


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase     		=							logging.get_logger(__name__)
def 							UpperCamelCase_(					_A :Dict					,  _A :str=False	)->		Union[str, Any]:
			UpperCamelCase__       =							[]
			for i in range(config.num_hidden_layers	):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')	)
						rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')	)
						rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')	)
						rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')	)
						rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')	)
						rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')	)
						rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')	)
						rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')	)
						rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')	)
						rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')	)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.embeddings.position_embeddings"),
			    ]	)

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.bias"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.bias"),
						    ]	)

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						UpperCamelCase__       =							[(pair[0], pair[1][4:]) if pair[1].startswith("deit"	) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]	)

			return rename_keys
def 							UpperCamelCase_(					_A :int					,  _A :int					,  _A :Union[str, Any]=False	)->		Any:
			for i in range(config.num_hidden_layers	):
						if base_model:
									UpperCamelCase__       =							""
						else:
									UpperCamelCase__       =							"deit."
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCamelCase__       =							state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
						UpperCamelCase__       =							state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase__       =							in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCamelCase__       =							in_proj_bias[: config.hidden_size]
						UpperCamelCase__       =							in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase__       =							in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase__       =							in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase__       =							in_proj_bias[-config.hidden_size :]
def 							UpperCamelCase_(					_A :Optional[Any]					,  _A :List[str]					,  _A :int	)->		Any:
			UpperCamelCase__       =							dct.pop(_A	)
			UpperCamelCase__       =							val
def 							UpperCamelCase_(					)->		List[Any]:
			UpperCamelCase__       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__       =							Image.open(requests.get(_A					,  stream=_A	).raw	)
			return im
@torch.no_grad()
def 							UpperCamelCase_(					_A :Any					,  _A :Dict	)->		Tuple:
			UpperCamelCase__       =							DeiTConfig()
			# all deit models have fine-tuned heads
			UpperCamelCase__       =							False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCamelCase__       =							10_00
			UpperCamelCase__       =							"huggingface/label-files"
			UpperCamelCase__       =							"imagenet-1k-id2label.json"
			UpperCamelCase__       =							json.load(open(hf_hub_download(_A					,  _A					,  repo_type="dataset"	)					,  "r"	)	)
			UpperCamelCase__       =							{int(_A	): v for k, v in idalabel.items()}
			UpperCamelCase__       =							idalabel
			UpperCamelCase__       =							{v: k for k, v in idalabel.items()}
			UpperCamelCase__       =							int(deit_name[-6:-4]	)
			UpperCamelCase__       =							int(deit_name[-3:]	)
			# size of the architecture
			if deit_name[9:].startswith("tiny"	):
						UpperCamelCase__       =							1_92
						UpperCamelCase__       =							7_68
						UpperCamelCase__       =							12
						UpperCamelCase__       =							3
			elif deit_name[9:].startswith("small"	):
						UpperCamelCase__       =							3_84
						UpperCamelCase__       =							15_36
						UpperCamelCase__       =							12
						UpperCamelCase__       =							6
			if deit_name[9:].startswith("base"	):
						pass
			elif deit_name[4:].startswith("large"	):
						UpperCamelCase__       =							10_24
						UpperCamelCase__       =							40_96
						UpperCamelCase__       =							24
						UpperCamelCase__       =							16

			# load original model from timm
			UpperCamelCase__       =							timm.create_model(_A					,  pretrained=_A	)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			UpperCamelCase__       =							timm_model.state_dict()
			UpperCamelCase__       =							create_rename_keys(_A					,  _A	)
			for src, dest in rename_keys:
						rename_key(_A					,  _A					,  _A	)
			read_in_q_k_v(_A					,  _A					,  _A	)

			# load HuggingFace model
			UpperCamelCase__       =							DeiTForImageClassificationWithTeacher(_A	).eval()
			model.load_state_dict(_A	)

			# Check outputs on an image, prepared by DeiTImageProcessor
			UpperCamelCase__       =							int(
			    (2_56 / 2_24) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			UpperCamelCase__       =							DeiTImageProcessor(size=_A					,  crop_size=config.image_size	)
			UpperCamelCase__       =							image_processor(images=prepare_img()					,  return_tensors="pt"	)
			UpperCamelCase__       =							encoding["pixel_values"]
			UpperCamelCase__       =							model(_A	)

			UpperCamelCase__       =							timm_model(_A	)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(_A					,  outputs.logits					,  atol=1e-3	)

			Path(_A	).mkdir(exist_ok=_A	)
			print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''	)
			model.save_pretrained(_A	)
			print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
			image_processor.save_pretrained(_A	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--deit_name',
						    default='vit_deit_base_distilled_patch16_224',
						    type=str,
						    help='Name of the DeiT 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.'
						)

						__UpperCamelCase     		=							parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





'''simple docstring'''




import qiskit


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						qiskit.Aer.get_backend('aer_simulator'    )

  # Create a Quantum Circuit acting on the q register
  UpperCAmelCase    				=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

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

  # Execute the circuit on the simulator
  UpperCAmelCase    				=						qiskit.execute(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, shots=1000    )

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


if __name__ == "__main__":
  print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')


