
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__:    Optional[Any]      = logging.get_logger(__name__)

@add_end_docstrings(lowercase_    )
class 							snake_case_ (				lowercase_    ):





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

					if prompt is not None:
							SCREAMING_SNAKE_CASE_					:	int		  =			prompt

					if generate_kwargs is not None:
							SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			generate_kwargs
					if max_new_tokens is not None:
							if "generate_kwargs" not in forward_kwargs:
									SCREAMING_SNAKE_CASE_					:	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'      )
							SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			max_new_tokens
					return preprocess_params, forward_kwargs, {}





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





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase=None      ):
					SCREAMING_SNAKE_CASE_					:	Dict		  =			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.'      )

							SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.model.config.model_type

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

							elif model_type == "pix2struct":
									SCREAMING_SNAKE_CASE_					:	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
									SCREAMING_SNAKE_CASE_					:	Dict		  =			self.image_processor(images=__lowerCAmelCase      ,						return_tensors=self.framework      )
									SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			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:
							SCREAMING_SNAKE_CASE_					:	int		  =			self.image_processor(images=__lowerCAmelCase      ,						return_tensors=self.framework      )

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

					return model_inputs





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase=None      ):







					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']      )
					):
							SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			None

					if generate_kwargs is None:
							SCREAMING_SNAKE_CASE_					:	Tuple		  =			{}
					# 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.
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			model_inputs.pop(self.model.main_input_name      )
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			self.model.generate(__lowerCAmelCase      ,						**__lowerCAmelCase      ,						**__lowerCAmelCase      )
					return model_outputs






			def 						__A						(		self      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			[]
					for output_ids in model_outputs:
							SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{
							    'generated_text': self.tokenizer.decode(
							        __lowerCAmelCase      ,						skip_special_tokens=__lowerCAmelCase      ,						)
							}
							records.append(__lowerCAmelCase      )
					return records










'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A = 1_000			)			->			int:






       """simple docstring"""







       return sum(2 * a * ((a - 1) // 2) for a in range(3    ,					n + 1			)			)


if __name__ == "__main__":
 print(solution())




