





import qiskit
from qiskit.providers import Backend

def     a						(      _lowercase   :     int ,						_lowercase   :     int       )     ->tuple[qiskit.QuantumCircuit, str, str]:
		UpperCamelCase__  ,			UpperCamelCase__         =	bin(_lowercase       )[2:], bin(_lowercase       )[2:]  # Remove leading '0b'

		# Ensure that both strings are of the same length
		if len(_lowercase       ) > len(_lowercase       ):
				UpperCamelCase__         =	y.zfill(len(_lowercase       )       )
		else:
				UpperCamelCase__         =	x.zfill(len(_lowercase       )       )

		# We need (3 * number of bits in the larger number)+1 qBits
		# The second parameter is the number of classical registers, to measure the result
		UpperCamelCase__         =	qiskit.QuantumCircuit((len(_lowercase       ) * 3) + 1 ,						len(_lowercase       ) + 1       )

		# We are essentially "not-ing" the bits that are 1
		# Reversed because it's easier to perform ops on more significant bits
		for i in range(len(_lowercase       )       ):
				if x[::-1][i] == "1":
						circuit.x(_lowercase       )
		for j in range(len(_lowercase       )       ):
				if y[::-1][j] == "1":
						circuit.x(len(_lowercase       ) + j       )

		return circuit, x, y

def     a						(      _lowercase   :     qiskit.QuantumCircuit ,						_lowercase   :     int ,						_lowercase   :     int ,						_lowercase   :     int ,						_lowercase   :     int ,						)     ->Tuple:
		circuit.ccx(_lowercase ,						_lowercase ,						_lowercase       )
		circuit.cx(_lowercase ,						_lowercase       )
		circuit.ccx(_lowercase ,						_lowercase ,						_lowercase       )
		circuit.cx(_lowercase ,						_lowercase       )
		circuit.cx(_lowercase ,						_lowercase       )

def     a						(      _lowercase   :     int ,						_lowercase   :     int ,						_lowercase   :     Backend = qiskit.Aer.get_backend("""aer_simulator"""       ) ,						)     ->int:

		if vala < 0 or vala < 0:
				raise ValueError("""Both Integers must be positive!"""       )

		# Store the Integers
		UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	store_two_classics(_lowercase ,						_lowercase       )
		for i in range(len(_lowercase       )       ):
				full_adder(_lowercase ,						_lowercase ,						len(_lowercase       ) + i ,						len(_lowercase       ) + len(_lowercase       ) + i ,						len(_lowercase       ) + len(_lowercase       ) + i + 1       )
				circuit.barrier()  # Optional, just for aesthetics

		# Measure the resultant qBits
		for i in range(len(_lowercase       ) + 1       ):
				circuit.measure([(len(_lowercase       ) * 2) + i] ,						[i]       )

		UpperCamelCase__         =	qiskit.execute(_lowercase ,						_lowercase ,						shots=1       ).result()

		# The result is in binary. Convert it back to int
		return int(list(res.get_counts()       )[0] ,						2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()












import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

def     a						(      _lowercase   :     Union[str, Any]       )     ->str:
		UpperCamelCase__         =	tf.image.resize(_lowercase ,						(3_4_6, 3_4_6)       )
		UpperCamelCase__         =	tf.image.crop_to_bounding_box(_lowercase ,						(3_4_6 - 2_8_9) // 2 ,						(3_4_6 - 2_8_9) // 2 ,						2_8_9 ,						2_8_9       )
		return image

def     a						(      )     ->Optional[int]:
		UpperCamelCase__         =	EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""       )
		UpperCamelCase__         =	2_8_9
		UpperCamelCase__         =	6_4_0
		UpperCamelCase__         =	{"""0""": """LABEL_0""", """1""": """LABEL_1"""}
		UpperCamelCase__         =	{"""LABEL_0""": 0, """LABEL_1""": 1}
		UpperCamelCase__         =	[]

		UpperCamelCase__         =	BertConfig()
		UpperCamelCase__         =	AlignConfig.from_text_vision_configs(
		    text_config=_lowercase ,						vision_config=_lowercase ,						projection_dim=6_4_0       )
		return config

def     a						(      )     ->Optional[int]:
		UpperCamelCase__         =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		UpperCamelCase__         =	Image.open(requests.get(_lowercase ,						stream=_lowercase       ).raw       )
		return im

def     a						(      )     ->Optional[int]:
		UpperCamelCase__         =	EfficientNetImageProcessor(
		    do_center_crop=_lowercase ,						rescale_factor=1 / 127.5 ,						rescale_offset=_lowercase ,						do_normalize=_lowercase ,						include_top=_lowercase ,						resample=Image.BILINEAR ,						)
		UpperCamelCase__         =	BertTokenizer.from_pretrained("""bert-base-uncased"""       )
		UpperCamelCase__         =	6_4
		UpperCamelCase__         =	AlignProcessor(image_processor=_lowercase ,						tokenizer=_lowercase       )
		return processor

