
import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			lowerCAmelCase_						=	XCLIPTextConfig()

			# derive patch size from model name
			lowerCAmelCase_						=	model_name.find("patch"				)
			lowerCAmelCase_						=	int(model_name[start_idx + len("patch"				) : start_idx + len("patch"				) + 2]				)
			lowerCAmelCase_						=	XCLIPVisionConfig(patch_size=__lowerCAmelCase    ,							num_frames=__lowerCAmelCase				)

			if "large" in model_name:
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	3072
						lowerCAmelCase_						=	12

						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	16
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	3072

			if model_name == "xclip-large-patch14-16-frames":
						lowerCAmelCase_						=	336

			lowerCAmelCase_						=	XCLIPConfig.from_text_vision_configs(__lowerCAmelCase    ,							__lowerCAmelCase				)

			if "large" in model_name:
						lowerCAmelCase_						=	768

			return config






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			if name == "token_embedding.weight":
						lowerCAmelCase_						=	name.replace("token_embedding.weight"    ,							"text_model.embeddings.token_embedding.weight"				)
			if name == "positional_embedding":
						lowerCAmelCase_						=	name.replace("positional_embedding"    ,							"text_model.embeddings.position_embedding.weight"				)
			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 name.startswith("transformer.resblocks"				):
						lowerCAmelCase_						=	name.replace("transformer.resblocks"    ,							"text_model.encoder.layers"				)
			if "attn.out_proj" in name and "message" not in name:
						lowerCAmelCase_						=	name.replace("attn.out_proj"    ,							"self_attn.out_proj"				)
			if "ln_final" in name:
						lowerCAmelCase_						=	name.replace("ln_final"    ,							"text_model.final_layer_norm"				)
			# visual encoder
			if name == "visual.class_embedding":
						lowerCAmelCase_						=	name.replace("visual.class_embedding"    ,							"vision_model.embeddings.class_embedding"				)
			if name == "visual.positional_embedding":
						lowerCAmelCase_						=	name.replace("visual.positional_embedding"    ,							"vision_model.embeddings.position_embedding.weight"				)
			if name.startswith("visual.transformer.resblocks"				):
						lowerCAmelCase_						=	name.replace("visual.transformer.resblocks"    ,							"vision_model.encoder.layers"				)
			if "visual.conv1" in name:
						lowerCAmelCase_						=	name.replace("visual.conv1"    ,							"vision_model.embeddings.patch_embedding"				)
			if "visual.ln_pre" in name:
						lowerCAmelCase_						=	name.replace("visual.ln_pre"    ,							"vision_model.pre_layernorm"				)
			if "visual.ln_post" in name:
						lowerCAmelCase_						=	name.replace("visual.ln_post"    ,							"vision_model.post_layernorm"				)
			if "visual.proj" in name:
						lowerCAmelCase_						=	name.replace("visual.proj"    ,							"visual_projection.weight"				)
			if "text_projection" in name:
						lowerCAmelCase_						=	name.replace("text_projection"    ,							"text_projection.weight"				)
			# things on top
			if "prompts_visual_proj" in name:
						lowerCAmelCase_						=	name.replace("prompts_visual_proj"    ,							"prompts_visual_projection"				)
			if "prompts_visual_ln" in name:
						lowerCAmelCase_						=	name.replace("prompts_visual_ln"    ,							"prompts_visual_layernorm"				)
			# mit
			if name == "mit.positional_embedding":
						lowerCAmelCase_						=	name.replace("positional"    ,							"position"				)
			if name.startswith("mit.resblocks"				):
						lowerCAmelCase_						=	name.replace("mit.resblocks"    ,							"mit.encoder.layers"				)
			# prompts generator
			if name.startswith("prompts_generator.norm"				):
						lowerCAmelCase_						=	name.replace("prompts_generator.norm"    ,							"prompts_generator.layernorm"				)

			return name






def    lowerCamelCase__				(				__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_						=	orig_state_dict.pop(__lowerCAmelCase				)

						if "attn.in_proj" in key:
									lowerCAmelCase_						=	key.split("."				)
									if key.startswith("visual"				):
												lowerCAmelCase_						=	key_split[3]
												lowerCAmelCase_						=	config.vision_config.hidden_size
												if "message_attn" in key:
															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:
															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 key.startswith("mit"				):
												lowerCAmelCase_						=	key_split[2]
												lowerCAmelCase_						=	config.vision_config.mit_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_						=	key_split[2]
												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(__lowerCAmelCase				)
									if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
												lowerCAmelCase_						=	val.T
									lowerCAmelCase_						=	val

			return orig_state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			if num_frames == 8:
						lowerCAmelCase_						=	"eating_spaghetti_8_frames.npy"
			elif num_frames == 16:
						lowerCAmelCase_						=	"eating_spaghetti.npy"
			elif num_frames == 32:
						lowerCAmelCase_						=	"eating_spaghetti_32_frames.npy"
			lowerCAmelCase_						=	hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"    ,							filename=__lowerCAmelCase    ,							repo_type="dataset"    ,							)
			lowerCAmelCase_						=	np.load(__lowerCAmelCase				)
			return list(__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  List[Any]=None    ,							__lowerCAmelCase  :  Any=False				):




