprocess_params, forward_kwargs, {}


     def __call__(						self ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return super().__call__(__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase=None)			->				Optional[Any]:
            _A      :       List[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.")

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

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

                   elif model_type == "pix2struct":
                          _A      :       List[str]				    = 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
                          _A      :       Optional[int]				    = self.image_processor(images=__lowerCamelCase ,						return_tensors=self.framework)
                          _A      :       Dict				    = 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:
                   _A      :       Any				    = self.image_processor(images=__lowerCamelCase ,						return_tensors=self.framework)

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

            return model_inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase=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"])
            ):
                   _A      :       Tuple				    = None

            if generate_kwargs is None:
                   _A      :       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.
            _A      :       List[str]				    = model_inputs.pop(self.model.main_input_name)
            _A      :       int				    = self.model.generate(__lowerCamelCase ,						**__lowerCamelCase ,						**__lowerCamelCase)
            return model_outputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            _A      :       List[str]				    = []
            for output_ids in model_outputs:
                   _A      :       Any				    = {
                       "generated_text": self.tokenizer.decode(
                           __lowerCamelCase ,						skip_special_tokens=__lowerCamelCase ,						)
                   }
                   records.append(__lowerCamelCase)
            return records







from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



