




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()
UpperCAmelCase__						:       str							=    logging.get_logger(__name__)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: int						     =    tf.image.resize(UpperCamelCase					,     (346, 346)							)
  _lowercase: Dict						     =    tf.image.crop_to_bounding_box(UpperCamelCase					,     (346 - 289) // 2					,     (346 - 289) // 2					,     289					,     289							)
  return image


def 		_lowerCamelCase				(					)			->		List[Any]:





  """simple docstring"""





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

  _lowercase: str						     =    BertConfig()
  _lowercase: List[str]						     =    AlignConfig.from_text_vision_configs(
      text_config=UpperCamelCase					,     vision_config=UpperCamelCase					,     projection_dim=640							)
  return config


def 		_lowerCamelCase				(					)			->		List[str]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: List[Any]						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return im


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Union[str, Any]						     =    EfficientNetImageProcessor(
      do_center_crop=UpperCamelCase					,     rescale_factor=1 / 127.5					,     rescale_offset=UpperCamelCase					,     do_normalize=UpperCamelCase					,     include_top=UpperCamelCase					,     resample=Image.BILINEAR					,     )
  _lowercase: List[Any]						     =    BertTokenizer.from_pretrained('bert-base-uncased'							)
  _lowercase: Any						     =    64
  _lowercase: Dict						     =    AlignProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)
  return processor


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  _lowercase: int						     =    [v.split('_'							)[0].split('block'							)[1] for v in original_param_names if v.startswith('block'							)]
  _lowercase: Dict						     =    list(set(UpperCamelCase							)							)
  _lowercase: Tuple						     =    sorted(UpperCamelCase							)
  _lowercase: Dict						     =    len(UpperCamelCase							)
  _lowercase: List[str]						     =    {b: str(UpperCamelCase							) for b, i in zip(UpperCamelCase					,     range(UpperCamelCase							)							)}

  _lowercase: 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:
    _lowercase: 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')							)

  _lowercase: List[str]						     =    {}
  for item in rename_keys:
    if item[0] in original_param_names:
      _lowercase: Dict						     =    'vision_model.' + item[1]

    # BERT text encoder
  _lowercase: Tuple						     =    []
  _lowercase: Optional[int]						     =    'tf_bert_model/bert'
  _lowercase: List[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:
      _lowercase: Dict						     =    item[1]
  return key_mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Any:





  """simple docstring"""





  list(hf_params.keys()							)

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

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

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


@torch.no_grad()
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: Dict						     =    64
  _lowercase: Optional[int]						     =    Tokenizer(UpperCamelCase							)
  _lowercase: List[str]						     =    align.Align('efficientnet-b7'					,     'bert-base'					,     640					,     UpperCamelCase					,     tok.get_vocab_size()							)
  original_model.compile()
  original_model.load_weights(UpperCamelCase							)

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

  # Load HuggingFace model
  _lowercase: Union[str, Any]						     =    get_align_config()
  _lowercase: Optional[int]						     =    AlignModel(UpperCamelCase							).eval()
  _lowercase: Optional[Any]						     =    hf_model.state_dict()

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

  # Initialize processor
  _lowercase: int						     =    get_processor()
  _lowercase: 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():
    _lowercase: Union[str, Any]						     =    hf_model(**UpperCamelCase							)

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

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

  _lowercase: Tuple						     =    original_model.image_encoder(UpperCamelCase					,     training=UpperCamelCase							)
  _lowercase: str						     =    original_model.text_encoder(UpperCamelCase					,     training=UpperCamelCase							)

  _lowercase: Dict						     =    tf.nn.la_normalize(UpperCamelCase					,     axis=-1							)
  _lowercase: str						     =    tf.nn.la_normalize(UpperCamelCase					,     axis=-1							)

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

  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__":
   UpperCAmelCase__						:       str							=    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')

   UpperCAmelCase__						:       Dict							=    parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

  _lowercase: Union[str, Any]						     =    True if 'refined' in model_name else False
  _lowercase: str						     =    16 if 'rd16' in model_name else 64

