






"""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

UpperCAmelCase_   : Any   			= logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__ )
class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Tuple						,	*lowercase_     :  str						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


          super().__init__(*lowercase_						,	**lowercase_)
          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     :  Any						,	lowercase_     :  str=None						,	lowercase_     :  Dict=None						,	lowercase_     :  Optional[int]=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{}
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		{}

          if prompt is not None:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		prompt

          if generate_kwargs is not None:
              SCREAMING_SNAKE_CASE_      :  int    						=		generate_kwargs
          if max_new_tokens is not None:
              if "generate_kwargs" not in forward_kwargs:
                  SCREAMING_SNAKE_CASE_      :  Union[str, 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_      :  Optional[Any]    						=		max_new_tokens
          return preprocess_params, forward_kwargs, {}
      def __call__(						self     :  Any						,	lowercase_     :  Union[str, List[str], "Image.Image", List["Image.Image"]]						,	**lowercase_     :  Any):




          '''simple docstring'''


          return super().__call__(lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Optional[int]						,	lowercase_     :  List[str]=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		load_image(lowercase_)

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model.config.model_type

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

              elif model_type == "pix2struct":
                  SCREAMING_SNAKE_CASE_      :  str    						=		self.image_processor(images=lowercase_						,	header_text=lowercase_						,	return_tensors=self.framework)

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

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

          else:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.image_processor(images=lowercase_						,	return_tensors=self.framework)

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

          return model_inputs
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[int]=None):




          '''simple docstring'''


          if (
              "input_ids" in model_inputs
              and isinstance(model_inputs['''input_ids''']						,	lowercase_)
              and all(x is None for x in model_inputs['''input_ids'''])
          ):
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		None

          if generate_kwargs is None:
              SCREAMING_SNAKE_CASE_      :  Optional[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.
          SCREAMING_SNAKE_CASE_      :  int    						=		model_inputs.pop(self.model.main_input_name)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model.generate(lowercase_						,	**lowercase_						,	**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
          for output_ids in model_outputs:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{
                  '''generated_text''': self.tokenizer.decode(
                      lowercase_						,	skip_special_tokens=lowercase_						,	)
              }
              records.append(lowercase_)
          return records










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


