


'''simple docstring'''
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						Optional[int]						=							old_name

    if "patch_embed" in old_name:
        UpperCAmelCase__     :						Union[str, Any]						=							old_name.split('''.'''		)

        if layer == "0":
            UpperCAmelCase__     :						List[str]						=							old_name.replace('''0'''    ,   '''convolution1'''		)
        elif layer == "1":
            UpperCAmelCase__     :						Any						=							old_name.replace('''1'''    ,   '''batchnorm_before'''		)
        elif layer == "3":
            UpperCAmelCase__     :						List[Any]						=							old_name.replace('''3'''    ,   '''convolution2'''		)
        else:
            UpperCAmelCase__     :						Optional[int]						=							old_name.replace('''4'''    ,   '''batchnorm_after'''		)

    if "network" in old_name and re.search(R'''\d\.\d'''    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						int						=							R"\b\d{2}\b"
        if bool(re.search(lowerCAmelCase__    ,   lowerCAmelCase__		)		):
            UpperCAmelCase__     :						Tuple						=							re.search(R'''\d\.\d\d.'''    ,   lowerCAmelCase__		).group()
        else:
            UpperCAmelCase__     :						Union[str, Any]						=							re.search(R'''\d\.\d.'''    ,   lowerCAmelCase__		).group()
        if int(match[0]		) < 6:
            UpperCAmelCase__     :						Optional[int]						=							old_name.replace(lowerCAmelCase__    ,   ''''''		)
            UpperCAmelCase__     :						Optional[Any]						=							trimmed_name.replace('''network'''    ,   match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]		)
            UpperCAmelCase__     :						Union[str, Any]						=							"intermediate_stages." + trimmed_name
        else:
            UpperCAmelCase__     :						str						=							old_name.replace(lowerCAmelCase__    ,   ''''''		)
            if int(match[2]		) < num_meta4D_last_stage:
                UpperCAmelCase__     :						int						=							trimmed_name.replace('''network'''    ,   '''meta4D_layers.blocks.''' + match[2]		)
            else:
                UpperCAmelCase__     :						Union[str, Any]						=							str(int(match[2]		) - num_meta4D_last_stage		)
                UpperCAmelCase__     :						Tuple						=							trimmed_name.replace('''network'''    ,   '''meta3D_layers.blocks.''' + layer_index		)
                if "norm1" in old_name:
                    UpperCAmelCase__     :						Dict						=							trimmed_name.replace('''norm1'''    ,   '''layernorm1'''		)
                elif "norm2" in old_name:
                    UpperCAmelCase__     :						Optional[Any]						=							trimmed_name.replace('''norm2'''    ,   '''layernorm2'''		)
                elif "fc1" in old_name:
                    UpperCAmelCase__     :						str						=							trimmed_name.replace('''fc1'''    ,   '''linear_in'''		)
                elif "fc2" in old_name:
                    UpperCAmelCase__     :						List[Any]						=							trimmed_name.replace('''fc2'''    ,   '''linear_out'''		)

            UpperCAmelCase__     :						Tuple						=							"last_stage." + trimmed_name

    elif "network" in old_name and re.search(R'''.\d.'''    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						Union[str, Any]						=							old_name.replace('''network'''    ,   '''intermediate_stages'''		)

    if "fc" in new_name:
        UpperCAmelCase__     :						Optional[int]						=							new_name.replace('''fc'''    ,   '''convolution'''		)
    elif ("norm1" in new_name) and ("layernorm1" not in new_name):
        UpperCAmelCase__     :						Optional[int]						=							new_name.replace('''norm1'''    ,   '''batchnorm_before'''		)
    elif ("norm2" in new_name) and ("layernorm2" not in new_name):
        UpperCAmelCase__     :						List[Any]						=							new_name.replace('''norm2'''    ,   '''batchnorm_after'''		)
    if "proj" in new_name:
        UpperCAmelCase__     :						Optional[int]						=							new_name.replace('''proj'''    ,   '''projection'''		)
    if "dist_head" in new_name:
        UpperCAmelCase__     :						List[Any]						=							new_name.replace('''dist_head'''    ,   '''distillation_classifier'''		)
    elif "head" in new_name:
        UpperCAmelCase__     :						List[Any]						=							new_name.replace('''head'''    ,   '''classifier'''		)
    elif "patch_embed" in new_name:
        UpperCAmelCase__     :						Tuple						=							"efficientformer." + new_name
    elif new_name == "norm.weight" or new_name == "norm.bias":
        UpperCAmelCase__     :						Dict						=							new_name.replace('''norm'''    ,   '''layernorm'''		)
        UpperCAmelCase__     :						Optional[Any]						=							"efficientformer." + new_name
    else:
        UpperCAmelCase__     :						Any						=							"efficientformer.encoder." + new_name

    return new_name





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Optional[Any]:




