




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def    A						(__A   :    Optional[int]       )   ->      str:







     """simple docstring"""


     if "img_encoder.pos_embed" in name:
          UpperCAmelCase_          =							name.replace('''img_encoder.pos_embed'''   ,				'''vision_model.embeddings.position_embeddings'''       )
     if "img_encoder.patch_embed.proj" in name:
          UpperCAmelCase_          =							name.replace('''img_encoder.patch_embed.proj'''   ,				'''vision_model.embeddings.patch_embeddings.projection'''       )
     if "img_encoder.patch_embed.norm" in name:
          UpperCAmelCase_          =							name.replace('''img_encoder.patch_embed.norm'''   ,				'''vision_model.embeddings.layernorm'''       )
     if "img_encoder.layers" in name:
          UpperCAmelCase_          =							name.replace('''img_encoder.layers'''   ,				'''vision_model.encoder.stages'''       )
     if "blocks" in name and "res" not in name:
          UpperCAmelCase_          =							name.replace('''blocks'''   ,				'''layers'''       )
     if "attn" in name and "pre_assign" not in name:
          UpperCAmelCase_          =							name.replace('''attn'''   ,				'''self_attn'''       )
     if "proj" in name and "self_attn" in name and "text" not in name:
          UpperCAmelCase_          =							name.replace('''proj'''   ,				'''out_proj'''       )
     if "pre_assign_attn.attn.proj" in name:
          UpperCAmelCase_          =							name.replace('''pre_assign_attn.attn.proj'''   ,				'''pre_assign_attn.attn.out_proj'''       )
     if "norm1" in name:
          UpperCAmelCase_          =							name.replace('''norm1'''   ,				'''layer_norm1'''       )
     if "norm2" in name and "pre_assign" not in name:
          UpperCAmelCase_          =							name.replace('''norm2'''   ,				'''layer_norm2'''       )
     if "img_encoder.norm" in name:
          UpperCAmelCase_          =							name.replace('''img_encoder.norm'''   ,				'''vision_model.layernorm'''       )
     # text encoder
     if "text_encoder.token_embedding" in name:
          UpperCAmelCase_          =							name.replace('''text_encoder.token_embedding'''   ,				'''text_model.embeddings.token_embedding'''       )
     if "text_encoder.positional_embedding" in name:
          UpperCAmelCase_          =							name.replace('''text_encoder.positional_embedding'''   ,				'''text_model.embeddings.position_embedding.weight'''       )
     if "text_encoder.transformer.resblocks." in name:
          UpperCAmelCase_          =							name.replace('''text_encoder.transformer.resblocks.'''   ,				'''text_model.encoder.layers.'''       )
     if "ln_1" in name:
          UpperCAmelCase_          =							name.replace('''ln_1'''   ,				'''layer_norm1'''       )
     if "ln_2" in name:
          UpperCAmelCase_          =							name.replace('''ln_2'''   ,				'''layer_norm2'''       )
     if "c_fc" in name:
          UpperCAmelCase_          =							name.replace('''c_fc'''   ,				'''fc1'''       )
     if "c_proj" in name:
          UpperCAmelCase_          =							name.replace('''c_proj'''   ,				'''fc2'''       )
     if "text_encoder" in name:
          UpperCAmelCase_          =							name.replace('''text_encoder'''   ,				'''text_model'''       )
     if "ln_final" in name:
          UpperCAmelCase_          =							name.replace('''ln_final'''   ,				'''final_layer_norm'''       )
     # projection layers
     if "img_projector.linear_hidden." in name:
          UpperCAmelCase_          =							name.replace('''img_projector.linear_hidden.'''   ,				'''visual_projection.'''       )
     if "img_projector.linear_out." in name:
          UpperCAmelCase_          =							name.replace('''img_projector.linear_out.'''   ,				'''visual_projection.3.'''       )
     if "text_projector.linear_hidden" in name:
          UpperCAmelCase_          =							name.replace('''text_projector.linear_hidden'''   ,				'''text_projection'''       )
     if "text_projector.linear_out" in name:
          UpperCAmelCase_          =							name.replace('''text_projector.linear_out'''   ,				'''text_projection.3'''       )

     return name


def    A						(__A   :    Union[str, Any]   ,				__A   :    Union[str, Any]       )   ->      Tuple:







     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_          =							orig_state_dict.pop(__lowerCamelCase       )

