
'''simple docstring'''





from typing import Any

import numpy as np


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[int]						,					_A	:np.ndarray						,					_A	:int      )					->      None:







       '''simple docstring'''



       if np.any(tableau[:, -1]						,					where=tableau[:, -1] < 0      ):
             raise ValueError('RHS must be > 0'      )

       __A													=      tableau
       __A						,   __A													=      tableau.shape

       # Number of decision variables x1, x2, x3...
       __A													=      n_vars

       # Number of artificial variables to be minimised
       __A													=      len(np.where(tableau[self.n_vars : -1] == -1      )[0]      )

       # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
       __A													=      (self.n_art_vars > 0) + 1

       # Number of slack variables added to make inequalities into equalities
       __A													=      self.n_rows - self.n_stages

       # Objectives for each stage
       __A													=      ['max']

       # In two stage simplex, first minimise then maximise
       if self.n_art_vars:
             self.objectives.append('min'      )

       __A													=      ['']

       # Index of current pivot row and column
       __A													=      None
       __A													=      None

       # Does objective row only contain (non)-negative values?
       __A													=      False






 @staticmethod
 def 						lowercase_    (      *_A	:int      )					->      list[str]:







       '''simple docstring'''



       if len(_A      ) != 3:
             raise ValueError('Must provide n_vars, n_slack, and n_art_vars'      )

       if not all(x >= 0 and isinstance(_A						,					_A      ) for x in args      ):
             raise ValueError('All arguments must be non-negative integers'      )

       # decision | slack | artificial
       __A													=      ['x', 's', 'a']
       __A													=      []
       for i in range(3      ):
             for j in range(args[i]      ):
                   titles.append(string_starts[i] + str(j + 1      )      )
       titles.append('RHS'      )
       return titles






 def 						lowercase_    (      self	:Optional[int]						,					_A	:np.ndarray      )					->      tuple[Any, Any]:







       '''simple docstring'''



       __A													=      self.objectives[-1]

       # Find entries of highest magnitude in objective rows
       __A													=      (objective == 'min') - (objective == 'max')
       __A													=      np.argmax(sign * tableau[0, : self.n_vars]      )

       # Choice is only valid if below 0 for maximise, and above for minimise
       if sign * self.tableau[0, col_idx] <= 0:
             __A													=      True
             return 0, 0

       # Pivot row is chosen as having the lowest quotient when elements of
       # the pivot column divide the right-hand side

       # Slice excluding the objective rows
       __A													=      slice(self.n_stages						,					self.n_rows      )

       # RHS
       __A													=      tableau[s, -1]

       # Elements of pivot column within slice
       __A													=      tableau[s, col_idx]

       # Array filled with nans
       __A													=      np.full(self.n_rows - self.n_stages						,					np.nan      )

       # If element in pivot column is greater than zeron_stages, return
       # quotient or nan otherwise
       __A													=      np.divide(_A						,					_A						,					out=_A						,					where=divisor > 0      )

       # Arg of minimum quotient excluding the nan values. n_stages is added
       # to compensate for earlier exclusion of objective columns
       __A													=      np.nanargmin(_A      ) + self.n_stages
       return row_idx, col_idx






 def 						lowercase_    (      self	:str						,					_A	:np.ndarray						,					_A	:int						,					_A	:int      )					->      np.ndarray:







       '''simple docstring'''



       __A													=      tableau[row_idx].copy()

       __A													=      piv_row[col_idx]

       # Entry becomes 1
       piv_row *= 1 / piv_val

       # Variable in pivot column becomes basic, ie the only non-zero entry
       for idx, coeff in enumerate(tableau[:, col_idx]      ):
             tableau[idx] += -coeff * piv_row
       __A													=      piv_row
       return tableau






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:np.ndarray      )					->      np.ndarray:







       '''simple docstring'''



       self.objectives.pop()

       if not self.objectives:
             return tableau

       # Slice containing ids for artificial columns
       __A													=      slice(-self.n_art_vars - 1						,					-1      )

       # Delete the artificial variable columns
       __A													=      np.delete(_A						,					_A						,					axis=1      )

       # Delete the objective row of the first stage
       __A													=      np.delete(_A						,					0						,					axis=0      )

       __A													=      1
       self.n_rows -= 1
       __A													=      0
       __A													=      False
       return tableau






 def 						lowercase_    (      self	:List[Any]      )					->      dict[Any, Any]:







       '''simple docstring'''



       for _ in range(100      ):
             # Completion of each stage removes an objective. If both stages
             # are complete, then no objectives are left
             if not self.objectives:
                   __A													=      self.generate_col_titles(
                       self.n_vars						,					self.n_slack						,					self.n_art_vars      )

                   # Find the values of each variable at optimal solution
                   return self.interpret_tableau(self.tableau						,					self.col_titles      )

             __A						,   __A													=      self.find_pivot(self.tableau      )

             # If there are no more negative values in objective row
             if self.stop_iter:
                   # Delete artificial variable columns and rows. Update attributes
                   __A													=      self.change_stage(self.tableau      )
             else:
                   __A													=      self.pivot(self.tableau						,					_A						,					_A      )
       return {}






 def 						lowercase_    (      self	:List[str]						,					_A	:np.ndarray						,					_A	:list[str]      )					->      dict[str, float]:







       '''simple docstring'''



       __A													=      {'P': abs(tableau[0, -1]      )}

       for i in range(self.n_vars      ):
             # Gives ids of nonzero entries in the ith column
             __A													=      np.nonzero(tableau[:, i]      )
             __A													=      len(nonzero[0]      )

             # First entry in the nonzero ids
             __A													=      nonzero[0][0]
             __A													=      tableau[nonzero_rowidx, i]

             # If there is only one nonzero value in column, which is one
             if n_nonzero == nonzero_val == 1:
                   __A													=      tableau[nonzero_rowidx, -1]
                   __A													=      rhs_val

        # Check for basic variables
       for title in col_titles:
             # Don't add RHS or slack variables to output dict
             if title[0] not in "R-s-a":
                   output_dict.setdefault(_A						,					0      )
       return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()








import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

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

						__UpperCamelCase  	=      pt_attn_layer.out_proj.weight
						__UpperCamelCase  	=      pt_attn_layer.out_proj.bias

						__UpperCamelCase  	=      q_proj
						__UpperCamelCase  	=      q_proj_bias

						__UpperCamelCase  	=      k_proj
						__UpperCamelCase  	=      k_proj_bias

						__UpperCamelCase  	=      v_proj
						__UpperCamelCase  	=      v_proj_bias

						__UpperCamelCase  	=      out_proj_weights
						__UpperCamelCase  	=      out_proj_bias




def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: int			)   ->     Any:



						'''simple docstring'''

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




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''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 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

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




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.token_embedding.weight
						__UpperCamelCase  	=      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 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

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




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      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
						__UpperCamelCase  	=      pt_model.visual.conva.weight.data
						__UpperCamelCase  	=      pt_model.visual.class_embedding
						__UpperCamelCase  	=      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 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

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

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

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

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      pt_model(snake_case			,   snake_case			)

						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)
						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)

						hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Union[str, Any]						=      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")
				lowercase__				:				str						=      parser.parse_args()

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




