code: 
  '''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__			=	logging.get_logger(__name__)
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= tf.image.resize(snake_case	,     (3_46, 3_46)   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= tf.image.crop_to_bounding_box(snake_case	,     (3_46 - 2_89) // 2	,     (3_46 - 2_89) // 2	,     2_89	,     2_89   )
     return image
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= 2_89
     SCREAMING_SNAKE_CASE__			:				Dict       						= 6_40
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
     SCREAMING_SNAKE_CASE__			:				Any       						= {'''LABEL_0''': 0, '''LABEL_1''': 1}
     SCREAMING_SNAKE_CASE__			:				int       						= []
  
     SCREAMING_SNAKE_CASE__			:				str       						= BertConfig()
     SCREAMING_SNAKE_CASE__			:				str       						= AlignConfig.from_text_vision_configs(
         text_config=snake_case	,     vision_config=snake_case	,     projection_dim=6_40   )
     return config
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Any       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(snake_case	,     stream=snake_case   ).raw   )
     return im
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= EfficientNetImageProcessor(
         do_center_crop=snake_case	,     rescale_factor=1 / 127.5	,     rescale_offset=snake_case	,     do_normalize=snake_case	,     include_top=snake_case	,     resample=Image.BILINEAR	,     )
     SCREAMING_SNAKE_CASE__			:				str       						= BertTokenizer.from_pretrained('''bert-base-uncased'''   )
     SCREAMING_SNAKE_CASE__			:				Dict       						= 64
     SCREAMING_SNAKE_CASE__			:				str       						= AlignProcessor(image_processor=snake_case	,     tokenizer=snake_case   )
     return processor
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [v.split('''_'''   )[0].split('''block'''   )[1] for v in original_param_names if v.startswith('''block'''   )]
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= list(set(snake_case   )   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= sorted(snake_case   )
     SCREAMING_SNAKE_CASE__			:				str       						= len(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= {b: str(snake_case   ) for b, i in zip(snake_case	,     range(snake_case   )   )}
  
     SCREAMING_SNAKE_CASE__			:				Union[str, 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:
        SCREAMING_SNAKE_CASE__			:				Tuple       						= 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""")   )
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= {}
     for item in rename_keys:
        if item[0] in original_param_names:
           SCREAMING_SNAKE_CASE__			:				Any       						= '''vision_model.''' + item[1]
  
      # BERT text encoder
     SCREAMING_SNAKE_CASE__			:				int       						= []
     SCREAMING_SNAKE_CASE__			:				str       						= '''tf_bert_model/bert'''
     SCREAMING_SNAKE_CASE__			:				Dict       						= '''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:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= item[1]
     return key_mapping
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     list(hf_params.keys()   )
  
     for key, value in tf_params.items():
        if key not in key_mapping:
           continue
  
        SCREAMING_SNAKE_CASE__			:				Tuple       						= key_mapping[key]
        if "_conv" in key and "kernel" in key:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= torch.from_numpy(snake_case   ).permute(3	,     2	,     0	,     1   )
        elif "embeddings" in key:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= torch.from_numpy(snake_case   )
        elif "depthwise_kernel" in key:
           SCREAMING_SNAKE_CASE__			:				Dict       						= torch.from_numpy(snake_case   ).permute(2	,     3	,     0	,     1   )
        elif "kernel" in key:
           SCREAMING_SNAKE_CASE__			:				int       						= torch.from_numpy(np.transpose(snake_case   )   )
        elif "temperature" in key:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= value
        elif "bn/gamma" or "bn/beta" in key:
           SCREAMING_SNAKE_CASE__			:				Dict       						= torch.from_numpy(np.transpose(snake_case   )   ).squeeze()
        else:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.from_numpy(snake_case   )
  
        # Replace HF parameters with original TF model parameters
        hf_params[hf_key].copy_(snake_case   )
  
  
  @torch.no_grad()
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[str]       						= 64
     SCREAMING_SNAKE_CASE__			:				int       						= Tokenizer(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= align.Align('''efficientnet-b7'''	,     '''bert-base'''	,     6_40	,     snake_case	,     tok.get_vocab_size()   )
     original_model.compile()
     original_model.load_weights(snake_case   )
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= original_model.trainable_variables
     SCREAMING_SNAKE_CASE__			:				Dict       						= original_model.non_trainable_variables
     SCREAMING_SNAKE_CASE__			:				str       						= {param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
        SCREAMING_SNAKE_CASE__			:				Tuple       						= param.numpy()
     SCREAMING_SNAKE_CASE__			:				Dict       						= list(tf_params.keys()   )
  
     # Load HuggingFace model
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= get_align_config()
     SCREAMING_SNAKE_CASE__			:				str       						= AlignModel(snake_case   ).eval()
     SCREAMING_SNAKE_CASE__			:				str       						= hf_model.state_dict()
  
     # Create src-to-dst parameter name mapping dictionary
     print('''Converting parameters...'''   )
     SCREAMING_SNAKE_CASE__			:				Any       						= rename_keys(snake_case   )
     replace_params(snake_case	,     snake_case	,     snake_case   )
  
     # Initialize processor
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= get_processor()
     SCREAMING_SNAKE_CASE__			:				int       						= 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():
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= hf_model(**snake_case   )
  
     SCREAMING_SNAKE_CASE__			:				str       						= outputs.image_embeds.detach().numpy()
     SCREAMING_SNAKE_CASE__			:				Tuple       						= outputs.text_embeds.detach().numpy()
  
     # Original model inference
     SCREAMING_SNAKE_CASE__			:				Any       						= False
     SCREAMING_SNAKE_CASE__			:				List[str]       						= EfficientNetImageProcessor(
         do_center_crop=snake_case	,     do_rescale=snake_case	,     do_normalize=snake_case	,     include_top=snake_case	,     resample=Image.BILINEAR	,     )
     SCREAMING_SNAKE_CASE__			:				Tuple       						= tf_image_processor(images=prepare_img()	,     return_tensors='''tf'''	,     data_format='''channels_last'''   )['''pixel_values''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= tok(tf.constant(['''A picture of a cat''']   )   )
  
     SCREAMING_SNAKE_CASE__			:				int       						= original_model.image_encoder(snake_case	,     training=snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= original_model.text_encoder(snake_case	,     training=snake_case   )
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= tf.nn.la_normalize(snake_case	,     axis=-1   )
     SCREAMING_SNAKE_CASE__			:				Any       						= tf.nn.la_normalize(snake_case	,     axis=-1   )
  
     # Check whether original and HF model outputs match  -> np.allclose
     if not np.allclose(snake_case	,     snake_case	,     atol=1e-3   ):
        raise ValueError('''The predicted image features are not the same.'''   )
     if not np.allclose(snake_case	,     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(snake_case   ):
           os.mkdir(snake_case   )
        # Save converted model and image processor
        hf_model.save_pretrained(snake_case   )
        processor.save_pretrained(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__":
         lowercase__			=	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__			=	parser.parse_args()
         convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  # rely on isort to merge the imports
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  lowercase__			=	{
      '''configuration_convnextv2''': [
          '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''ConvNextV2Config''',
      ]
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowercase__			=	[
             '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''ConvNextV2ForImageClassification''',
             '''ConvNextV2Model''',
             '''ConvNextV2PreTrainedModel''',
             '''ConvNextV2Backbone''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_convnextva import (
             CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             ConvNextVaConfig,
         )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_convnextva import (
                    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    ConvNextVaBackbone,
                    ConvNextVaForImageClassification,
                    ConvNextVaModel,
                    ConvNextVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  