

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

A_ :				List[str]						 =    logging.get_logger(__name__)



@add_end_docstrings(__magic_name__       )
class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, *A__					, **A__    ):
						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 						__A    (				self					, A__=None					, A__=None					, A__=None    ):
						A__							:					Optional[Any]       =	{}
						A__							:					Any       =	{}

						if prompt is not None:
							A__							:					str       =	prompt

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






					def __call__(				self					, A__					, **A__    ):
						return super().__call__(A__					, **A__    )






					def 						__A    (				self					, A__					, A__=None    ):
						A__							:					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."""    )

							A__							:					Any       =	self.model.config.model_type

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

							elif model_type == "pix2struct":
								A__							:					Any       =	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
								A__							:					Dict       =	self.image_processor(images=A__					, return_tensors=self.framework    )
								A__							:					List[Any]       =	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:
							A__							:					Union[str, Any]       =	self.image_processor(images=A__					, return_tensors=self.framework    )

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

						return model_inputs






					def 						__A    (				self					, A__					, A__=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"""]					, A__    )
						    and all(x is None for x in model_inputs["""input_ids"""]    )
						):
							A__							:					Optional[Any]       =	None

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






					def 						__A    (				self					, A__    ):
						A__							:					Optional[Any]       =	[]
						for output_ids in model_outputs:
							A__							:					Dict       =	{
							    """generated_text""": self.tokenizer.decode(
							        A__					, skip_special_tokens=A__					, )
							}
							records.append(A__    )
						return records






from functools import lru_cache

@lru_cache
def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if num < 0:
		raise ValueError("""Number should not be negative."""      )

	return 1 if num in (0, 1) else num * factorial(num - 1      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



