


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()
_A							     =       logging.get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[int]	)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   tf.image.resize(UpperCamelCase     ,					(346, 346)	)
	a_     			=   tf.image.crop_to_bounding_box(UpperCamelCase     ,					(346 - 289) // 2     ,					(346 - 289) // 2     ,					289     ,					289	)
	return image

def 			__SCREAMING_SNAKE_CASE		(		)     ->	int:



	"""simple docstring"""






	a_     			=   EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""	)
	a_     			=   289
	a_     			=   640
	a_     			=   {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
	a_     			=   {"""LABEL_0""": 0, """LABEL_1""": 1}
	a_     			=   []

	a_     			=   BertConfig()
	a_     			=   AlignConfig.from_text_vision_configs(
	    text_config=UpperCamelCase     ,					vision_config=UpperCamelCase     ,					projection_dim=640	)
	return config

def 			__SCREAMING_SNAKE_CASE		(		)     ->	str:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   EfficientNetImageProcessor(
	    do_center_crop=UpperCamelCase     ,					rescale_factor=1 / 127.5     ,					rescale_offset=UpperCamelCase     ,					do_normalize=UpperCamelCase     ,					include_top=UpperCamelCase     ,					resample=Image.BILINEAR     ,					)
	a_     			=   BertTokenizer.from_pretrained("""bert-base-uncased"""	)
	a_     			=   64
	a_     			=   AlignProcessor(image_processor=UpperCamelCase     ,					tokenizer=UpperCamelCase	)
	return processor

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict	)     ->	Any:



	"""simple docstring"""






	a_     			=   [v.split("""_"""	)[0].split("""block"""	)[1] for v in original_param_names if v.startswith("""block"""	)]
	a_     			=   list(set(UpperCamelCase	)	)
	a_     			=   sorted(UpperCamelCase	)
	a_     			=   len(UpperCamelCase	)
	a_     			=   {b: str(UpperCamelCase	) for b, i in zip(UpperCamelCase     ,					range(UpperCamelCase	)	)}

	a_     			=   []
	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:
		a_     			=   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""")	)

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

    # BERT text encoder
	a_     			=   []
	a_     			=   """tf_bert_model/bert"""
	a_     			=   """text_model"""
	for i in range(12	):
		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:
			a_     			=   item[1]
	return key_mapping

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any     ,					UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any	)     ->	Tuple:



	"""simple docstring"""






	list(hf_params.keys()	)

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

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

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

@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any     ,					UpperCamelCase		:      Any     ,					UpperCamelCase		:      List[Any]	)     ->	Tuple:



	"""simple docstring"""






	a_     			=   64
	a_     			=   Tokenizer(UpperCamelCase	)
	a_     			=   align.Align("""efficientnet-b7"""     ,					"""bert-base"""     ,					640     ,					UpperCamelCase     ,					tok.get_vocab_size()	)
	original_model.compile()
	original_model.load_weights(UpperCamelCase	)

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

	# Load HuggingFace model
	a_     			=   get_align_config()
	a_     			=   AlignModel(UpperCamelCase	).eval()
	a_     			=   hf_model.state_dict()

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

	# Initialize processor
	a_     			=   get_processor()
	a_     			=   processor(
	    images=prepare_img()     ,					text="""A picture of a cat"""     ,					padding="""max_length"""     ,					max_length=64     ,					return_tensors="""pt"""	)

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		a_     			=   hf_model(**UpperCamelCase	)

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

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

	a_     			=   original_model.image_encoder(UpperCamelCase     ,					training=UpperCamelCase	)
	a_     			=   original_model.text_encoder(UpperCamelCase     ,					training=UpperCamelCase	)

	a_     			=   tf.nn.la_normalize(UpperCamelCase     ,					axis=-1	)
	a_     			=   tf.nn.la_normalize(UpperCamelCase     ,					axis=-1	)

	# Check whether original and HF model outputs match  -> np.allclose
	if not np.allclose(UpperCamelCase     ,					UpperCamelCase     ,					atol=1E-3	):
		raise ValueError("""The predicted image features are not the same."""	)
	if not np.allclose(UpperCamelCase     ,					UpperCamelCase     ,					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(UpperCamelCase	):
			os.mkdir(UpperCamelCase	)
		# Save converted model and image processor
		hf_model.save_pretrained(UpperCamelCase	)
		processor.save_pretrained(UpperCamelCase	)

	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__":
			_A							     =       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')

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



class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(_SCREAMING_SNAKE_CASE    ):
										self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
								return self.sequence[:index]







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	print(
	    """Fibonacci Series Using Dynamic Programming\n"""     ,					"""Enter the index of the Fibonacci number you want to calculate """     ,					"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""     ,					sep=""""""     ,					)
	a_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()