
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  :					List[Any]						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None		,snake_case=None		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      {}
           lowercase							:     Optional[Any]       	=      {}

           if prompt is not None:
                  lowercase							:     Any       	=      prompt

           if generate_kwargs is not None:
                  lowercase							:     List[Any]       	=      generate_kwargs
           if max_new_tokens is not None:
                  if "generate_kwargs" not in forward_kwargs:
                         lowercase							:     Tuple       	=      {}
                  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							:     Optional[int]       	=      max_new_tokens
           return preprocess_params, forward_kwargs, {}
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      load_image(snake_case      )

           if prompt is not None:
                  if not isinstance(snake_case		,snake_case      ):
                         raise ValueError(
                             f"Received an invalid text input, got - {type(snake_case      )} - but expected a single string. "
                             """Note also that one single text can be provided for conditional image to text generation."""      )

                  lowercase							:     Any       	=      self.model.config.model_type

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

                  elif model_type == "pix2struct":
                         lowercase							:     Union[str, Any]       	=      self.image_processor(images=snake_case		,header_text=snake_case		,return_tensors=self.framework      )

                  elif model_type != "vision-encoder-decoder":
                         # vision-encoder-decoder does not support conditional generation
                         lowercase							:     Tuple       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
                         lowercase							:     int       	=      self.tokenizer(snake_case		,return_tensors=self.framework      )
                         model_inputs.update(snake_case      )

                  else:
                         raise ValueError(f"Model type {model_type} does not support conditional text generation"      )

           else:
                  lowercase							:     Union[str, Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )

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

           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs["""input_ids"""]		,snake_case      )
               and all(x is None for x in model_inputs["""input_ids"""]      )
           ):
                  lowercase							:     Optional[Any]       	=      None

           if generate_kwargs is None:
                  lowercase							:     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.
           lowercase							:     Union[str, Any]       	=      model_inputs.pop(self.model.main_input_name      )
           lowercase							:     Tuple       	=      self.model.generate(snake_case		,**snake_case		,**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      []
           for output_ids in model_outputs:
                  lowercase							:     Optional[int]       	=      {
                      """generated_text""": self.tokenizer.decode(
                          snake_case		,skip_special_tokens=snake_case		,)
                  }
                  records.append(snake_case      )
           return records






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if num <= 0:
		raise ValueError("Input must be a positive integer"							)

	snake_case_		    =     [True] * (num + 1)

	snake_case_		    =     2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p     ,	num + 1     ,	_A							):
				snake_case_		    =     False
		p += 1

	return [prime for prime in range(2     ,	num + 1							) if primes[prime]]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase__				:     Tuple 		=						int(input("Enter a positive integer: ").strip())
		print(prime_sieve_eratosthenes(user_num))