    for key in checkpoint.copy().keys():
        UpperCAmelCase__     :						List[Any]						=							checkpoint.pop(lowerCAmelCase__		)
        UpperCAmelCase__     :						str						=							val

    return checkpoint





def    a__			(   )       ->      List[str]:
    UpperCAmelCase__     :						Dict						=							"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCAmelCase__     :						Union[str, Any]						=							Image.open(requests.get(lowerCAmelCase__    ,   stream=lowerCAmelCase__		).raw		)

    return image





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						Union[str, Any]						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)["model"]
    UpperCAmelCase__     :						Any						=							EfficientFormerConfig.from_json_file(lowerCAmelCase__		)
    UpperCAmelCase__     :						Optional[int]						=							EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[Any]						=							"_".join(checkpoint_path.split('''/'''		)[-1].split('''.'''		)[0].split('''_'''		)[:-1]		)

    UpperCAmelCase__     :						List[Any]						=							config.depths[-1] - config.num_metaad_blocks + 1
    UpperCAmelCase__     :						List[Any]						=							convert_torch_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__		)

    model.load_state_dict(lowerCAmelCase__		)
    model.eval()

    UpperCAmelCase__     :						str						=							{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    # prepare image
    UpperCAmelCase__     :						Optional[Any]						=							prepare_img()
    UpperCAmelCase__     :						List[Any]						=							2_56
    UpperCAmelCase__     :						str						=							2_24
    UpperCAmelCase__     :						Union[str, Any]						=							EfficientFormerImageProcessor(
        size={'''shortest_edge''': image_size}    ,   crop_size={'''height''': crop_size, '''width''': crop_size}    ,   resample=pillow_resamplings['''bicubic''']    ,   )
    UpperCAmelCase__     :						Any						=							processor(images=lowerCAmelCase__    ,   return_tensors='''pt'''		).pixel_values

    # original processing pipeline
    UpperCAmelCase__     :						Union[str, Any]						=							Compose(
        [
            Resize(lowerCAmelCase__    ,   interpolation=pillow_resamplings['''bicubic''']		),
            CenterCrop(lowerCAmelCase__		),
            ToTensor(),
            Normalize(lowerCAmelCase__    ,   lowerCAmelCase__		),
        ]		)
    UpperCAmelCase__     :						Optional[Any]						=							image_transforms(lowerCAmelCase__		).unsqueeze(0		)

    assert torch.allclose(lowerCAmelCase__    ,   lowerCAmelCase__		)

    UpperCAmelCase__     :						int						=							model(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[Any]						=							outputs.logits

    UpperCAmelCase__     :						Optional[Any]						=							(1, 10_00)

    if "l1" in model_name:
        UpperCAmelCase__     :						int						=							torch.Tensor(
            [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]		)
        assert torch.allclose(logits[0, :10]    ,   lowerCAmelCase__    ,   atol=1E-3		)
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        UpperCAmelCase__     :						Any						=							torch.Tensor(
            [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]		)
        assert torch.allclose(logits[0, :10]    ,   lowerCAmelCase__    ,   atol=1E-3		)
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        UpperCAmelCase__     :						str						=							torch.Tensor(
            [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]		)
        assert logits.shape == expected_shape
    else:
        raise ValueError(
            F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""		)

    # Save Checkpoints
    Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
    model.save_pretrained(lowerCAmelCase__		)
    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)
    processor.save_pretrained(lowerCAmelCase__		)
    print(F"""Processor successfuly saved at {pytorch_dump_path}"""		)

    if push_to_hub:
        print('''Pushing model to the hub...'''		)

        model.push_to_hub(
            repo_id=F"""Bearnardd/{pytorch_dump_path}"""    ,   commit_message='''Add model'''    ,   use_temp_dir=lowerCAmelCase__    ,   )
        processor.push_to_hub(
            repo_id=F"""Bearnardd/{pytorch_dump_path}"""    ,   commit_message='''Add image processor'''    ,   use_temp_dir=lowerCAmelCase__    ,   )


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--pytorch_model_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to EfficientFormer pytorch checkpoint.''',
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The json file for EfficientFormer model config.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )

  parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
  parser.add_argument(
      '''--no-push_to_hub''',
      dest='''push_to_hub''',
      action='''store_false''',
      help='''Do not push model and image processor to the hub''',
  )
  parser.set_defaults(push_to_hub=True)

  UpperCamelCase__          = parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )




import qiskit


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						qiskit.Aer.get_backend("aer_simulator" )

		# Create a Quantum Circuit acting on the q register
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						qiskit.QuantumCircuit(a     , a )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0 )
		circuit.x(1 )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]     , [0, 1] )

		# Execute the circuit on the qasm simulator
		SCREAMING_SNAKE_CASE_				:Any				     =						qiskit.execute(a     , a     , shots=1000 )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(a )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			single_qubit_measure(2, 2)
		print(F'''Total count for various states are: {counts}''')






