

import math



class 						lowercase_					:



      def __init__(    self   :       int    ,      A__   :       int    ,      A__   :       int    ,      A__   :       int    ,      A__   :       str	)					->  str:
        _snake_case		=							x
        _snake_case		=							y
        _snake_case		=							step_size
        _snake_case		=							function_to_optimize



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  int:
        return self.function(self.x    ,      self.y	)



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  List[str]:
        _snake_case		=							self.step_size
        return [
            SearchProblem(A__    ,      A__    ,      A__    ,      self.function	)
            for x, y in (
                (self.x - step_size, self.y - step_size),
                (self.x - step_size, self.y),
                (self.x - step_size, self.y + step_size),
                (self.x, self.y - step_size),
                (self.x, self.y + step_size),
                (self.x + step_size, self.y - step_size),
                (self.x + step_size, self.y),
                (self.x + step_size, self.y + step_size),
            )
        ]



      def __hash__(    self   :       Union[str, Any]	)					->  Dict:
        return hash(str(self	)	)



      def __eq__(    self   :       Dict    ,      A__   :       List[str]	)					->  List[Any]:
        if isinstance(A__    ,      A__	):
          return hash(str(self	)	) == hash(str(A__	)	)
        return False







      def __str__(    self   :       int	)					->  Any:
        return f"""x: {self.x} y: {self.y}"""



def    snake_case_(_UpperCamelCase				, _UpperCamelCase = True				, _UpperCamelCase = math.inf				, _UpperCamelCase = -math.inf				, _UpperCamelCase = math.inf				, _UpperCamelCase = -math.inf				, _UpperCamelCase = False				, _UpperCamelCase = 10_000				, )							->			SearchProblem:



  """simple docstring"""






  _snake_case		=							search_prob
  _snake_case		=							[]  # list to store the current score at each iteration
  _snake_case		=							0
  _snake_case		=							False
  _snake_case		=							set()
  while not solution_found and iterations < max_iter:
    visited.add(_UpperCamelCase  )
    iterations += 1
    _snake_case		=							current_state.score()
    scores.append(_UpperCamelCase  )
    _snake_case		=							current_state.get_neighbors()
    _snake_case		=							-math.inf
    _snake_case		=							math.inf
    _snake_case		=							None  # to hold the next best neighbor
    for neighbor in neighbors:
      if neighbor in visited:
        continue  # do not want to visit the same state again
      if (
          neighbor.x > max_x
          or neighbor.x < min_x
          or neighbor.y > max_y
          or neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds
      _snake_case		=							neighbor.score() - current_score
      if find_max:  # finding max
        # going to direction with greatest ascent
        if change > max_change and change > 0:
          _snake_case		=							change
          _snake_case		=							neighbor
      else:  # finding min
        # to direction with greatest descent
        if change < min_change and change < 0:
          _snake_case		=							change
          _snake_case		=							neighbor
    if next_state is not None:
      # we found at least one neighbor which improved the current state
      _snake_case		=							next_state
    else:
      # since we have no neighbor that improves the solution we stop the search
      _snake_case		=							True

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(_UpperCamelCase  )				, _UpperCamelCase  )
    plt.xlabel('''Iterations'''  )
    plt.ylabel('''Function values'''  )
    plt.show()

  return current_state


if __name__ == "__main__":
 import doctest

 doctest.testmod()



 def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



   """simple docstring"""






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

 # starting the problem with initial coordinates (3, 4)
 __A    	=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __A    	=			hill_climbing(prob, find_max=False)
 print(
     '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
     f'''{local_min.score()}'''
 )

 # starting the problem with initial coordinates (12, 47)
 __A    	=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __A    	=			hill_climbing(
     prob, find_max=False, max_x=1_00, 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()}'''
 )



 def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



   """simple docstring"""






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

 __A    	=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __A    	=			hill_climbing(prob, find_max=True)

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




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_weight.chunk(3				, dim=0  )
  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_bias.chunk(3				, dim=0  )

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






  copy_linear(hf_mlp.fca				, pt_mlp.c_fc  )
  copy_linear(hf_mlp.fca				, pt_mlp.c_proj  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )
  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )

  # copy MLP
  copy_mlp(hf_layer.mlp				, pt_layer.mlp  )

  # copy attn
  copy_attn_layer(hf_layer.self_attn				, pt_layer.attn  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  for hf_layer, pt_layer in zip(_UpperCamelCase				, _UpperCamelCase  ):
    copy_layer(_UpperCamelCase				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _snake_case		=							pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm				, pt_model.ln_final  )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers				, pt_model.transformer.resblocks  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm				, pt_model.visual.ln_pre  )
  copy_linear(hf_model.vision_model.post_layernorm				, pt_model.visual.ln_post  )

  # copy embeds
  _snake_case		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _snake_case		=							pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers				, pt_model.visual.transformer.resblocks  )



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






  if config_path is not None:
    _snake_case		=							CLIPConfig.from_pretrained(_UpperCamelCase  )
  else:
    _snake_case		=							CLIPConfig(projection_dim=512				, text_config={}				, vision_config={}  )

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

  _snake_case		=							torch.arange(0				, 77  ).unsqueeze(0  )
  _snake_case		=							torch.randn(1				, 3				, 224				, 224  )

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 __A    	=			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