def     a						(      _lowercase   :     str       )     ->int:
		# EfficientNet image encoder
		UpperCamelCase__         =	[v.split("""_"""       )[0].split("""block"""       )[1] for v in original_param_names if v.startswith("""block"""       )]
		UpperCamelCase__         =	list(set(_lowercase       )       )
		UpperCamelCase__         =	sorted(_lowercase       )
		UpperCamelCase__         =	len(_lowercase       )
		UpperCamelCase__         =	{b: str(_lowercase       ) for b, i in zip(_lowercase ,						range(_lowercase       )       )}

		UpperCamelCase__         =	[]
		rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")       )
		rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")       )
		rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")       )
		rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")       )
		rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")       )

		for b in block_names:
				UpperCamelCase__         =	block_name_mapping[b]
				rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")       )
				rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")       )
				rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")       )
				rename_keys.append(
				    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")       )
				rename_keys.append(
				    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")       )
				rename_keys.append(
				    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")       )
				rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")       )
				rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")       )
				rename_keys.append(
				    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")       )
				rename_keys.append(
				    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")       )

				rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")       )
				rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")       )
				rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")       )
				rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")       )
				rename_keys.append(
				    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")       )
				rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")       )
				rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")       )
				rename_keys.append(
				    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")       )
				rename_keys.append(
				    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")       )

		UpperCamelCase__         =	{}
		for item in rename_keys:
				if item[0] in original_param_names:
						UpperCamelCase__         =	"""vision_model.""" + item[1]

    # BERT text encoder
		UpperCamelCase__         =	[]
		UpperCamelCase__         =	"""tf_bert_model/bert"""
		UpperCamelCase__         =	"""text_model"""
		for i in range(1_2       ):
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.query.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.query.bias""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.key.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.key.bias""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.value.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.value.bias""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
				        F"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
				    )       )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
				        F"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
				    )       )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", F"""{new}.encoder.layer.{i}.output.dense.weight""")       )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/dense/bias:0""", F"""{new}.encoder.layer.{i}.output.dense.bias""")       )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")       )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")       )

		rename_keys.append((F"""{old}/embeddings/word_embeddings/weight:0""", F"""{new}.embeddings.word_embeddings.weight""")       )
		rename_keys.append(
		    (F"""{old}/embeddings/position_embeddings/embeddings:0""", F"""{new}.embeddings.position_embeddings.weight""")       )
		rename_keys.append(
		    (F"""{old}/embeddings/token_type_embeddings/embeddings:0""", F"""{new}.embeddings.token_type_embeddings.weight""")       )
		rename_keys.append((F"""{old}/embeddings/LayerNorm/gamma:0""", F"""{new}.embeddings.LayerNorm.weight""")       )
		rename_keys.append((F"""{old}/embeddings/LayerNorm/beta:0""", F"""{new}.embeddings.LayerNorm.bias""")       )

		rename_keys.append((F"""{old}/pooler/dense/kernel:0""", F"""{new}.pooler.dense.weight""")       )
		rename_keys.append((F"""{old}/pooler/dense/bias:0""", F"""{new}.pooler.dense.bias""")       )
		rename_keys.append(("""dense/kernel:0""", """text_projection.weight""")       )
		rename_keys.append(("""dense/bias:0""", """text_projection.bias""")       )
		rename_keys.append(("""dense/bias:0""", """text_projection.bias""")       )
		rename_keys.append(("""temperature:0""", """temperature""")       )

		for item in rename_keys:
				if item[0] in original_param_names:
						UpperCamelCase__         =	item[1]
		return key_mapping

def     a						(      _lowercase   :     int ,						_lowercase   :     str ,						_lowercase   :     int       )     ->Optional[Any]:
		list(hf_params.keys()       )

		for key, value in tf_params.items():
				if key not in key_mapping:
						continue

				UpperCamelCase__         =	key_mapping[key]
				if "_conv" in key and "kernel" in key:
						UpperCamelCase__         =	torch.from_numpy(_lowercase       ).permute(3 ,						2 ,						0 ,						1       )
				elif "embeddings" in key:
						UpperCamelCase__         =	torch.from_numpy(_lowercase       )
				elif "depthwise_kernel" in key:
						UpperCamelCase__         =	torch.from_numpy(_lowercase       ).permute(2 ,						3 ,						0 ,						1       )
				elif "kernel" in key:
						UpperCamelCase__         =	torch.from_numpy(np.transpose(_lowercase       )       )
				elif "temperature" in key:
						UpperCamelCase__         =	value
				elif "bn/gamma" or "bn/beta" in key:
						UpperCamelCase__         =	torch.from_numpy(np.transpose(_lowercase       )       ).squeeze()
				else:
						UpperCamelCase__         =	torch.from_numpy(_lowercase       )

