'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]     )       -> List[Any]:


     '''simple docstring'''




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

     return name





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					List[str]     )       -> int:


     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_			       =  orig_state_dict.pop(snake_case_     )

          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
               UpperCAmelCase_			       =  key.split("."     )
               UpperCAmelCase_       ,					UpperCAmelCase_			       =  int(key_split[2]     ), int(key_split[4]     )
               UpperCAmelCase_			       =  config.vision_config.hidden_size
               if "weight" in key:
                    UpperCAmelCase_			       =  val[:dim, :]
                    UpperCAmelCase_			       =  val[dim : dim * 2, :]
                    UpperCAmelCase_			       =  val[-dim:, :]
               else:
                    UpperCAmelCase_			       =  val[:dim]
                    UpperCAmelCase_			       =  val[dim : dim * 2]
                    UpperCAmelCase_			       =  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
               UpperCAmelCase_			       =  key.split("."     )
               UpperCAmelCase_			       =  int(key_split[3]     )
               UpperCAmelCase_			       =  config.text_config.hidden_size
               if "weight" in key:
                    UpperCAmelCase_			       =  val[:dim, :]
                    UpperCAmelCase_			       =  val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_			       =  val[-dim:, :]
               else:
                    UpperCAmelCase_			       =  val[:dim]
                    UpperCAmelCase_			       =  val[dim : dim * 2]
                    UpperCAmelCase_			       =  val[-dim:]
          else:
               UpperCAmelCase_			       =  rename_key(snake_case_     )
               # 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
               ):
                    UpperCAmelCase_			       =  val.squeeze_()
               else:
                    UpperCAmelCase_			       =  val

     return orig_state_dict





def       lowerCAmelCase_							(   )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_			       =  Image.open(requests.get(snake_case_   ,       stream=snake_case_     ).raw     )
     return im





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					Tuple   ,       snake_case_			:					Any   ,       snake_case_			:					Dict="groupvit-gcc-yfcc"   ,       snake_case_			:					Any=False     )       -> Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  GroupViTConfig()
     UpperCAmelCase_			       =  GroupViTModel(snake_case_     ).eval()

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

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

     with torch.no_grad():
          UpperCAmelCase_			       =  model(**snake_case_     )

     if model_name == "groupvit-gcc-yfcc":
          UpperCAmelCase_			       =  torch.tensor([[13.3523, 6.3629]]     )
     elif model_name == "groupvit-gcc-redcaps":
          UpperCAmelCase_			       =  torch.tensor([[16.1873, 8.6230]]     )
     else:
          raise ValueError(f"""Model name {model_name} not supported."""     )
     assert torch.allclose(outputs.logits_per_image   ,       snake_case_   ,       atol=1E-3     )

     processor.save_pretrained(snake_case_     )
     model.save_pretrained(snake_case_     )
     print("Successfully saved processor and model to"   ,       snake_case_     )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	Union[str, Any]    =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`.',
    )
    SCREAMING_SNAKE_CASE_:	Union[str, Any]    =parser.parse_args()

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








import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_="resnet50"       ,  lowercase_=3       ,  lowercase_=32       ,  lowercase_=3       ,  lowercase_=True       ,  lowercase_=True       ,  )  -> Union[str, Any]:
											a__      =parent
											a__      =out_indices if out_indices is not None else [4]
											a__      =stage_names
											a__      =out_features
											a__      =backbone
											a__      =batch_size
											a__      =image_size
											a__      =num_channels
											a__      =use_pretrained_backbone
											a__      =is_training





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
											a__      =self.get_config()

											return config, pixel_values





				def 						__UpperCamelCase    (				self)  -> Tuple:
											return TimmBackboneConfig(
											    image_size=self.image_size       ,  num_channels=self.num_channels       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  stage_names=self.stage_names       ,  use_pretrained_backbone=self.use_pretrained_backbone       ,  backbone=self.backbone       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =TimmBackbone(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											with torch.no_grad():
																		a__      =model(lowercase_)
											self.parent.assertEqual(
											    result.feature_map[-1].shape       ,  (self.batch_size, model.channels[-1], 14, 14)       ,  )





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
@require_timm
class       lowercase_					(lowercase__      ,					lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(TimmBackbone,) if is_torch_available() else ()
				snake_case	    ={'feature-extraction': TimmBackbone} if is_torch_available() else {}
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =TimmBackboneModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()





				def 						__UpperCamelCase    (				self)  -> str:
											a__      ='resnet18'
											a__      ='microsoft/resnet-18'

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_)
											a__      =AutoBackbone.from_pretrained(lowercase_)

											self.assertEqual(len(timm_model.out_features)       ,  len(transformers_model.out_features))
											self.assertEqual(len(timm_model.stage_names)       ,  len(transformers_model.stage_names))
											self.assertEqual(timm_model.channels       ,  transformers_model.channels)
											# Out indices are set to the last layer by default. For timm models, we don't know
											# the number of layers in advance, so we set it to (-1,), whereas for transformers
											# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
											self.assertEqual(timm_model.out_indices       ,  (-1,))
											self.assertEqual(transformers_model.out_indices       ,  [len(timm_model.stage_names) - 1])

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_       ,  out_indices=[1, 2, 3])
											a__      =AutoBackbone.from_pretrained(lowercase_       ,  out_indices=[1, 2, 3])

											self.assertEqual(timm_model.out_indices       ,  transformers_model.out_indices)
											self.assertEqual(len(timm_model.out_features)       ,  len(transformers_model.out_features))
											self.assertEqual(timm_model.channels       ,  transformers_model.channels)





				@unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
				def 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('TimmBackbone initialization is managed on the timm side')
				def 						__UpperCamelCase    (				self)  -> Any:
											pass





				@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> Any:
											pass





				@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											pass





				@unittest.skip('model weights aren\'t tied in TimmBackbone.')
				def 						__UpperCamelCase    (				self)  -> Dict:
											pass





				@unittest.skip('model weights aren\'t tied in TimmBackbone.')
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											pass





				@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
				def 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip('TimmBackbone doesn\'t support output_attentions.')
				def 						__UpperCamelCase    (				self)  -> str:
											pass





				@unittest.skip('Safetensors is not supported by timm.')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		a__      =inspect.signature(model.forward)
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		a__      =[*signature.parameters.keys()]

																		a__      =['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()
											a__      =True
											a__      =self.has_attentions

											# no need to test all models as different heads yield the same functionality
											a__      =self.all_model_classes[0]
											a__      =model_class(lowercase_)
											model.to(lowercase_)

											a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
											a__      =model(**lowercase_)
											a__      =outputs[0][-1]

											# Encoder-/Decoder-only models
											a__      =outputs.hidden_states[0]
											hidden_states.retain_grad()

											if self.has_attentions:
																		a__      =outputs.attentions[0]
																		attentions.retain_grad()

											output.flatten()[0].backward(retain_graph=lowercase_)

											self.assertIsNotNone(hidden_states.grad)

											if self.has_attentions:
																		self.assertIsNotNone(attentions.grad)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

																		self.assertEqual(len(result.feature_maps)       ,  len(config.out_indices))
																		self.assertEqual(len(model.channels)       ,  len(config.out_indices))

																		# Check output of last stage is taken if out_features=None, out_indices=None
																		a__      =copy.deepcopy(lowercase_)
																		a__      =None
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

																		self.assertEqual(len(result.feature_maps)       ,  1)
																		self.assertEqual(len(model.channels)       ,  1)

																		# Check backbone can be initialized with fresh weights
																		a__      =copy.deepcopy(lowercase_)
																		a__      =False
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

