


"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     if "img_encoder.pos_embed" in name:
          _a    	=				name.replace('''img_encoder.pos_embed''',     '''vision_model.embeddings.position_embeddings'''       )
     if "img_encoder.patch_embed.proj" in name:
          _a    	=				name.replace('''img_encoder.patch_embed.proj''',     '''vision_model.embeddings.patch_embeddings.projection'''       )
     if "img_encoder.patch_embed.norm" in name:
          _a    	=				name.replace('''img_encoder.patch_embed.norm''',     '''vision_model.embeddings.layernorm'''       )
     if "img_encoder.layers" in name:
          _a    	=				name.replace('''img_encoder.layers''',     '''vision_model.encoder.stages'''       )
     if "blocks" in name and "res" not in name:
          _a    	=				name.replace('''blocks''',     '''layers'''       )
     if "attn" in name and "pre_assign" not in name:
          _a    	=				name.replace('''attn''',     '''self_attn'''       )
     if "proj" in name and "self_attn" in name and "text" not in name:
          _a    	=				name.replace('''proj''',     '''out_proj'''       )
     if "pre_assign_attn.attn.proj" in name:
          _a    	=				name.replace('''pre_assign_attn.attn.proj''',     '''pre_assign_attn.attn.out_proj'''       )
     if "norm1" in name:
          _a    	=				name.replace('''norm1''',     '''layer_norm1'''       )
     if "norm2" in name and "pre_assign" not in name:
          _a    	=				name.replace('''norm2''',     '''layer_norm2'''       )
     if "img_encoder.norm" in name:
          _a    	=				name.replace('''img_encoder.norm''',     '''vision_model.layernorm'''       )
     # text encoder
     if "text_encoder.token_embedding" in name:
          _a    	=				name.replace('''text_encoder.token_embedding''',     '''text_model.embeddings.token_embedding'''       )
     if "text_encoder.positional_embedding" in name:
          _a    	=				name.replace('''text_encoder.positional_embedding''',     '''text_model.embeddings.position_embedding.weight'''       )
     if "text_encoder.transformer.resblocks." in name:
          _a    	=				name.replace('''text_encoder.transformer.resblocks.''',     '''text_model.encoder.layers.'''       )
     if "ln_1" in name:
          _a    	=				name.replace('''ln_1''',     '''layer_norm1'''       )
     if "ln_2" in name:
          _a    	=				name.replace('''ln_2''',     '''layer_norm2'''       )
     if "c_fc" in name:
          _a    	=				name.replace('''c_fc''',     '''fc1'''       )
     if "c_proj" in name:
          _a    	=				name.replace('''c_proj''',     '''fc2'''       )
     if "text_encoder" in name:
          _a    	=				name.replace('''text_encoder''',     '''text_model'''       )
     if "ln_final" in name:
          _a    	=				name.replace('''ln_final''',     '''final_layer_norm'''       )
     # projection layers
     if "img_projector.linear_hidden." in name:
          _a    	=				name.replace('''img_projector.linear_hidden.''',     '''visual_projection.'''       )
     if "img_projector.linear_out." in name:
          _a    	=				name.replace('''img_projector.linear_out.''',     '''visual_projection.3.'''       )
     if "text_projector.linear_hidden" in name:
          _a    	=				name.replace('''text_projector.linear_hidden''',     '''text_projection'''       )
     if "text_projector.linear_out" in name:
          _a    	=				name.replace('''text_projector.linear_out''',     '''text_projection.3'''       )

     return name
def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _a    	=				orig_state_dict.pop(_lowerCAmelCase       )

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a    	=				key.split('''.'''       )
               _a	,      _a    	=				int(key_split[2]       ), int(key_split[4]       )
               _a    	=				config.vision_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[dim : dim * 2, :]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a    	=				key.split('''.'''       )
               _a    	=				int(key_split[3]       )
               _a    	=				config.text_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[
                        dim : dim * 2, :
                    ]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          else:
               _a    	=				rename_key(_lowerCAmelCase       )
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    _a    	=				val.squeeze_()
               else:
                    _a    	=				val

     return orig_state_dict
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict="groupvit-gcc-yfcc",     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				GroupViTConfig()
     _a    	=				GroupViTModel(_lowerCAmelCase       ).eval()