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  if "clip_model" in name:
    _lowercase: Tuple						     =    name.replace('clip_model'					,     'clip'							)
  if "transformer" in name:
    if "visual" in name:
      _lowercase: Union[str, Any]						     =    name.replace('visual.transformer'					,     'vision_model'							)
    else:
      _lowercase: Optional[int]						     =    name.replace('transformer'					,     'text_model'							)
  if "resblocks" in name:
    _lowercase: List[Any]						     =    name.replace('resblocks'					,     'encoder.layers'							)
  if "ln_1" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_1'					,     'layer_norm1'							)
  if "ln_2" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_2'					,     'layer_norm2'							)
  if "c_fc" in name:
    _lowercase: str						     =    name.replace('c_fc'					,     'fc1'							)
  if "c_proj" in name:
    _lowercase: List[Any]						     =    name.replace('c_proj'					,     'fc2'							)
  if "attn" in name and "self" not in name:
    _lowercase: Dict						     =    name.replace('attn'					,     'self_attn'							)
  # text encoder
  if "token_embedding" in name:
    _lowercase: Optional[int]						     =    name.replace('token_embedding'					,     'text_model.embeddings.token_embedding'							)
  if "positional_embedding" in name and "visual" not in name:
    _lowercase: int						     =    name.replace('positional_embedding'					,     'text_model.embeddings.position_embedding.weight'							)
  if "ln_final" in name:
    _lowercase: Optional[Any]						     =    name.replace('ln_final'					,     'text_model.final_layer_norm'							)
  # vision encoder
  if "visual.class_embedding" in name:
    _lowercase: Union[str, Any]						     =    name.replace('visual.class_embedding'					,     'vision_model.embeddings.class_embedding'							)
  if "visual.conv1" in name:
    _lowercase: int						     =    name.replace('visual.conv1'					,     'vision_model.embeddings.patch_embedding'							)
  if "visual.positional_embedding" in name:
    _lowercase: List[Any]						     =    name.replace('visual.positional_embedding'					,     'vision_model.embeddings.position_embedding.weight'							)
  if "visual.ln_pre" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_pre'					,     'vision_model.pre_layrnorm'							)
  if "visual.ln_post" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_post'					,     'vision_model.post_layernorm'							)
  # projection layers
  if "visual.proj" in name:
    _lowercase: List[str]						     =    name.replace('visual.proj'					,     'visual_projection.weight'							)
  if "text_projection" in name:
    _lowercase: Any						     =    name.replace('text_projection'					,     'text_projection.weight'							)
  # decoder
  if "trans_conv" in name:
    _lowercase: List[Any]						     =    name.replace('trans_conv'					,     'transposed_convolution'							)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    _lowercase: Any						     =    'decoder.' + name
  if "blocks" in name:
    _lowercase: Optional[int]						     =    name.replace('blocks'					,     'decoder.layers'							)
  if "linear1" in name:
    _lowercase: Optional[Any]						     =    name.replace('linear1'					,     'mlp.fc1'							)
  if "linear2" in name:
    _lowercase: int						     =    name.replace('linear2'					,     'mlp.fc2'							)
  if "norm1" in name and "layer_" not in name:
    _lowercase: Optional[int]						     =    name.replace('norm1'					,     'layer_norm1'							)
  if "norm2" in name and "layer_" not in name:
    _lowercase: Tuple						     =    name.replace('norm2'					,     'layer_norm2'							)

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    _lowercase: int						     =    orig_state_dict.pop(UpperCamelCase							)

    if key.startswith('clip_model'							) and "attn.in_proj" in key:
      _lowercase: Union[str, Any]						     =    key.split('.'							)
      if "visual" in key:
        _lowercase: List[str]						     =    int(key_split[4]							)
        _lowercase: Dict						     =    config.vision_config.hidden_size
        _lowercase: Union[str, Any]						     =    'vision_model'
      else:
        _lowercase: str						     =    int(key_split[3]							)
        _lowercase: Optional[Any]						     =    config.text_config.hidden_size
        _lowercase: List[str]						     =    'text_model'

      if "weight" in key:
        _lowercase: Optional[int]						     =    val[:dim, :]
        _lowercase: List[Any]						     =    val[
            dim : dim * 2, :
        ]
        _lowercase: Tuple						     =    val[-dim:, :]
      else:
        _lowercase: Any						     =    val[:dim]
        _lowercase: List[str]						     =    val[dim : dim * 2]
        _lowercase: Optional[int]						     =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _lowercase: str						     =    key.split('.'							)
      _lowercase: Union[str, Any]						     =    int(key_split[1]							)
      _lowercase: List[str]						     =    config.reduce_dim
      if "weight" in key:
        _lowercase: Union[str, Any]						     =    val[:dim, :]
        _lowercase: Tuple						     =    val[dim : dim * 2, :]
        _lowercase: Union[str, Any]						     =    val[-dim:, :]
      else:
        _lowercase: Union[str, Any]						     =    val[:dim]
        _lowercase: Dict						     =    val[dim : dim * 2]
        _lowercase: Any						     =    val[-dim:]
    else:
      _lowercase: Dict						     =    rename_key(UpperCamelCase							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _lowercase: str						     =    val.T
      _lowercase: Tuple						     =    val

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith('model'							):
      state_dict.pop(UpperCamelCase					,     UpperCamelCase							)

    # rename some keys
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase							)							)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(F'Unexpected keys: {unexpected_keys}'							)

  _lowercase: Tuple						     =    ViTImageProcessor(size=352							)
  _lowercase: Union[str, Any]						     =    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
  _lowercase: Optional[Any]						     =    CLIPSegProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    prepare_img()
  _lowercase: List[Any]						     =    ['a glass', 'something to fill', 'wood', 'a jar']

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  else:
    raise ValueError(F'Model name {model_name} not supported.'							)

  assert torch.allclose(outputs.logits[0, :3, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.pooled_output[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  print('Looks ok!'							)

  if pytorch_dump_folder_path is not None:
    print(F'Saving model and processor to {pytorch_dump_folder_path}'							)
    model.save_pretrained(UpperCamelCase							)
    processor.save_pretrained(UpperCamelCase							)

  if push_to_hub:
    print(F'Pushing model and processor for {model_name} to the hub'							)
    model.push_to_hub(F'CIDAS/{model_name}'							)
    processor.push_to_hub(F'CIDAS/{model_name}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       int							=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='clipseg-rd64',
       type=str,
       choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
       help=(
           'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
           ' reduce dimension)'
       ),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
       type=str,
       help=(
           'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
           ' the decoder weights.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   UpperCAmelCase__						:       str							=    parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
