



'''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

snake_case_					:      Dict    			=    logging.get_logger(__name__)






@add_end_docstrings(lowercase  )
class 				lowercase__      (				lowercase  ):





	def __init__(					self   :							Optional[Any]					,*lowerCamelCase__   :							Optional[Any]					,**lowerCamelCase__   :							Any					):







							'''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       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Union[str, Any]=None					,lowerCamelCase__   :							List[str]=None					,lowerCamelCase__   :							Union[str, Any]=None					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  {}
							_UpperCamelCase   :						Any												=  {}

							if prompt is not None:
													_UpperCamelCase   :						List[str]												=  prompt

							if generate_kwargs is not None:
													_UpperCamelCase   :						Any												=  generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			_UpperCamelCase   :						List[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'					)
													_UpperCamelCase   :						Optional[Any]												=  max_new_tokens
							return preprocess_params, forward_kwargs, {}





	def __call__(					self   :							Tuple					,lowerCamelCase__   :							Union[str, List[str], "Image.Image", List["Image.Image"]]					,**lowerCamelCase__   :							Dict					):







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





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							int=None					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  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   :						Tuple												=  self.model.config.model_type

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

													elif model_type == "pix2struct":
																			_UpperCamelCase   :						Tuple												=  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   :						List[Any]												=  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[int]												=  None

							return model_inputs





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Dict=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']					)
							):
													_UpperCamelCase   :						List[str]												=  None

							if generate_kwargs is None:
													_UpperCamelCase   :						Dict												=  {}
							# 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[int]												=  model_inputs.pop(self.model.main_input_name					)
							_UpperCamelCase   :						Tuple												=  self.model.generate(lowerCamelCase__					,**lowerCamelCase__					,**lowerCamelCase__					)
							return model_outputs





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







							'''simple docstring'''
							_UpperCamelCase   :						int												=  []
							for output_ids in model_outputs:
													_UpperCamelCase   :						Any												=  {
													    'generated_text': self.tokenizer.decode(
													        lowerCamelCase__					,skip_special_tokens=lowerCamelCase__					,)
													}
													records.append(lowerCamelCase__					)
							return records







'''simple docstring'''
def A__       (	UpperCAmelCase_ = 1_0_0_0				):
						_UpperCamelCase   :						List[str]												=  3
						_UpperCamelCase   :						Any												=  0
						while a < n:
												if a % 3 == 0 or a % 5 == 0:
																		result += a
												elif a % 1_5 == 0:
																		result -= a
												a += 1
						return result


if __name__ == "__main__":
					print(F"""{solution() = }""")


