
import qiskit
from qiskit.providers import Backend







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[Any]				      = bin(a__   )[2:], bin(a__   )[2:]  # Remove leading '0b'

    # Ensure that both strings are of the same length
    if len(a__   ) > len(a__   ):
        SCREAMING_SNAKE_CASE				:				List[str]				      = y.zfill(len(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				Tuple				      = x.zfill(len(a__   )   )

    # We need (3 * number of bits in the larger number)+1 qBits
    # The second parameter is the number of classical registers, to measure the result
    SCREAMING_SNAKE_CASE				:				int				      = qiskit.QuantumCircuit((len(a__   ) * 3) + 1 ,						len(a__   ) + 1   )

    # We are essentially "not-ing" the bits that are 1
    # Reversed because it's easier to perform ops on more significant bits
    for i in range(len(a__   )   ):
        if x[::-1][i] == "1":
            circuit.x(a__   )
    for j in range(len(a__   )   ):
        if y[::-1][j] == "1":
            circuit.x(len(a__   ) + j   )

    return circuit, x, y







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):



    """simple docstring"""



    circuit.ccx(a__ ,						a__ ,						a__   )
    circuit.cx(a__ ,						a__   )
    circuit.ccx(a__ ,						a__ ,						a__   )
    circuit.cx(a__ ,						a__   )
    circuit.cx(a__ ,						a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = qiskit.Aer.get_backend('''aer_simulator'''   ) ,						):



    """simple docstring"""



    if vala < 0 or vala < 0:
        raise ValueError('''Both Integers must be positive!'''   )

    # Store the Integers
    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = store_two_classics(a__ ,						a__   )
    for i in range(len(a__   )   ):
        full_adder(a__ ,						a__ ,						len(a__   ) + i ,						len(a__   ) + len(a__   ) + i ,						len(a__   ) + len(a__   ) + i + 1   )
        circuit.barrier()  # Optional, just for aesthetics

    # Measure the resultant qBits
    for i in range(len(a__   ) + 1   ):
        circuit.measure([(len(a__   ) * 2) + i] ,						[i]   )

    SCREAMING_SNAKE_CASE				:				Any				      = qiskit.execute(a__ ,						a__ ,						shots=1   ).result()

    # The result is in binary. Convert it back to int
    return int(list(res.get_counts()   )[0] ,						2   )


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)




