



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()
__UpperCAmelCase    	=  logging.get_logger(__name__)





def     __lowerCamelCase							(   __magic_name__						:	Dict    ):
							a__:							List[Any]									=tf.image.resize(__magic_name__  ,			(346, 346)    )
							a__:							List[str]									=tf.image.crop_to_bounding_box(__magic_name__  ,			(346 - 289) // 2  ,			(346 - 289) // 2  ,			289  ,			289    )
							return image





def     __lowerCamelCase							(   ):
							a__:							List[Any]									=EfficientNetConfig.from_pretrained("google/efficientnet-b7"    )
							a__:							Any									=289
							a__:							Any									=640
							a__:							str									={"0": "LABEL_0", "1": "LABEL_1"}
							a__:							Optional[int]									={"LABEL_0": 0, "LABEL_1": 1}
							a__:							Optional[Any]									=[]

							a__:							Any									=BertConfig()
							a__:							Optional[int]									=AlignConfig.from_text_vision_configs(
							    text_config=__magic_name__  ,			vision_config=__magic_name__  ,			projection_dim=640    )
							return config





def     __lowerCamelCase							(   ):
							a__:							str									="http://images.cocodataset.org/val2017/000000039769.jpg"
							a__:							Optional[int]									=Image.open(requests.get(__magic_name__  ,			stream=__magic_name__    ).raw    )
							return im





def     __lowerCamelCase							(   ):
							a__:							Dict									=EfficientNetImageProcessor(
							    do_center_crop=__magic_name__  ,			rescale_factor=1 / 127.5  ,			rescale_offset=__magic_name__  ,			do_normalize=__magic_name__  ,			include_top=__magic_name__  ,			resample=Image.BILINEAR  ,			)
							a__:							Any									=BertTokenizer.from_pretrained("bert-base-uncased"    )
							a__:							int									=64
							a__:							List[str]									=AlignProcessor(image_processor=__magic_name__  ,			tokenizer=__magic_name__    )
							return processor





def     __lowerCamelCase							(   __magic_name__						:	str    ):
							# EfficientNet image encoder
							a__:							List[str]									=[v.split("_"    )[0].split("block"    )[1] for v in original_param_names if v.startswith("block"    )]
							a__:							Dict									=list(set(__magic_name__    )    )
							a__:							List[Any]									=sorted(__magic_name__    )
							a__:							Dict									=len(__magic_name__    )
							a__:							int									={b: str(__magic_name__    ) for b, i in zip(__magic_name__  ,			range(__magic_name__    )    )}

							a__:							List[Any]									=[]
							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__:							List[Any]									=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__:							int									={}
							for item in rename_keys:
														if item[0] in original_param_names:
																					a__:							List[str]									="vision_model." + item[1]

    # BERT text encoder
							a__:							Dict									=[]
							a__:							Any									="tf_bert_model/bert"
							a__:							int									="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__:							str									=item[1]
							return key_mapping





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	Optional[int]  ,			__magic_name__						:	List[Any]    ):
							list(hf_params.keys()    )

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

														a__:							Dict									=key_mapping[key]
														if "_conv" in key and "kernel" in key:
																					a__:							Union[str, Any]									=torch.from_numpy(__magic_name__    ).permute(3  ,			2  ,			0  ,			1    )
														elif "embeddings" in key:
																					a__:							List[Any]									=torch.from_numpy(__magic_name__    )
														elif "depthwise_kernel" in key:
																					a__:							Optional[Any]									=torch.from_numpy(__magic_name__    ).permute(2  ,			3  ,			0  ,			1    )
														elif "kernel" in key:
																					a__:							Optional[int]									=torch.from_numpy(np.transpose(__magic_name__    )    )
														elif "temperature" in key:
																					a__:							Optional[int]									=value
														elif "bn/gamma" or "bn/beta" in key:
																					a__:							Optional[Any]									=torch.from_numpy(np.transpose(__magic_name__    )    ).squeeze()
														else:
																					a__:							Optional[Any]									=torch.from_numpy(__magic_name__    )

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





@torch.no_grad()
def     __lowerCamelCase							(   __magic_name__						:	List[str]  ,			__magic_name__						:	int  ,			__magic_name__						:	Dict  ,			__magic_name__						:	str    ):
							a__:							Optional[int]									=64
							a__:							Any									=Tokenizer(__magic_name__    )
							a__:							List[Any]									=align.Align("efficientnet-b7"  ,			"bert-base"  ,			640  ,			__magic_name__  ,			tok.get_vocab_size()    )
							original_model.compile()
							original_model.load_weights(__magic_name__    )

							a__:							Union[str, Any]									=original_model.trainable_variables
							a__:							Optional[Any]									=original_model.non_trainable_variables
							a__:							Optional[int]									={param.name: param.numpy() for param in tf_params}
							for param in tf_non_train_params:
														a__:							Optional[int]									=param.numpy()
							a__:							List[str]									=list(tf_params.keys()    )

							# Load HuggingFace model
							a__:							Any									=get_align_config()
							a__:							List[str]									=AlignModel(__magic_name__    ).eval()
							a__:							Union[str, Any]									=hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print("Converting parameters..."    )
							a__:							int									=rename_keys(__magic_name__    )
							replace_params(__magic_name__  ,			__magic_name__  ,			__magic_name__    )

							# Initialize processor
							a__:							Tuple									=get_processor()
							a__:							Tuple									=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__:							List[Any]									=hf_model(**__magic_name__    )

							a__:							Tuple									=outputs.image_embeds.detach().numpy()
							a__:							Optional[int]									=outputs.text_embeds.detach().numpy()

							# Original model inference
							a__:							Dict									=False
							a__:							Union[str, Any]									=EfficientNetImageProcessor(
							    do_center_crop=__magic_name__  ,			do_rescale=__magic_name__  ,			do_normalize=__magic_name__  ,			include_top=__magic_name__  ,			resample=Image.BILINEAR  ,			)
							a__:							Tuple									=tf_image_processor(images=prepare_img()  ,			return_tensors="tf"  ,			data_format="channels_last"    )["pixel_values"]
							a__:							Any									=tok(tf.constant(["A picture of a cat"]    )    )

							a__:							Dict									=original_model.image_encoder(__magic_name__  ,			training=__magic_name__    )
							a__:							Optional[int]									=original_model.text_encoder(__magic_name__  ,			training=__magic_name__    )

							a__:							List[Any]									=tf.nn.la_normalize(__magic_name__  ,			axis=-1    )
							a__:							Union[str, Any]									=tf.nn.la_normalize(__magic_name__  ,			axis=-1    )

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

							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__":
		__UpperCAmelCase    	=  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''')

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










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





