






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()
__snake_case     :     List[Any]       =  logging.get_logger(__name__)







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    tf.image.resize(UpperCamelCase_     ,							(346, 346)		)
						lowerCAmelCase__         =    tf.image.crop_to_bounding_box(UpperCamelCase_     ,							(346 - 289) // 2     ,							(346 - 289) // 2     ,							289     ,							289		)
						return image







def 						_UpperCamelCase (     )	-> Any:

						"""simple docstring"""



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

						lowerCAmelCase__         =    BertConfig()
						lowerCAmelCase__         =    AlignConfig.from_text_vision_configs(
						    text_config=UpperCamelCase_     ,							vision_config=UpperCamelCase_     ,							projection_dim=640		)
						return config







def 						_UpperCamelCase (     )	-> Tuple:

						"""simple docstring"""



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







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

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Tuple:

						"""simple docstring"""



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

						lowerCAmelCase__         =    []
						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__         =    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__         =    {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		lowerCAmelCase__         =    'vision_model.' + item[1]

    # BERT text encoder
						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    'tf_bert_model/bert'
						lowerCAmelCase__         =    '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__         =    item[1]
						return key_mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Optional[int]		)	-> Union[str, Any]:

						"""simple docstring"""



						list(hf_params.keys()		)

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

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

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







@torch.no_grad()
def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any     ,							UpperCamelCase_							:       Any     ,							UpperCamelCase_							:       Union[str, Any]		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    64
						lowerCAmelCase__         =    Tokenizer(UpperCamelCase_		)
						lowerCAmelCase__         =    align.Align('efficientnet-b7'     ,							'bert-base'     ,							640     ,							UpperCamelCase_     ,							tok.get_vocab_size()		)
						original_model.compile()
						original_model.load_weights(UpperCamelCase_		)

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

						# Load HuggingFace model
						lowerCAmelCase__         =    get_align_config()
						lowerCAmelCase__         =    AlignModel(UpperCamelCase_		).eval()
						lowerCAmelCase__         =    hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('Converting parameters...'		)
						lowerCAmelCase__         =    rename_keys(UpperCamelCase_		)
						replace_params(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

						# Initialize processor
						lowerCAmelCase__         =    get_processor()
						lowerCAmelCase__         =    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__         =    hf_model(**UpperCamelCase_		)

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

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

						lowerCAmelCase__         =    original_model.image_encoder(UpperCamelCase_     ,							training=UpperCamelCase_		)
						lowerCAmelCase__         =    original_model.text_encoder(UpperCamelCase_     ,							training=UpperCamelCase_		)

						lowerCAmelCase__         =    tf.nn.la_normalize(UpperCamelCase_     ,							axis=-1		)
						lowerCAmelCase__         =    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__":
					__snake_case     :     str       =  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""")

					__snake_case     :     int       =  parser.parse_args()
					convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.split(UpperCamelCase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)]







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__    ,							lowerCAmelCase__         =    [], []
						for title, text in zip(documents['title']     ,							documents['text']		):
												if text is not None:
																		for passage in split_text(UpperCamelCase_		):
																								titles.append(title if title is not None else ''		)
																								texts.append(UpperCamelCase_		)
						return {"title": titles, "text": texts}







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

						def embed(
						    UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       DPRContextEncoder     ,							UpperCamelCase_							:       DPRContextEncoderTokenizerFast     ,							UpperCamelCase_							:       Tuple		) -> dict:
												lowerCAmelCase__         =    ctx_tokenizer(
												    documents['title']     ,							documents['text']     ,							truncation=UpperCamelCase_     ,							padding='longest'     ,							return_tensors='pt'		)['input_ids']
												lowerCAmelCase__         =    ctx_encoder(input_ids.to(device=UpperCamelCase_		)     ,							return_dict=UpperCamelCase_		).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						lowerCAmelCase__         =    Features(
						    {'text': Value('string'		), 'title': Value('string'		), 'embeddings': Sequence(Value('float32'		)		)}		)  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