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     _a    	=				convert_state_dict(_lowerCAmelCase,     _lowerCAmelCase       )
     _a	,      _a    	=				model.load_state_dict(_lowerCAmelCase,     strict=_lowerCAmelCase       )
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase       ) == 0)

     # verify result
     _a    	=				CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
     _a    	=				prepare_img()
     _a    	=				processor(text=['''a photo of a cat''', '''a photo of a dog'''],     images=_lowerCAmelCase,     padding=_lowerCAmelCase,     return_tensors='''pt'''       )

     with torch.no_grad():
          _a    	=				model(**_lowerCAmelCase       )

     if model_name == "groupvit-gcc-yfcc":
          _a    	=				torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
     elif model_name == "groupvit-gcc-redcaps":
          _a    	=				torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]       )
     else:
          raise ValueError(f'Model name {model_name} not supported.'       )
     assert torch.allclose(outputs.logits_per_image,     _lowerCAmelCase,     atol=1e-3       )

     processor.save_pretrained(_lowerCAmelCase       )
     model.save_pretrained(_lowerCAmelCase       )
     print('''Successfully saved processor and model to''',     _lowerCAmelCase       )

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          processor.push_to_hub(_lowerCAmelCase,     organization='''nielsr'''       )
          model.push_to_hub(_lowerCAmelCase,     organization='''nielsr'''       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
   )
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
   parser.add_argument(
       '''--model_name''',
       default='''groupvit-gccy-fcc''',
       type=str,
       help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
       help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
   )
   __snake_case		=       parser.parse_args()

   convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 384}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _a    	=				do_resize
          _a    	=				size
          # Default value set here for backwards compatibility where the value in config is None
          _a    	=				crop_pct if crop_pct is not None else 224 / 256
          _a    	=				resample
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_normalize
          _a    	=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _a    	=				image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BICUBIC    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          if "shortest_edge" not in size:
               raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'							)
          _a    	=				size['''shortest_edge''']

          if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               _a    	=				int(shortest_edge / crop_pct							)
               _a    	=				get_resize_output_image_size(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
               _a    	=				resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=__UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   __UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							Optional[int]:
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							PIL.Image.Image:
          _a    	=				do_resize if do_resize is not None else self.do_resize
          _a    	=				crop_pct if crop_pct is not None else self.crop_pct
          _a    	=				resample if resample is not None else self.resample
          _a    	=				do_rescale if do_rescale is not None else self.do_rescale
          _a    	=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _a    	=				do_normalize if do_normalize is not None else self.do_normalize
          _a    	=				image_mean if image_mean is not None else self.image_mean
          _a    	=				image_std if image_std is not None else self.image_std

          _a    	=				size if size is not None else self.size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _a    	=				make_list_of_images(__UpperCAmelCase							)

          if not valid_images(__UpperCAmelCase							):
               raise ValueError(
                   '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                   '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

          if do_resize and size is None or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

          if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError('''crop_pct must be specified if size < 384.'''							)

          if do_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

          # All transformations expect numpy arrays.
          _a    	=				[to_numpy_array(__UpperCAmelCase							) for image in images]

          if do_resize:
               _a    	=				[self.resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				crop_pct=__UpperCAmelCase    ,				resample=__UpperCAmelCase							) for image in images]

          if do_rescale:
               _a    	=				[self.rescale(image=__UpperCAmelCase    ,				scale=__UpperCAmelCase							) for image in images]

          if do_normalize:
               _a    	=				[self.normalize(image=__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase							) for image in images]

          _a    	=				[to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase							) for image in images]

          _a    	=				{'''pixel_values''': images}
          return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase							)