				# Replace HF parameters with original TF model parameters
				hf_params[hf_key].copy_(_lowercase       )

@torch.no_grad()
def     a						(      _lowercase   :     List[str] ,						_lowercase   :     Any ,						_lowercase   :     Dict ,						_lowercase   :     Optional[Any]       )     ->str:
		UpperCamelCase__         =	6_4
		UpperCamelCase__         =	Tokenizer(_lowercase       )
		UpperCamelCase__         =	align.Align("""efficientnet-b7""" ,						"""bert-base""" ,						6_4_0 ,						_lowercase ,						tok.get_vocab_size()       )
		original_model.compile()
		original_model.load_weights(_lowercase       )

		UpperCamelCase__         =	original_model.trainable_variables
		UpperCamelCase__         =	original_model.non_trainable_variables
		UpperCamelCase__         =	{param.name: param.numpy() for param in tf_params}
		for param in tf_non_train_params:
				UpperCamelCase__         =	param.numpy()
		UpperCamelCase__         =	list(tf_params.keys()       )

		# Load HuggingFace model
		UpperCamelCase__         =	get_align_config()
		UpperCamelCase__         =	AlignModel(_lowercase       ).eval()
		UpperCamelCase__         =	hf_model.state_dict()

		# Create src-to-dst parameter name mapping dictionary
		print("""Converting parameters..."""       )
		UpperCamelCase__         =	rename_keys(_lowercase       )
		replace_params(_lowercase ,						_lowercase ,						_lowercase       )

		# Initialize processor
		UpperCamelCase__         =	get_processor()
		UpperCamelCase__         =	processor(
		    images=prepare_img() ,						text="""A picture of a cat""" ,						padding="""max_length""" ,						max_length=6_4 ,						return_tensors="""pt"""       )

		# HF model inference
		hf_model.eval()
		with torch.no_grad():
				UpperCamelCase__         =	hf_model(**_lowercase       )

		UpperCamelCase__         =	outputs.image_embeds.detach().numpy()
		UpperCamelCase__         =	outputs.text_embeds.detach().numpy()

		# Original model inference
		UpperCamelCase__         =	False
		UpperCamelCase__         =	EfficientNetImageProcessor(
		    do_center_crop=_lowercase ,						do_rescale=_lowercase ,						do_normalize=_lowercase ,						include_top=_lowercase ,						resample=Image.BILINEAR ,						)
		UpperCamelCase__         =	tf_image_processor(images=prepare_img() ,						return_tensors="""tf""" ,						data_format="""channels_last"""       )["""pixel_values"""]
		UpperCamelCase__         =	tok(tf.constant(["""A picture of a cat"""]       )       )

		UpperCamelCase__         =	original_model.image_encoder(_lowercase ,						training=_lowercase       )
		UpperCamelCase__         =	original_model.text_encoder(_lowercase ,						training=_lowercase       )

		UpperCamelCase__         =	tf.nn.la_normalize(_lowercase ,						axis=-1       )
		UpperCamelCase__         =	tf.nn.la_normalize(_lowercase ,						axis=-1       )

		# Check whether original and HF model outputs match  -> np.allclose
		if not np.allclose(_lowercase ,						_lowercase ,						atol=1e-3       ):
				raise ValueError("""The predicted image features are not the same."""       )
		if not np.allclose(_lowercase ,						_lowercase ,						atol=1e-3       ):
				raise ValueError("""The predicted text features are not the same."""       )
		print("""Model outputs match!"""       )

		if save_model:
				# Create folder to save model
				if not os.path.isdir(_lowercase       ):
						os.mkdir(_lowercase       )
				# Save converted model and image processor
				hf_model.save_pretrained(_lowercase       )
				processor.save_pretrained(_lowercase       )

		if push_to_hub:
				# Push model and image processor to hub
				print("""Pushing converted ALIGN to the hub..."""       )
				processor.push_to_hub("""align-base"""       )
				hf_model.push_to_hub("""align-base"""       )


if __name__ == "__main__":
				__lowerCAmelCase     =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_path",
				    default="./weights/model-weights",
				    type=str,
				    help="Path to the pretrained TF ALIGN checkpoint.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="hf_model",
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--save_model", action="store_true", help="Save model to local")
				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

				__lowerCAmelCase     =			parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