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               UpperCAmelCase_          =							key.split('''.'''       )
               UpperCAmelCase_     ,       UpperCAmelCase_          =							int(key_split[2]       ), int(key_split[4]       )
               UpperCAmelCase_          =							config.vision_config.hidden_size
               if "weight" in key:
                    UpperCAmelCase_          =							val[:dim, :]
                    UpperCAmelCase_          =							val[dim : dim * 2, :]
                    UpperCAmelCase_          =							val[-dim:, :]
               else:
                    UpperCAmelCase_          =							val[:dim]
                    UpperCAmelCase_          =							val[dim : dim * 2]
                    UpperCAmelCase_          =							val[-dim:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               UpperCAmelCase_          =							key.split('''.'''       )
               UpperCAmelCase_          =							int(key_split[3]       )
               UpperCAmelCase_          =							config.text_config.hidden_size
               if "weight" in key:
                    UpperCAmelCase_          =							val[:dim, :]
                    UpperCAmelCase_          =							val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_          =							val[-dim:, :]
               else:
                    UpperCAmelCase_          =							val[:dim]
                    UpperCAmelCase_          =							val[dim : dim * 2]
                    UpperCAmelCase_          =							val[-dim:]
          else:
               UpperCAmelCase_          =							rename_key(__lowerCamelCase       )
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    UpperCAmelCase_          =							val.squeeze_()
               else:
                    UpperCAmelCase_          =							val

     return orig_state_dict


def    A						()   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_          =							Image.open(requests.get(__lowerCamelCase   ,				stream=__lowerCamelCase       ).raw       )
     return im


@torch.no_grad()
def    A						(__A   :    Tuple   ,				__A   :    List[Any]   ,				__A   :    Optional[Any]="groupvit-gcc-yfcc"   ,				__A   :    str=False       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							GroupViTConfig()
     UpperCAmelCase_          =							GroupViTModel(__lowerCamelCase       ).eval()

     UpperCAmelCase_          =							torch.load(__lowerCamelCase   ,				map_location='''cpu'''       )['''model''']
     UpperCAmelCase_          =							convert_state_dict(__lowerCamelCase   ,				__lowerCamelCase       )
     UpperCAmelCase_     ,       UpperCAmelCase_          =							model.load_state_dict(__lowerCamelCase   ,				strict=__lowerCamelCase       )
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowerCamelCase       ) == 0)

     # verify result
     UpperCAmelCase_          =							CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
     UpperCAmelCase_          =							prepare_img()
     UpperCAmelCase_          =							processor(text=['''a photo of a cat''', '''a photo of a dog''']   ,				images=__lowerCamelCase   ,				padding=__lowerCamelCase   ,				return_tensors='''pt'''       )

     with torch.no_grad():
          UpperCAmelCase_          =							model(**__lowerCamelCase       )

     if model_name == "groupvit-gcc-yfcc":
          UpperCAmelCase_          =							torch.tensor([[13.3_523, 6.3_629]]       )
     elif model_name == "groupvit-gcc-redcaps":
          UpperCAmelCase_          =							torch.tensor([[16.1_873, 8.6_230]]       )
     else:
          raise ValueError(F"""Model name {model_name} not supported."""       )
     assert torch.allclose(outputs.logits_per_image   ,				__lowerCamelCase   ,				atol=1E-3       )

     processor.save_pretrained(__lowerCamelCase       )
     model.save_pretrained(__lowerCamelCase       )
     print('''Successfully saved processor and model to'''   ,				__lowerCamelCase       )

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          processor.push_to_hub(__lowerCamelCase   ,				organization='''nielsr'''       )
          model.push_to_hub(__lowerCamelCase   ,				organization='''nielsr'''       )


if __name__ == "__main__":
    snake_case_ : Optional[Any]    		=       argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
    )
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
    parser.add_argument(
        "--model_name",
        default="groupvit-gccy-fcc",
        type=str,
        help="Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'",
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
    )
    snake_case_ : Optional[int]    		=       parser.parse_args()

    convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    raise TypeError('''number of qubits must be a integer.'''		)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''		)
  if math.floor(__lowerCamelCase		) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''		)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

  for i in range(__lowerCamelCase		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(__lowerCamelCase		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j) ,					__lowerCamelCase ,					__lowerCamelCase		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(__lowerCamelCase ,					number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(__lowerCamelCase ,					__lowerCamelCase		)
  # simulate with 10000 shots
  _snake_case					     =       Aer.get_backend('''qasm_simulator'''		)
  _snake_case					     =       execute(__lowerCamelCase ,					__lowerCamelCase ,					shots=1_00_00		)

  return job.result().get_counts(__lowerCamelCase		)


if __name__ == "__main__":
 print(
     F"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
 )



