



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			UpperCamelCase         =					"huggingface/label-files"
			UpperCamelCase         =					"imagenet-1k-id2label.json"
			UpperCamelCase         =					json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  repo_type="""dataset"""	)       ,  """r"""	)	)
			UpperCamelCase         =					{int(__SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
			UpperCamelCase         =					{v: k for k, v in idalabel.items()}

			UpperCamelCase         =					"std_conv" if "bit" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCamelCase         =					BitConfig(
			    conv_layer=__SCREAMING_SNAKE_CASE       ,  num_labels=1_000       ,  idalabel=__SCREAMING_SNAKE_CASE       ,  labelaid=__SCREAMING_SNAKE_CASE       ,  )

			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




			if "stem.conv" in name:
						UpperCamelCase         =					name.replace("""stem.conv"""       ,  """bit.embedder.convolution"""	)
			if "blocks" in name:
						UpperCamelCase         =					name.replace("""blocks"""       ,  """layers"""	)
			if "head.fc" in name:
						UpperCamelCase         =					name.replace("""head.fc"""       ,  """classifier.1"""	)
			if name.startswith("""norm"""	):
						UpperCamelCase         =					"bit." + name
			if "bit" not in name and "classifier" not in name:
						UpperCamelCase         =					"bit.encoder." + name

			return name






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase         =					Image.open(requests.get(__SCREAMING_SNAKE_CASE       ,  stream=__SCREAMING_SNAKE_CASE	).raw	)
			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any]=False	):





			"""simple docstring"""




			UpperCamelCase         =					get_config(__SCREAMING_SNAKE_CASE	)

			# load original model from timm
			UpperCamelCase         =					create_model(__SCREAMING_SNAKE_CASE       ,  pretrained=__SCREAMING_SNAKE_CASE	)
			timm_model.eval()

			# load state_dict of original model
			UpperCamelCase         =					timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCamelCase         =					state_dict.pop(__SCREAMING_SNAKE_CASE	)
						UpperCamelCase         =					val.squeeze() if "head" in key else val

			# load HuggingFace model
			UpperCamelCase         =					BitForImageClassification(__SCREAMING_SNAKE_CASE	)
			model.eval()
			model.load_state_dict(__SCREAMING_SNAKE_CASE	)

			# create image processor
			UpperCamelCase         =					create_transform(**resolve_data_config({}       ,  model=__SCREAMING_SNAKE_CASE	)	)
			UpperCamelCase         =					transform.transforms

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

			UpperCamelCase         =					BitImageProcessor(
			    do_resize=__SCREAMING_SNAKE_CASE       ,  size={"""shortest_edge""": timm_transforms[0].size}       ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,  do_center_crop=__SCREAMING_SNAKE_CASE       ,  crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}       ,  do_normalize=__SCREAMING_SNAKE_CASE       ,  image_mean=timm_transforms[-1].mean.tolist()       ,  image_std=timm_transforms[-1].std.tolist()       ,  )

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					transform(__SCREAMING_SNAKE_CASE	).unsqueeze(0	)
			UpperCamelCase         =					processor(__SCREAMING_SNAKE_CASE       ,  return_tensors="""pt"""	).pixel_values

			# verify pixel values
			assert torch.allclose(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE	)

			# verify logits
			with torch.no_grad():
						UpperCamelCase         =					model(__SCREAMING_SNAKE_CASE	)
						UpperCamelCase         =					outputs.logits

			print("""Logits:"""       ,  logits[0, :3]	)
			print("""Predicted class:"""       ,  model.config.idalabel[logits.argmax(-1	).item()]	)
			UpperCamelCase         =					timm_model(__SCREAMING_SNAKE_CASE	)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(__SCREAMING_SNAKE_CASE       ,  outputs.logits       ,  atol=1e-3	)
			print("""Looks ok!"""	)

			if pytorch_dump_folder_path is not None:
						Path(__SCREAMING_SNAKE_CASE	).mkdir(exist_ok=__SCREAMING_SNAKE_CASE	)
						print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'	)
						model.save_pretrained(__SCREAMING_SNAKE_CASE	)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE	)

			if push_to_hub:
						print(f'Pushing model {model_name} and processor to the hub'	)
						model.push_to_hub(f'ybelkada/{model_name}'	)
						processor.push_to_hub(f'ybelkada/{model_name}'	)


if __name__ == "__main__":
			__snake_case            =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="resnetv2_50x1_bitm",
			    type=str,
			    help="Name of the BiT timm model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument(
			    "--push_to_hub",
			    action="store_true",
			    help="Whether to push the model to the hub.",
			)

			__snake_case            =   parser.parse_args()
			convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import qiskit






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 2	):





			"""simple docstring"""




			UpperCamelCase         =					qubits

			# Using Aer's simulator
			UpperCamelCase         =					qiskit.Aer.get_backend("""aer_simulator"""	)

			# Creating a Quantum Circuit acting on the q register
			UpperCamelCase         =					qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0	)

			for i in range(1       ,  SCREAMING_SNAKE_CASE_	):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,  SCREAMING_SNAKE_CASE_	)

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE_	)	)       ,  list(range(SCREAMING_SNAKE_CASE_	)	)	)

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			UpperCamelCase         =					qiskit.execute(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  shots=1_000	)

			return job.result().get_counts(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			print(F'''Total count for various states are: {quantum_entanglement(3)}''')




