





"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 					A__				(			UpperCamelCase       ):
							# vision encoder
							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__				(			UpperCamelCase					,  UpperCamelCase       ):
							for key in orig_state_dict.copy().keys():
														A		=  orig_state_dict.pop(UpperCamelCase       )

														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(UpperCamelCase       )
																					# 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__				(			):
							A		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
							A		=  Image.open(requests.get(UpperCamelCase					,  stream=UpperCamelCase       ).raw       )
							return im






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase="groupvit-gcc-yfcc"					,  UpperCamelCase=False       ):
							A		=  GroupViTConfig()
							A		=  GroupViTModel(UpperCamelCase       ).eval()

							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )["model"]
							A		=  convert_state_dict(UpperCamelCase					,  UpperCamelCase       )
							A,	A		=  model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							assert missing_keys == ["text_model.embeddings.position_ids"]
							assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase       ) == 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=UpperCamelCase					,  padding=UpperCamelCase					,  return_tensors="pt"       )

							with torch.no_grad():
														A		=  model(**UpperCamelCase       )

							if model_name == "groupvit-gcc-yfcc":
														A		=  torch.tensor([[13.35_23, 6.36_29]]       )
							elif model_name == "groupvit-gcc-redcaps":
														A		=  torch.tensor([[16.18_73, 8.62_30]]       )
							else:
														raise ValueError(F"Model name {model_name} not supported."       )
							assert torch.allclose(outputs.logits_per_image					,  UpperCamelCase					,  atol=1E-3       )

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

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


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= 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     :		Optional[Any]       						= parser.parse_args()

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









"""simple docstring"""
_snake_case     :		Optional[int]       						= [
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager


