





'''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_   :   Tuple       =			logging.get_logger(__name__)






@add_end_docstrings(__lowerCAmelCase     )
class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   Union[str, Any] , *lowerCamelCase			:   Dict , **lowerCamelCase			:   List[str]		):




				'''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			:   List[str]=None , lowerCamelCase			:   Dict=None , lowerCamelCase			:   List[str]=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			:   Union[str, Any] , lowerCamelCase			:   Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				return super().__call__(lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Any=None		):




				'''simple docstring'''
				a__     = 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__     = self.model.config.model_type

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

							elif model_type == "pix2struct":
										a__     = 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__     = self.image_processor(images=lowerCamelCase , return_tensors=self.framework		)
										a__     = 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__     = self.image_processor(images=lowerCamelCase , return_tensors=self.framework		)

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

				return model_inputs







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Optional[int]=None		):




				'''simple docstring'''
				# 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__     = 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(lowerCamelCase , **lowerCamelCase , **lowerCamelCase		)
				return model_outputs







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = []
				for output_ids in model_outputs:
							a__     = {
							    "generated_text": self.tokenizer.decode(
							        lowerCamelCase , skip_special_tokens=lowerCamelCase , )
							}
							records.append(lowerCamelCase		)
				return records










'''simple docstring'''


from __future__ import annotations

from math import pi







def    _lowerCamelCase  (__lowerCamelCase  :     float					,  __lowerCamelCase  :     float					,  __lowerCamelCase  :     float     )   ->    dict[str, float]:

			if (inductance, frequency, reactance).count(0     ) != 1:
						raise ValueError("One and only one argument must be 0"     )
			if inductance < 0:
						raise ValueError("Inductance cannot be negative"     )
			if frequency < 0:
						raise ValueError("Frequency cannot be negative"     )
			if reactance < 0:
						raise ValueError("Inductive reactance cannot be negative"     )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError("Exactly one argument must be 0"     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



