






"""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

SCREAMING_SNAKE_CASE						:						str					   =       logging.get_logger(__name__)





@add_end_docstrings(__lowerCamelCase       )
class       snake_case_       (    __lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					super().__init__(*UpperCamelCase_  ,					**UpperCamelCase_						)
					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  ,					__a=None  ,					__a=None  ,					__a=None						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}

					if prompt is not None:
									A__							 =       prompt

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


	def __call__(      self  ,					__a  ,					**__a						):


					"""simple docstring"""

					return super().__call__(UpperCamelCase_  ,					**UpperCamelCase_						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=None						):


					"""simple docstring"""

					A__							 =       load_image(UpperCamelCase_						)

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

									A__							 =       self.model.config.model_type

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

									elif model_type == "pix2struct":
													A__							 =       self.image_processor(images=UpperCamelCase_  ,					header_text=UpperCamelCase_  ,					return_tensors=self.framework						)

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

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

					else:
									A__							 =       self.image_processor(images=UpperCamelCase_  ,					return_tensors=self.framework						)

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

					return model_inputs


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=None						):


					"""simple docstring"""

					if (
					    "input_ids" in model_inputs
					    and isinstance(model_inputs['input_ids']  ,					UpperCamelCase_						)
					    and all(x is None for x in model_inputs['input_ids']						)
					):
									A__							 =       None

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






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for output_ids in model_outputs:
									A__							 =       {
									    'generated_text': self.tokenizer.decode(
									        UpperCamelCase_  ,					skip_special_tokens=UpperCamelCase_  ,					)
									}
									records.append(UpperCamelCase_						)
					return records










"""simple docstring"""






from maths.prime_check import is_prime



def  __lowerCamelCase					(					lowerCAmelCase__				):
				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       f'''Input value of [number={number}] must be an integer'''
								raise TypeError(lowerCAmelCase__				)
				if is_prime(lowerCAmelCase__				) and is_prime(number + 2				):
								return number + 2
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()


