



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

lowercase_						=       logging.get_logger(__name__)
@add_end_docstrings(lowercase__)
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Union[str, Any],   *__lowercase   :  Union[str, Any],   **__lowercase   :  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 							A__ (  self   :  str,   __lowercase   :  Dict=None,   __lowercase   :  int=None,   __lowercase   :  Union[str, Any]=None  ):
							lowercase__   					=			{}
							lowercase__   					=			{}

							if prompt is not None:
										lowercase__   					=			prompt

							if generate_kwargs is not None:
										lowercase__   					=			generate_kwargs
							if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
													lowercase__   					=			{}
										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__   					=			max_new_tokens
							return preprocess_params, forward_kwargs, {}




				def __call__(  self   :  Tuple,   __lowercase   :  List[str],   **__lowercase   :  Dict  ):
							return super().__call__(lowerCamelCase_,   **lowerCamelCase_  )




				def 							A__ (  self   :  List[str],   __lowercase   :  List[str],   __lowercase   :  Any=None  ):
							lowercase__   					=			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."  )

										lowercase__   					=			self.model.config.model_type

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

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

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

							return model_inputs




				def 							A__ (  self   :  Tuple,   __lowercase   :  Optional[Any],   __lowercase   :  Optional[int]=None  ):
							# 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"]  )
							):
										lowercase__   					=			None

							if generate_kwargs is None:
										lowercase__   					=			{}
							# 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__   					=			model_inputs.pop(self.model.main_input_name  )
							lowercase__   					=			self.model.generate(lowerCamelCase_,   **lowerCamelCase_,   **lowerCamelCase_  )
							return model_outputs




				def 							A__ (  self   :  Any,   __lowercase   :  Tuple  ):
							lowercase__   					=			[]
							for output_ids in model_outputs:
										lowercase__   					=			{
										    "generated_text": self.tokenizer.decode(
										        lowerCamelCase_,   skip_special_tokens=lowerCamelCase_,   )
										}
										records.append(lowerCamelCase_  )
							return records









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			for i in range(SCREAMING_SNAKE_CASE_					):
						for j in range(i + 1  ,					SCREAMING_SNAKE_CASE_					):
									if numbers[j] < numbers[i]:
												lowercase__					,    lowercase__   					=			numbers[j], numbers[i]
			return numbers


if __name__ == "__main__":
							lowercase_						=       input("""Enter numbers separated by a comma:\n""").strip()
							lowercase_						=       [int(item) for item in user_input.split(""",""")]
							print(exchange_sort(unsorted))




