





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def lowerCAmelCase__(__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







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

	return name





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__              =	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
			lowerCamelCase__              =	key.split('''.'''							)
			lowerCamelCase__       ,			lowerCamelCase__              =	int(key_split[2]							), int(key_split[4]							)
			lowerCamelCase__              =	config.vision_config.hidden_size
			if "weight" in key:
				lowerCamelCase__              =	val[:dim, :]
				lowerCamelCase__              =	val[dim : dim * 2, :]
				lowerCamelCase__              =	val[-dim:, :]
			else:
				lowerCamelCase__              =	val[:dim]
				lowerCamelCase__              =	val[dim : dim * 2]
				lowerCamelCase__              =	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
			lowerCamelCase__              =	key.split('''.'''							)
			lowerCamelCase__              =	int(key_split[3]							)
			lowerCamelCase__              =	config.text_config.hidden_size
			if "weight" in key:
				lowerCamelCase__              =	val[:dim, :]
				lowerCamelCase__              =	val[
				    dim : dim * 2, :
				]
				lowerCamelCase__              =	val[-dim:, :]
			else:
				lowerCamelCase__              =	val[:dim]
				lowerCamelCase__              =	val[dim : dim * 2]
				lowerCamelCase__              =	val[-dim:]
		else:
			lowerCamelCase__              =	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
			):
				lowerCamelCase__              =	val.squeeze_()
			else:
				lowerCamelCase__              =	val

	return orig_state_dict





def lowerCAmelCase__()       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCamelCase__              =	Image.open(requests.get(__snake_case ,stream=__snake_case							).raw							)
	return im





@torch.no_grad()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case="groupvit-gcc-yfcc" ,__snake_case=False							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	GroupViTConfig()
	lowerCamelCase__              =	GroupViTModel(__snake_case							).eval()

	lowerCamelCase__              =	torch.load(__snake_case ,map_location='''cpu'''							)['''model''']
	lowerCamelCase__              =	convert_state_dict(__snake_case ,__snake_case							)
	lowerCamelCase__       ,			lowerCamelCase__              =	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
	lowerCamelCase__              =	CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''							)
	lowerCamelCase__              =	prepare_img()
	lowerCamelCase__              =	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():
		lowerCamelCase__              =	model(**__snake_case							)

	if model_name == "groupvit-gcc-yfcc":
		lowerCamelCase__              =	torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
	elif model_name == "groupvit-gcc-redcaps":
		lowerCamelCase__              =	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 ,__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__":
			_a						=   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`.",
			)
			_a						=   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

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42






class       __A					(					lowerCAmelCase    ,       lowerCAmelCase				):

			'''simple docstring'''







			@register_to_config
			def __init__(					self ,						__lowerCAmelCase = 1_6 ,						__lowerCAmelCase = 8_8 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = 3_2 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = False ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "geglu" ,						__lowerCAmelCase = True ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__              =	num_attention_heads
				lowerCamelCase__              =	attention_head_dim
				lowerCamelCase__              =	num_attention_heads * attention_head_dim

				lowerCamelCase__              =	in_channels

				lowerCamelCase__              =	torch.nn.GroupNorm(num_groups=__lowerCAmelCase ,						num_channels=__lowerCAmelCase ,						eps=1E-6 ,						affine=__lowerCAmelCase							)
				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)

				# 3. Define transformers blocks
				lowerCamelCase__              =	nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						dropout=__lowerCAmelCase ,						cross_attention_dim=__lowerCAmelCase ,						activation_fn=__lowerCAmelCase ,						attention_bias=__lowerCAmelCase ,						double_self_attention=__lowerCAmelCase ,						norm_elementwise_affine=__lowerCAmelCase ,						)
				        for d in range(__lowerCAmelCase							)
				    ]							)

				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=None ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	hidden_states.shape
				lowerCamelCase__              =	batch_frames // num_frames

				lowerCamelCase__              =	hidden_states

				lowerCamelCase__              =	hidden_states[None, :].reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						2 ,						1 ,						3 ,						4							)

				lowerCamelCase__              =	self.norm(__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						3 ,						4 ,						2 ,						1							).reshape(batch_size * height * width ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	self.proj_in(__lowerCAmelCase							)

				# 2. Blocks
				for block in self.transformer_blocks:
					lowerCamelCase__              =	block(
					    __lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase ,						timestep=__lowerCAmelCase ,						cross_attention_kwargs=__lowerCAmelCase ,						class_labels=__lowerCAmelCase ,						)

				# 3. Output
				lowerCamelCase__              =	self.proj_out(__lowerCAmelCase							)
				lowerCamelCase__              =	(
				    hidden_states[None, None, :]
				    .reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				    .permute(0 ,						3 ,						4 ,						1 ,						2							)
				    .contiguous()
				)
				lowerCamelCase__              =	hidden_states.reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=__lowerCAmelCase							)



