

'''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()
_a			:	Optional[Any]								=  logging.get_logger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  UpperCAmelCase  							=		tf.image.resize(SCREAMING_SNAKE_CASE    ,				(346, 346)  )
  UpperCAmelCase  							=		tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE    ,				(346 - 289) // 2    ,				(346 - 289) // 2    ,				289    ,				289  )
  return image


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		EfficientNetConfig.from_pretrained('google/efficientnet-b7'  )
  UpperCAmelCase  							=		289
  UpperCAmelCase  							=		640
  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=SCREAMING_SNAKE_CASE    ,				vision_config=SCREAMING_SNAKE_CASE    ,				projection_dim=640  )
  return config


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCAmelCase  							=		Image.open(requests.get(SCREAMING_SNAKE_CASE    ,				stream=SCREAMING_SNAKE_CASE  ).raw  )
  return im


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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict  ):
  # EfficientNet image encoder
  UpperCAmelCase  							=		[v.split('_'  )[0].split('block'  )[1] for v in original_param_names if v.startswith('block'  )]
  UpperCAmelCase  							=		list(set(SCREAMING_SNAKE_CASE  )  )
  UpperCAmelCase  							=		sorted(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{b: str(SCREAMING_SNAKE_CASE  ) for b, i in zip(SCREAMING_SNAKE_CASE    ,				range(SCREAMING_SNAKE_CASE  )  )}

  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(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:
      UpperCAmelCase  							=		item[1]
  return key_mapping


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				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(SCREAMING_SNAKE_CASE  ).permute(3    ,				2    ,				0    ,				1  )
    elif "embeddings" in key:
      UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  )
    elif "depthwise_kernel" in key:
      UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  ).permute(2    ,				3    ,				0    ,				1  )
    elif "kernel" in key:
      UpperCAmelCase  							=		torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE  )  )
    elif "temperature" in key:
      UpperCAmelCase  							=		value
    elif "bn/gamma" or "bn/beta" in key:
      UpperCAmelCase  							=		torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE  )  ).squeeze()
    else:
      UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  )

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


@torch.no_grad()
def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  UpperCAmelCase  							=		64
  UpperCAmelCase  							=		Tokenizer(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		align.Align('efficientnet-b7'    ,				'bert-base'    ,				640    ,				SCREAMING_SNAKE_CASE    ,				tok.get_vocab_size()  )
  original_model.compile()
  original_model.load_weights(SCREAMING_SNAKE_CASE  )

  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(SCREAMING_SNAKE_CASE  ).eval()
  UpperCAmelCase  							=		hf_model.state_dict()

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

  # Initialize processor
  UpperCAmelCase  							=		get_processor()
  UpperCAmelCase  							=		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():
    UpperCAmelCase  							=		hf_model(**SCREAMING_SNAKE_CASE  )

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

  # Original model inference
  UpperCAmelCase  							=		False
  UpperCAmelCase  							=		EfficientNetImageProcessor(
      do_center_crop=SCREAMING_SNAKE_CASE    ,				do_rescale=SCREAMING_SNAKE_CASE    ,				do_normalize=SCREAMING_SNAKE_CASE    ,				include_top=SCREAMING_SNAKE_CASE    ,				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(SCREAMING_SNAKE_CASE    ,				training=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		original_model.text_encoder(SCREAMING_SNAKE_CASE    ,				training=SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		tf.nn.la_normalize(SCREAMING_SNAKE_CASE    ,				axis=-1  )
  UpperCAmelCase  							=		tf.nn.la_normalize(SCREAMING_SNAKE_CASE    ,				axis=-1  )

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

  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			:	Tuple								=  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			:	Any								=  parser.parse_args()
  convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


