






'''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     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					A	:							Union[str, Any]	)     ->			Optional[Any]:







   """simple docstring"""
   __snake_case					:   Dict  				=			tf.image.resize(__lowerCAmelCase	,		(3_46, 3_46)	)
   __snake_case					:   Optional[Any]  				=			tf.image.crop_to_bounding_box(__lowerCAmelCase	,		(3_46 - 2_89) // 2	,		(3_46 - 2_89) // 2	,		2_89	,		2_89	)
   return image




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""
   __snake_case					:   Dict  				=			EfficientNetConfig.from_pretrained('google/efficientnet-b7'	)
   __snake_case					:   Dict  				=			2_89
   __snake_case					:   List[str]  				=			6_40
   __snake_case					:   Dict  				=			{"0": "LABEL_0", "1": "LABEL_1"}
   __snake_case					:   List[Any]  				=			{"LABEL_0": 0, "LABEL_1": 1}
   __snake_case					:   Union[str, Any]  				=			[]

   __snake_case					:   Optional[int]  				=			BertConfig()
   __snake_case					:   List[Any]  				=			AlignConfig.from_text_vision_configs(
       text_config=__lowerCAmelCase	,		vision_config=__lowerCAmelCase	,		projection_dim=6_40	)
   return config




def 							_SCREAMING_SNAKE_CASE	(					)     ->			str:







   """simple docstring"""
   __snake_case					:   List[Any]  				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
   __snake_case					:   Dict  				=			Image.open(requests.get(__lowerCAmelCase	,		stream=__lowerCAmelCase	).raw	)
   return im




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			EfficientNetImageProcessor(
       do_center_crop=__lowerCAmelCase	,		rescale_factor=1 / 1_27.5	,		rescale_offset=__lowerCAmelCase	,		do_normalize=__lowerCAmelCase	,		include_top=__lowerCAmelCase	,		resample=Image.BILINEAR	,		)
   __snake_case					:   Optional[Any]  				=			BertTokenizer.from_pretrained('bert-base-uncased'	)
   __snake_case					:   Optional[int]  				=			64
   __snake_case					:   int  				=			AlignProcessor(image_processor=__lowerCAmelCase	,		tokenizer=__lowerCAmelCase	)
   return processor




def 							_SCREAMING_SNAKE_CASE	(					A	:							Union[str, Any]	)     ->			List[Any]:







   """simple docstring"""
   # EfficientNet image encoder
   __snake_case					:   List[str]  				=			[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
   __snake_case					:   str  				=			list(set(__lowerCAmelCase	)	)
   __snake_case					:   Dict  				=			sorted(__lowerCAmelCase	)
   __snake_case					:   Optional[int]  				=			len(__lowerCAmelCase	)
   __snake_case					:   Union[str, Any]  				=			{b: str(__lowerCAmelCase	) for b, i in zip(__lowerCAmelCase	,		range(__lowerCAmelCase	)	)}

   __snake_case					:   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:
      __snake_case					:   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""")	)

   __snake_case					:   Dict  				=			{}
   for item in rename_keys:
      if item[0] in original_param_names:
         __snake_case					:   List[Any]  				=			"vision_model." + item[1]

    # BERT text encoder
   __snake_case					:   Any  				=			[]
   __snake_case					:   Union[str, Any]  				=			"tf_bert_model/bert"
   __snake_case					:   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:
         __snake_case					:   int  				=			item[1]
   return key_mapping




def 							_SCREAMING_SNAKE_CASE	(					A	:							Any	,		A	:							Union[str, Any]	,		A	:							Optional[Any]	)     ->			Union[str, Any]:







   """simple docstring"""
   list(hf_params.keys()	)

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

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

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




@torch.no_grad()
def 							_SCREAMING_SNAKE_CASE	(					A	:							Tuple	,		A	:							int	,		A	:							str	,		A	:							Any	)     ->			Union[str, Any]:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			64
   __snake_case					:   Union[str, Any]  				=			Tokenizer(__lowerCAmelCase	)
   __snake_case					:   Optional[int]  				=			align.Align('efficientnet-b7'	,		'bert-base'	,		6_40	,		__lowerCAmelCase	,		tok.get_vocab_size()	)
   original_model.compile()
   original_model.load_weights(__lowerCAmelCase	)

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

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

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

   # Initialize processor
   __snake_case					:   List[str]  				=			get_processor()
   __snake_case					:   Optional[Any]  				=			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():
      __snake_case					:   Optional[Any]  				=			hf_model(**__lowerCAmelCase	)

   __snake_case					:   Union[str, Any]  				=			outputs.image_embeds.detach().numpy()
   __snake_case					:   List[str]  				=			outputs.text_embeds.detach().numpy()

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

   __snake_case					:   int  				=			original_model.image_encoder(__lowerCAmelCase	,		training=__lowerCAmelCase	)
   __snake_case					:   Any  				=			original_model.text_encoder(__lowerCAmelCase	,		training=__lowerCAmelCase	)

   __snake_case					:   Union[str, Any]  				=			tf.nn.la_normalize(__lowerCAmelCase	,		axis=-1	)
   __snake_case					:   Tuple  				=			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     						= 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     						= parser.parse_args()
      convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__     						=  logging.get_logger(__name__)

lowerCamelCase__     						=  {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__						):
			lowerCAmelCase			:						Optional[Any]							 =  "convnext"




			def __init__(		self	:  Optional[int]						,	lowerCamelCase__	:  List[str]=3						,	lowerCamelCase__	:  List[str]=4						,	lowerCamelCase__	:  Optional[int]=4						,	lowerCamelCase__	:  Dict=None						,	lowerCamelCase__	:  Tuple=None						,	lowerCamelCase__	:  List[str]="gelu"						,	lowerCamelCase__	:  List[Any]=0.0_2						,	lowerCamelCase__	:  Dict=1E-12						,	lowerCamelCase__	:  Tuple=1E-6						,	lowerCamelCase__	:  Dict=0.0						,	lowerCamelCase__	:  Optional[int]=2_24						,	lowerCamelCase__	:  List[Any]=None						,	lowerCamelCase__	:  Any=None						,	**lowerCamelCase__	:  int						,	) ->str:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[int]             =   num_channels
					_UpperCAmelCase		:							Tuple             =   patch_size
					_UpperCAmelCase		:							Dict             =   num_stages
					_UpperCAmelCase		:							Optional[int]             =   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
					_UpperCAmelCase		:							Dict             =   [3, 3, 9, 3] if depths is None else depths
					_UpperCAmelCase		:							Union[str, Any]             =   hidden_act
					_UpperCAmelCase		:							Tuple             =   initializer_range
					_UpperCAmelCase		:							Tuple             =   layer_norm_eps
					_UpperCAmelCase		:							Tuple             =   layer_scale_init_value
					_UpperCAmelCase		:							str             =   drop_path_rate
					_UpperCAmelCase		:							str             =   image_size
					_UpperCAmelCase		:							Tuple             =   ["stem"] + [F"""stage{idx}""" for idx in range(1						,	len(self.depths			) + 1			)]
					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   get_aligned_output_features_output_indices(
					    out_features=lowerCamelCase__						,	out_indices=lowerCamelCase__						,	stage_names=self.stage_names			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						str							 =  version.parse("1.11"						)




			@property
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Mapping[str, Mapping[int, str]]:



					'''simple docstring'''







					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]			)






			@property
			def 				lowerCAmelCase__     (		self	:  str			) ->float:



					'''simple docstring'''







					return 1E-5



