


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Any:




						'''simple docstring'''







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

						return name




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







						for key in orig_state_dict.copy().keys():
												__UpperCAmelCase			:							Optional[Any]	    =	orig_state_dict.pop(lowercase_      )

												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			:							List[Any]	    =	key.split('''.'''      )
																		__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	int(key_split[2]      ), int(key_split[4]      )
																		__UpperCAmelCase			:							Tuple	    =	config.vision_config.hidden_size
																		if "weight" in key:
																								__UpperCAmelCase			:							Optional[Any]	    =	val[:dim, :]
																								__UpperCAmelCase			:							List[Any]	    =	val[dim : dim * 2, :]
																								__UpperCAmelCase			:							Dict	    =	val[-dim:, :]
																		else:
																								__UpperCAmelCase			:							Optional[Any]	    =	val[:dim]
																								__UpperCAmelCase			:							str	    =	val[dim : dim * 2]
																								__UpperCAmelCase			:							Tuple	    =	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			:							List[str]	    =	key.split('''.'''      )
																		__UpperCAmelCase			:							List[Any]	    =	int(key_split[3]      )
																		__UpperCAmelCase			:							str	    =	config.text_config.hidden_size
																		if "weight" in key:
																								__UpperCAmelCase			:							Optional[int]	    =	val[:dim, :]
																								__UpperCAmelCase			:							Tuple	    =	val[
																								    dim : dim * 2, :
																								]
																								__UpperCAmelCase			:							Dict	    =	val[-dim:, :]
																		else:
																								__UpperCAmelCase			:							Union[str, Any]	    =	val[:dim]
																								__UpperCAmelCase			:							Tuple	    =	val[dim : dim * 2]
																								__UpperCAmelCase			:							List[str]	    =	val[-dim:]
												else:
																		__UpperCAmelCase			:							int	    =	rename_key(lowercase_      )
																		# 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			:							Optional[int]	    =	val.squeeze_()
																		else:
																								__UpperCAmelCase			:							Dict	    =	val

						return orig_state_dict




def     __SCREAMING_SNAKE_CASE							(       )		->     List[str]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCAmelCase			:							Union[str, Any]	    =	Image.open(requests.get(lowercase_      ,       stream=lowercase_      ).raw      )
						return im




@torch.no_grad()
def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_="groupvit-gcc-yfcc"      ,       lowercase_=False      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	GroupViTConfig()
						__UpperCAmelCase			:							Dict	    =	GroupViTModel(lowercase_      ).eval()

						__UpperCAmelCase			:							Any	    =	torch.load(lowercase_      ,       map_location='''cpu'''      )['''model''']
						__UpperCAmelCase			:							List[Any]	    =	convert_state_dict(lowercase_      ,       lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	model.load_state_dict(lowercase_      ,       strict=lowercase_      )
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_      ) == 0)

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

						with torch.no_grad():
												__UpperCAmelCase			:							int	    =	model(**lowercase_      )

						if model_name == "groupvit-gcc-yfcc":
												__UpperCAmelCase			:							List[Any]	    =	torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]      )
						elif model_name == "groupvit-gcc-redcaps":
												__UpperCAmelCase			:							str	    =	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      ,       lowercase_      ,       atol=1e-3      )

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

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


if __name__ == "__main__":
				lowerCAmelCase       		=    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       		=    parser.parse_args()

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





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

