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()
  A_			  =    logging.get_logger(__name__)
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =tf.image.resize(A    ,       (346, 346)			)
     UpperCamelCase_     =tf.image.crop_to_bounding_box(A    ,       (346 - 289) // 2    ,       (346 - 289) // 2    ,       289    ,       289			)
     return image
  
  
  
  
  def 						_UpperCamelCase			(  ):
     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=A    ,       vision_config=A    ,       projection_dim=640			)
     return config
  
  
  
  
  def 						_UpperCamelCase			(  ):
     UpperCamelCase_     ="http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase_     =Image.open(requests.get(A    ,       stream=A			).raw			)
     return im
  
  
  
  
  def 						_UpperCamelCase			(  ):
     UpperCamelCase_     =EfficientNetImageProcessor(
         do_center_crop=A    ,       rescale_factor=1 / 1_27.5    ,       rescale_offset=A    ,       do_normalize=A    ,       include_top=A    ,       resample=Image.BILINEAR    ,       )
     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-uncased"			)
     UpperCamelCase_     =64
     UpperCamelCase_     =AlignProcessor(image_processor=A    ,       tokenizer=A			)
     return processor
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     # EfficientNet image encoder
     UpperCamelCase_     =[v.split("_"			)[0].split("block"			)[1] for v in original_param_names if v.startswith("block"			)]
     UpperCamelCase_     =list(set(A			)			)
     UpperCamelCase_     =sorted(A			)
     UpperCamelCase_     =len(A			)
     UpperCamelCase_     ={b: str(A			) for b, i in zip(A    ,       range(A			)			)}
  
     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 						_UpperCamelCase			(  A    ,       A    ,       A			):
     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(A			).permute(3    ,       2    ,       0    ,       1			)
        elif "embeddings" in key:
           UpperCamelCase_     =torch.from_numpy(A			)
        elif "depthwise_kernel" in key:
           UpperCamelCase_     =torch.from_numpy(A			).permute(2    ,       3    ,       0    ,       1			)
        elif "kernel" in key:
           UpperCamelCase_     =torch.from_numpy(np.transpose(A			)			)
        elif "temperature" in key:
           UpperCamelCase_     =value
        elif "bn/gamma" or "bn/beta" in key:
           UpperCamelCase_     =torch.from_numpy(np.transpose(A			)			).squeeze()
        else:
           UpperCamelCase_     =torch.from_numpy(A			)
  
        # Replace HF parameters with original TF model parameters
        hf_params[hf_key].copy_(A			)
  
  
  
  
  @torch.no_grad()
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A			):
     UpperCamelCase_     =64
     UpperCamelCase_     =Tokenizer(A			)
     UpperCamelCase_     =align.Align("efficientnet-b7"    ,       "bert-base"    ,       640    ,       A    ,       tok.get_vocab_size()			)
     original_model.compile()
     original_model.load_weights(A			)
  
     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(A			).eval()
     UpperCamelCase_     =hf_model.state_dict()
  
     # Create src-to-dst parameter name mapping dictionary
     print("Converting parameters..."			)
     UpperCamelCase_     =rename_keys(A			)
     replace_params(A    ,       A    ,       A			)
  
     # 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(**A			)
  
     UpperCamelCase_     =outputs.image_embeds.detach().numpy()
     UpperCamelCase_     =outputs.text_embeds.detach().numpy()
  
     # Original model inference
     UpperCamelCase_     =False
     UpperCamelCase_     =EfficientNetImageProcessor(
         do_center_crop=A    ,       do_rescale=A    ,       do_normalize=A    ,       include_top=A    ,       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(A    ,       training=A			)
     UpperCamelCase_     =original_model.text_encoder(A    ,       training=A			)
  
     UpperCamelCase_     =tf.nn.la_normalize(A    ,       axis=-1			)
     UpperCamelCase_     =tf.nn.la_normalize(A    ,       axis=-1			)
  
     # Check whether original and HF model outputs match  -> np.allclose
     if not np.allclose(A    ,       A    ,       atol=1e-3			):
        raise ValueError("The predicted image features are not the same."			)
     if not np.allclose(A    ,       A    ,       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(A			):
           os.mkdir(A			)
        # Save converted model and image processor
        hf_model.save_pretrained(A			)
        processor.save_pretrained(A			)
  
     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)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
    __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
    __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
       UpperCamelCase_     =None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."				,			UpperCamelCase_				,			)
          UpperCamelCase_     =kwargs.pop("feature_extractor"					)
  
       UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."					)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."					)
  
       super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
       UpperCamelCase_     =self.image_processor
    def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):
  
       if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."					)
  
       if text is not None:
          UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if videos is not None:
          UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if text is not None and videos is not None:
          UpperCamelCase_     =image_features.pixel_values
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
       return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
       return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
    @property
    def 					UpperCamelCase__   (      self:		str					):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
       return self.image_processor_class
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
       return self.image_processor
  