



'''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

lowercase_	 =						logging.get_logger(__name__)






@add_end_docstrings(A		)
class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     *A				,     **A )					->  List[str]:


      """simple docstring"""



      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 )					->  Optional[Any]:


      """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 )					->  int:


      """simple docstring"""



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



 def       a__						(self				,     A				,     A=None )					->  List[Any]:


      """simple docstring"""



      _a											=						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											=						self.model.config.model_type

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

           elif model_type == "pix2struct":
                _a											=						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											=						self.image_processor(images=A				,     return_tensors=self.framework )
                _a											=						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											=						self.image_processor(images=A				,     return_tensors=self.framework )

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

      return model_inputs



 def       a__						(self				,     A				,     A=None )					->  Dict:


      """simple docstring"""



      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											=						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(A				,     **A				,     **A )
      return model_outputs



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



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








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


if __name__ == "__main__":
  print(F"""{len(find_circular_primes()) = }""")



