

"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






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

		return name






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

		return orig_state_dict






def    a__   (   )     ->     Optional[int]:
		__lowerCAmelCase:	Tuple   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	Union[str, Any]   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)
		return im






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc" ,			__SCREAMING_SNAKE_CASE=False			)     ->     List[str]:
		__lowerCAmelCase:	Optional[Any]   				=      GroupViTConfig()
		__lowerCAmelCase:	List[str]   				=      GroupViTModel(__SCREAMING_SNAKE_CASE			).eval()

		__lowerCAmelCase:	int   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["model"]
		__lowerCAmelCase:	Optional[int]   				=      convert_state_dict(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase				,			__lowerCAmelCase:	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
		__lowerCAmelCase:	List[Any]   				=      CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"			)
		__lowerCAmelCase:	Union[str, Any]   				=      prepare_img()
		__lowerCAmelCase:	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():
				__lowerCAmelCase:	Dict   				=      model(**__SCREAMING_SNAKE_CASE			)

		if model_name == "groupvit-gcc-yfcc":
				__lowerCAmelCase:	Any   				=      torch.tensor([[13.3523, 6.3629]]			)
		elif model_name == "groupvit-gcc-redcaps":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([[16.1873, 8.6230]]			)
		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__":
			__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)





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


