






"""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_			: List[str]				=			logging.get_logger(__name__)






def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			List[Any]     				=						tf.image.resize(_lowerCamelCase			,   (346, 346)				)
 lowerCamelCase__     :			Union[str, Any]     				=						tf.image.crop_to_bounding_box(_lowerCamelCase			,   (346 - 289) // 2			,   (346 - 289) // 2			,   289			,   289				)
 return image






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

 lowerCamelCase__     :			int     				=						BertConfig()
 lowerCamelCase__     :			List[Any]     				=						AlignConfig.from_text_vision_configs(
     text_config=_lowerCamelCase			,   vision_config=_lowerCamelCase			,   projection_dim=640				)
 return config






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






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






def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			List[Any]     				=						[v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
 lowerCamelCase__     :			Union[str, Any]     				=						list(set(_lowerCamelCase				)				)
 lowerCamelCase__     :			Optional[Any]     				=						sorted(_lowerCamelCase				)
 lowerCamelCase__     :			List[Any]     				=						len(_lowerCamelCase				)
 lowerCamelCase__     :			Optional[int]     				=						{b: str(_lowerCamelCase				) for b, i in zip(_lowerCamelCase			,   range(_lowerCamelCase				)				)}

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

    # BERT text encoder
 lowerCamelCase__     :			List[str]     				=						[]
 lowerCamelCase__     :			Dict     				=						'''tf_bert_model/bert'''
 lowerCamelCase__     :			Any     				=						'''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__     :			Dict     				=						item[1]
 return key_mapping






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

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






@torch.no_grad()
def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[int]     				=						64
 lowerCamelCase__     :			Any     				=						Tokenizer(_lowerCamelCase				)
 lowerCamelCase__     :			List[str]     				=						align.Align('efficientnet-b7'			,   'bert-base'			,   640			,   _lowerCamelCase			,   tok.get_vocab_size()				)
 original_model.compile()
 original_model.load_weights(_lowerCamelCase				)

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

 # Load HuggingFace model
 lowerCamelCase__     :			List[Any]     				=						get_align_config()
 lowerCamelCase__     :			List[str]     				=						AlignModel(_lowerCamelCase				).eval()
 lowerCamelCase__     :			int     				=						hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print('Converting parameters...'				)
 lowerCamelCase__     :			int     				=						rename_keys(_lowerCamelCase				)
 replace_params(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)

 # Initialize processor
 lowerCamelCase__     :			str     				=						get_processor()
 lowerCamelCase__     :			str     				=						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__     :			str     				=						hf_model(**_lowerCamelCase				)

 lowerCamelCase__     :			Any     				=						outputs.image_embeds.detach().numpy()
 lowerCamelCase__     :			Tuple     				=						outputs.text_embeds.detach().numpy()

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

 lowerCamelCase__     :			Tuple     				=						original_model.image_encoder(_lowerCamelCase			,   training=_lowerCamelCase				)
 lowerCamelCase__     :			str     				=						original_model.text_encoder(_lowerCamelCase			,   training=_lowerCamelCase				)

 lowerCamelCase__     :			Optional[Any]     				=						tf.nn.la_normalize(_lowerCamelCase			,   axis=-1				)
 lowerCamelCase__     :			Optional[Any]     				=						tf.nn.la_normalize(_lowerCamelCase			,   axis=-1				)

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

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







from typing import Any





class 		A__				:






	def __init__(	self						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							[]
						__magic_name__					:							str		   =							0






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




						"""simple docstring"""






						__magic_name__					:							str		   =							'''<''' + str(self.stack						)[1:-1] + '''>'''
						return printed






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						self.stack.append(lowerCamelCase						)
						__magic_name__					:							Tuple		   =							self.length + 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						self.rotate(1						)
						__magic_name__					:							Union[str, Any]		   =							self.stack[self.length - 1]
						__magic_name__					:							Union[str, Any]		   =							self.stack[:-1]
						self.rotate(self.length - 1						)
						__magic_name__					:							List[Any]		   =							self.length - 1
						return dequeued






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						for _ in range(lowerCamelCase						):
											__magic_name__					:							str		   =							self.stack[0]
											__magic_name__					:							Optional[Any]		   =							self.stack[1:]
											self.put(lowerCamelCase						)
											__magic_name__					:							List[Any]		   =							self.length - 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							self.get()
						self.put(lowerCamelCase						)
						self.rotate(self.length - 1						)
						return front






	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return self.length



