

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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Union[str, Any]=False						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						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"vit.encoder.layer.{i}.layernorm_before.weight")						)
												rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")						)
												rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")						)
												rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")						)
												rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")						)
												rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")						)
												rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")						)
												rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")						)
												rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")						)
												rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")						)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('cls_token', 'vit.embeddings.cls_token'),
						        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
						        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
						        ('pos_embed', 'vit.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 "vit" from all keys that start with "vit"
												lowerCAmelCase__        =  [(pair[0], pair[1][4:]) if pair[1].startswith('vit'						) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('norm.weight', 'vit.layernorm.weight'),
												        ('norm.bias', 'vit.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]						)

						return rename_keys







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Dict=False						)					->				Union[str, Any]:

						"""simple docstring"""



						for i in range(config.num_hidden_layers						):
												if base_model:
																		lowerCAmelCase__        =  ''
												else:
																		lowerCAmelCase__        =  'vit.'
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCAmelCase__        =  state_dict.pop(F"blocks.{i}.attn.qkv.weight"						)
												lowerCAmelCase__        =  state_dict.pop(F"blocks.{i}.attn.qkv.bias"						)
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__        =  in_proj_weight[
												    : config.hidden_size, :
												]
												lowerCAmelCase__        =  in_proj_bias[: config.hidden_size]
												lowerCAmelCase__        =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase__        =  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase__        =  in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCAmelCase__        =  in_proj_bias[-config.hidden_size :]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  ['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
						lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      )					->				Optional[int]:

						"""simple docstring"""



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







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Any						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  ViTConfig()
						lowerCAmelCase__        =  False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												lowerCAmelCase__        =  True
												lowerCAmelCase__        =  int(vit_name[-12:-10]						)
												lowerCAmelCase__        =  int(vit_name[-9:-6]						)
						else:
												lowerCAmelCase__        =  1000
												lowerCAmelCase__        =  'huggingface/label-files'
												lowerCAmelCase__        =  'imagenet-1k-id2label.json'
												lowerCAmelCase__        =  json.load(open(hf_hub_download(UpperCamelCase_      ,    UpperCamelCase_      ,    repo_type='dataset'						)      ,    'r'						)						)
												lowerCAmelCase__        =  {int(UpperCamelCase_						): v for k, v in idalabel.items()}
												lowerCAmelCase__        =  idalabel
												lowerCAmelCase__        =  {v: k for k, v in idalabel.items()}
												lowerCAmelCase__        =  int(vit_name[-6:-4]						)
												lowerCAmelCase__        =  int(vit_name[-3:]						)
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith('tiny'						):
																		lowerCAmelCase__        =  192
																		lowerCAmelCase__        =  768
																		lowerCAmelCase__        =  12
																		lowerCAmelCase__        =  3
												elif vit_name[9:].startswith('small'						):
																		lowerCAmelCase__        =  384
																		lowerCAmelCase__        =  1536
																		lowerCAmelCase__        =  12
																		lowerCAmelCase__        =  6
												else:
																		pass
						else:
												if vit_name[4:].startswith('small'						):
																		lowerCAmelCase__        =  768
																		lowerCAmelCase__        =  2304
																		lowerCAmelCase__        =  8
																		lowerCAmelCase__        =  8
												elif vit_name[4:].startswith('base'						):
																		pass
												elif vit_name[4:].startswith('large'						):
																		lowerCAmelCase__        =  1024
																		lowerCAmelCase__        =  4096
																		lowerCAmelCase__        =  24
																		lowerCAmelCase__        =  16
												elif vit_name[4:].startswith('huge'						):
																		lowerCAmelCase__        =  1280
																		lowerCAmelCase__        =  5120
																		lowerCAmelCase__        =  32
																		lowerCAmelCase__        =  16

    # load original model from timm
						lowerCAmelCase__        =  timm.create_model(UpperCamelCase_      ,    pretrained=UpperCamelCase_						)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						lowerCAmelCase__        =  timm_model.state_dict()
						if base_model:
												remove_classification_head_(UpperCamelCase_						)
						lowerCAmelCase__        =  create_rename_keys(UpperCamelCase_      ,    UpperCamelCase_						)
						for src, dest in rename_keys:
												rename_key(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						read_in_q_k_v(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												lowerCAmelCase__        =  ViTModel(UpperCamelCase_						).eval()
						else:
												lowerCAmelCase__        =  ViTForImageClassification(UpperCamelCase_						).eval()
						model.load_state_dict(UpperCamelCase_						)

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												lowerCAmelCase__        =  DeiTImageProcessor(size=config.image_size						)
						else:
												lowerCAmelCase__        =  ViTImageProcessor(size=config.image_size						)
						lowerCAmelCase__        =  image_processor(images=prepare_img()      ,    return_tensors='pt'						)
						lowerCAmelCase__        =  encoding['pixel_values']
						lowerCAmelCase__        =  model(UpperCamelCase_						)

						if base_model:
												lowerCAmelCase__        =  timm_model.forward_features(UpperCamelCase_						)
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(UpperCamelCase_      ,    outputs.pooler_output      ,    atol=1e-3						)
						else:
												lowerCAmelCase__        =  timm_model(UpperCamelCase_						)
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(UpperCamelCase_      ,    outputs.logits      ,    atol=1e-3						)

						Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
						print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"						)
						model.save_pretrained(UpperCamelCase_						)
						print(F"Saving image processor to {pytorch_dump_folder_path}"						)
						image_processor.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case      :      Tuple             =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--vit_name""",
					    default="""vit_base_patch16_224""",
					    type=str,
					    help="""Name of the ViT 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."""
					)

					__snake_case      :      Union[str, Any]             =       parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













import qiskit







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				qiskit.result.counts.Counts:

						"""simple docstring"""



						lowerCAmelCase__        =  qiskit.Aer.get_backend('aer_simulator'						)

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

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

						# Execute the circuit on the simulator
						lowerCAmelCase__        =  qiskit.execute(UpperCamelCase_      ,    UpperCamelCase_      ,    shots=1000						)

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


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





