import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

				return name





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__lowercase 			=							orig_state_dict.pop(lowercase							)

								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
												__lowercase 			=							key.split('''.'''							)
												__lowercase			,     __lowercase 			=							int(key_split[2]							), int(key_split[4]							)
												__lowercase 			=							config.vision_config.hidden_size
												if "weight" in key:
																__lowercase 			=							val[:dim, :]
																__lowercase 			=							val[dim : dim * 2, :]
																__lowercase 			=							val[-dim:, :]
												else:
																__lowercase 			=							val[:dim]
																__lowercase 			=							val[dim : dim * 2]
																__lowercase 			=							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
												__lowercase 			=							key.split('''.'''							)
												__lowercase 			=							int(key_split[3]							)
												__lowercase 			=							config.text_config.hidden_size
												if "weight" in key:
																__lowercase 			=							val[:dim, :]
																__lowercase 			=							val[
																    dim : dim * 2, :
																]
																__lowercase 			=							val[-dim:, :]
												else:
																__lowercase 			=							val[:dim]
																__lowercase 			=							val[dim : dim * 2]
																__lowercase 			=							val[-dim:]
								else:
												__lowercase 			=							rename_key(lowercase							)
												# 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
												):
																__lowercase 			=							val.squeeze_()
												else:
																__lowercase 			=							val

				return orig_state_dict





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



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





@torch.no_grad()
def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase="groupvit-gcc-yfcc"							,			lowercase=False							):







				"""simple docstring"""



				__lowercase 			=							GroupViTConfig()
				__lowercase 			=							GroupViTModel(lowercase							).eval()

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

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

				with torch.no_grad():
								__lowercase 			=							model(**lowercase							)

				if model_name == "groupvit-gcc-yfcc":
								__lowercase 			=							torch.tensor([[13.3523, 6.3629]]							)
				elif model_name == "groupvit-gcc-redcaps":
								__lowercase 			=							torch.tensor([[16.1873, 8.6230]]							)
				else:
								raise ValueError(F"Model name {model_name} not supported."							)
				assert torch.allclose(outputs.logits_per_image							,			lowercase							,			atol=1E-3							)

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

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


if __name__ == "__main__":
		__a  :     List[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`.""",
		)
		__a  :     Dict       =  parser.parse_args()

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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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

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

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

				# Execute the circuit on the simulator
				__lowercase 			=							qiskit.execute(lowercase							,			lowercase							,			shots=1000							)

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


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