


'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

			return name



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						A_     :						Tuple     =					orig_state_dict.pop(__SCREAMING_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
									A_     :						List[Any]     =					key.split("""."""   )
									A_     :						Union[str, Any]     =					int(key_split[2]   ), int(key_split[4]   )
									A_     :						Dict     =					config.vision_config.hidden_size
									if "weight" in key:
												A_     :						Union[str, Any]     =					val[:dim, :]
												A_     :						Tuple     =					val[dim : dim * 2, :]
												A_     :						Union[str, Any]     =					val[-dim:, :]
									else:
												A_     :						Union[str, Any]     =					val[:dim]
												A_     :						str     =					val[dim : dim * 2]
												A_     :						Optional[int]     =					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_     :						str     =					key.split("""."""   )
									A_     :						Optional[Any]     =					int(key_split[3]   )
									A_     :						str     =					config.text_config.hidden_size
									if "weight" in key:
												A_     :						Optional[Any]     =					val[:dim, :]
												A_     :						Dict     =					val[
												    dim : dim * 2, :
												]
												A_     :						Optional[int]     =					val[-dim:, :]
									else:
												A_     :						Optional[int]     =					val[:dim]
												A_     :						Optional[Any]     =					val[dim : dim * 2]
												A_     :						Tuple     =					val[-dim:]
						else:
									A_     :						List[Any]     =					rename_key(__SCREAMING_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
									):
												A_     :						int     =					val.squeeze_()
									else:
												A_     :						List[Any]     =					val

			return orig_state_dict



def      a      (			):
			'''simple docstring'''





			A_     :						Tuple     =					"http://images.cocodataset.org/val2017/000000039769.jpg"
			A_     :						Union[str, Any]     =					Image.open(requests.get(__SCREAMING_SNAKE_CASE						,				stream=__SCREAMING_SNAKE_CASE   ).raw   )
			return im



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__="groupvit-gcc-yfcc"						,				lowerCamelCase__=False   ):
			'''simple docstring'''





			A_     :						Optional[Any]     =					GroupViTConfig()
			A_     :						List[str]     =					GroupViTModel(__SCREAMING_SNAKE_CASE   ).eval()

			A_     :						int     =					torch.load(__SCREAMING_SNAKE_CASE						,				map_location="""cpu"""   )["model"]
			A_     :						Optional[int]     =					convert_state_dict(__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE   )
			A_     :						Any     =					model.load_state_dict(__SCREAMING_SNAKE_CASE						,				strict=__SCREAMING_SNAKE_CASE   )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__SCREAMING_SNAKE_CASE   ) == 0)

			# verify result
			A_     :						List[Any]     =					CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""   )
			A_     :						Union[str, Any]     =					prepare_img()
			A_     :						Dict     =					processor(text=["""a photo of a cat""", """a photo of a dog"""]						,				images=__SCREAMING_SNAKE_CASE						,				padding=__SCREAMING_SNAKE_CASE						,				return_tensors="""pt"""   )

			with torch.no_grad():
						A_     :						Dict     =					model(**__SCREAMING_SNAKE_CASE   )

			if model_name == "groupvit-gcc-yfcc":
						A_     :						Any     =					torch.tensor([[13.3_523, 6.3_629]]   )
			elif model_name == "groupvit-gcc-redcaps":
						A_     :						List[str]     =					torch.tensor([[16.1_873, 8.6_230]]   )
			else:
						raise ValueError(f'Model name {model_name} not supported.'   )
			assert torch.allclose(outputs.logits_per_image						,				__SCREAMING_SNAKE_CASE						,				atol=1E-3   )

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

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


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

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




'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()