


'''simple docstring'''




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()
__lowercase	:   Tuple   		=					logging.get_logger(__name__)





def 				a_							(    lowercase     )      ->Any:






				'''simple docstring'''

				__lowerCamelCase		:   Union[str, Any]           =       tf.image.resize(lowercase				,    (346, 346)     )
				__lowerCamelCase		:   Any           =       tf.image.crop_to_bounding_box(lowercase				,    (346 - 289) // 2				,    (346 - 289) // 2				,    289				,    289     )
				return image





def 				a_							(    )      ->Union[str, Any]:






				'''simple docstring'''

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

				__lowerCamelCase		:   Dict           =       BertConfig()
				__lowerCamelCase		:   Dict           =       AlignConfig.from_text_vision_configs(
				    text_config=lowercase				,    vision_config=lowercase				,    projection_dim=640     )
				return config





def 				a_							(    )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__lowerCamelCase		:   str           =       Image.open(requests.get(lowercase				,    stream=lowercase     ).raw     )
				return im





def 				a_							(    )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       EfficientNetImageProcessor(
				    do_center_crop=lowercase				,    rescale_factor=1 / 1_2_7.5				,    rescale_offset=lowercase				,    do_normalize=lowercase				,    include_top=lowercase				,    resample=Image.BILINEAR				,    )
				__lowerCamelCase		:   Optional[int]           =       BertTokenizer.from_pretrained("bert-base-uncased"     )
				__lowerCamelCase		:   str           =       64
				__lowerCamelCase		:   Dict           =       AlignProcessor(image_processor=lowercase				,    tokenizer=lowercase     )
				return processor





def 				a_							(    lowercase     )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   List[Any]           =       [v.split("_"     )[0].split("block"     )[1] for v in original_param_names if v.startswith("block"     )]
				__lowerCamelCase		:   Optional[int]           =       list(set(lowercase     )     )
				__lowerCamelCase		:   str           =       sorted(lowercase     )
				__lowerCamelCase		:   str           =       len(lowercase     )
				__lowerCamelCase		:   Tuple           =       {b: str(lowercase     ) for b, i in zip(lowercase				,    range(lowercase     )     )}

				__lowerCamelCase		:   int           =       []
				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:
								__lowerCamelCase		:   Optional[int]           =       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')     )

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

    # BERT text encoder
				__lowerCamelCase		:   int           =       []
				__lowerCamelCase		:   Optional[Any]           =       "tf_bert_model/bert"
				__lowerCamelCase		:   Tuple           =       "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:
												__lowerCamelCase		:   Tuple           =       item[1]
				return key_mapping





def 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->Any:






				'''simple docstring'''

				list(hf_params.keys()     )

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

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

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





@torch.no_grad()
def 				a_							(    lowercase				,    lowercase				,    lowercase				,    lowercase     )      ->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       64
				__lowerCamelCase		:   List[str]           =       Tokenizer(lowercase     )
				__lowerCamelCase		:   str           =       align.Align("efficientnet-b7"				,    "bert-base"				,    640				,    lowercase				,    tok.get_vocab_size()     )
				original_model.compile()
				original_model.load_weights(lowercase     )

				__lowerCamelCase		:   Dict           =       original_model.trainable_variables
				__lowerCamelCase		:   List[str]           =       original_model.non_trainable_variables
				__lowerCamelCase		:   Tuple           =       {param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								__lowerCamelCase		:   List[Any]           =       param.numpy()
				__lowerCamelCase		:   List[Any]           =       list(tf_params.keys()     )

				# Load HuggingFace model
				__lowerCamelCase		:   Optional[Any]           =       get_align_config()
				__lowerCamelCase		:   Dict           =       AlignModel(lowercase     ).eval()
				__lowerCamelCase		:   List[Any]           =       hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("Converting parameters..."     )
				__lowerCamelCase		:   List[Any]           =       rename_keys(lowercase     )
				replace_params(lowercase				,    lowercase				,    lowercase     )

				# Initialize processor
				__lowerCamelCase		:   List[str]           =       get_processor()
				__lowerCamelCase		:   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():
								__lowerCamelCase		:   List[Any]           =       hf_model(**lowercase     )

				__lowerCamelCase		:   List[Any]           =       outputs.image_embeds.detach().numpy()
				__lowerCamelCase		:   List[Any]           =       outputs.text_embeds.detach().numpy()

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

				__lowerCamelCase		:   List[str]           =       original_model.image_encoder(lowercase				,    training=lowercase     )
				__lowerCamelCase		:   Optional[int]           =       original_model.text_encoder(lowercase				,    training=lowercase     )

				__lowerCamelCase		:   List[str]           =       tf.nn.la_normalize(lowercase				,    axis=-1     )
				__lowerCamelCase		:   int           =       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__":
				__lowercase	:   List[Any]   		=					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""")

				__lowercase	:   Optional[int]   		=					parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





