




"""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

__snake_case		=       logging.get_logger(__name__)


@add_end_docstrings(a__				)
class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							str:
          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    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None							)       ->							int:
          _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    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[Any]:
          return super().__call__(__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							int:
          _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    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							Optional[int]:
          # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
          # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
          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    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _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"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					bool = True,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					float = 1_00,     _lowerCAmelCase     :					float = 0.0_1,     _lowerCAmelCase     :					float = 1,     ):






     """simple docstring"""

     _a    	=				False
     _a    	=				search_prob
     _a    	=				start_temperate
     _a    	=				[]
     _a    	=				0
     _a    	=				None

     while not search_end:
          _a    	=				current_state.score()
          if best_state is None or current_score > best_state.score():
               _a    	=				current_state
          scores.append(_lowerCAmelCase       )
          iterations += 1
          _a    	=				None
          _a    	=				current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               _a    	=				random.randint(0,     len(_lowerCAmelCase       ) - 1       )  # picking a random neighbor
               _a    	=				neighbors.pop(_lowerCAmelCase       )
               _a    	=				picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    _a    	=				change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    _a    	=				picked_neighbor
               else:
                    _a    	=				(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         _a    	=				picked_neighbor
          _a    	=				current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               _a    	=				True
          else:
               _a    	=				next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(_lowerCAmelCase       ),     _lowerCAmelCase       )
          plt.xlabel('''Iterations'''       )
          plt.ylabel('''Function values'''       )
          plt.show()
     return best_state


if __name__ == "__main__":
   def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict       ):






        """simple docstring"""

        return (x**2) + (y**2)

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(
       prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
   )
   print(
       '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
       f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
   )

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(
       prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
   )
   print(
       '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
       f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
   )
   def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]       ):






        """simple docstring"""

        return (3 * x**2) - (6 * y)

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(prob, find_max=False, visualization=True)
   print(
       '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
       f'{local_min.score()}'
   )

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(prob, find_max=True, visualization=True)

   print(
       '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
       f'{local_min.score()}'
   )