			"""simple docstring"""


			lowerCAmelCase_						=	{
			    # fully supervised kinetics-400 checkpoints
			    "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
			    "xclip-base-patch32-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
			    ),
			    "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
			    "xclip-base-patch16-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
			    ),
			    "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
			    "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
			    # fully supervised kinetics-600 checkpoints
			    "xclip-base-patch16-kinetics-600": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
			    ),
			    "xclip-base-patch16-kinetics-600-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
			    ),
			    "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
			    # few shot
			    "xclip-base-patch16-hmdb-2-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
			    ),
			    "xclip-base-patch16-hmdb-4-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
			    ),
			    "xclip-base-patch16-hmdb-8-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
			    ),
			    "xclip-base-patch16-hmdb-16-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
			    ),
			    "xclip-base-patch16-ucf-2-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
			    ),
			    "xclip-base-patch16-ucf-4-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
			    ),
			    "xclip-base-patch16-ucf-8-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
			    ),
			    "xclip-base-patch16-ucf-16-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
			    ),
			    # zero shot
			    "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
			}

			lowerCAmelCase_						=	model_to_url[model_name]
			lowerCAmelCase_						=	8
			if "16-frames" in model_name:
						lowerCAmelCase_						=	16
			elif "shot" in model_name:
						lowerCAmelCase_						=	32

			lowerCAmelCase_						=	get_xclip_config(__lowerCAmelCase    ,							__lowerCAmelCase				)
			lowerCAmelCase_						=	XCLIPModel(__lowerCAmelCase				)
			model.eval()

			if "drive" in checkpoint_url:
						lowerCAmelCase_						=	"pytorch_model.bin"
						gdown.cached_download(__lowerCAmelCase    ,							__lowerCAmelCase    ,							quiet=__lowerCAmelCase				)
						lowerCAmelCase_						=	torch.load(__lowerCAmelCase    ,							map_location="cpu"				)["model"]
			else:
						lowerCAmelCase_						=	torch.hub.load_state_dict_from_url(__lowerCAmelCase				)["model"]

			lowerCAmelCase_						=	convert_state_dict(__lowerCAmelCase    ,							__lowerCAmelCase				)

			lowerCAmelCase_						=	XCLIPModel(__lowerCAmelCase				)
			lowerCAmelCase_	,  lowerCAmelCase_						=	model.load_state_dict(__lowerCAmelCase    ,							strict=__lowerCAmelCase				)
			assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
			model.eval()

			lowerCAmelCase_						=	336 if model_name == "xclip-large-patch14-16-frames" else 224
			lowerCAmelCase_						=	VideoMAEImageProcessor(size=__lowerCAmelCase				)
			lowerCAmelCase_						=	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"				)
			lowerCAmelCase_						=	CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"				)
			lowerCAmelCase_						=	XCLIPProcessor(image_processor=__lowerCAmelCase    ,							tokenizer=__lowerCAmelCase				)

			lowerCAmelCase_						=	prepare_video(__lowerCAmelCase				)
			lowerCAmelCase_						=	processor(
			    text=["playing sports", "eating spaghetti", "go shopping"]    ,							videos=__lowerCAmelCase    ,							return_tensors="pt"    ,							padding=__lowerCAmelCase				)

			print("Shape of pixel values:"    ,							inputs.pixel_values.shape				)

			with torch.no_grad():
						lowerCAmelCase_						=	model(**__lowerCAmelCase				)

			# Verify outputs
			lowerCAmelCase_						=	outputs.logits_per_video
			lowerCAmelCase_						=	logits_per_video.softmax(dim=1				)
			print("Probs:"    ,							__lowerCAmelCase				)
			# kinetics-400
			if model_name == "xclip-base-patch32":
						lowerCAmelCase_						=	torch.tensor([[0.0_019, 0.9_951, 0.0_030]]				)
			elif model_name == "xclip-base-patch32-16-frames":
						lowerCAmelCase_						=	torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]				)
			elif model_name == "xclip-base-patch16":
						lowerCAmelCase_						=	torch.tensor([[0.0_083, 0.9_681, 0.0_236]]				)
			elif model_name == "xclip-base-patch16-16-frames":
						lowerCAmelCase_						=	torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]				)
			elif model_name == "xclip-large-patch14":
						lowerCAmelCase_						=	torch.tensor([[0.0_062, 0.9_864, 0.0_075]]				)
			elif model_name == "xclip-large-patch14-16-frames":
						lowerCAmelCase_						=	torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]				)
			# kinetics-600
			elif model_name == "xclip-base-patch16-kinetics-600":
						lowerCAmelCase_						=	torch.tensor([[0.0_555, 0.8_914, 0.0_531]]				)
			elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
						lowerCAmelCase_						=	torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]				)
			elif model_name == "xclip-large-patch14-kinetics-600":
						lowerCAmelCase_						=	torch.tensor([[0.0_036, 0.9_920, 0.0_045]]				)
			# few shot
			elif model_name == "xclip-base-patch16-hmdb-2-shot":
						lowerCAmelCase_						=	torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-4-shot":
						lowerCAmelCase_						=	torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-8-shot":
						lowerCAmelCase_						=	torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-16-shot":
						lowerCAmelCase_						=	torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]				)
			elif model_name == "xclip-base-patch16-ucf-2-shot":
						lowerCAmelCase_						=	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
			elif model_name == "xclip-base-patch16-ucf-4-shot":
						lowerCAmelCase_						=	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
			elif model_name == "xclip-base-patch16-ucf-8-shot":
						lowerCAmelCase_						=	torch.tensor([[0.0_027, 0.9_904, 0.0_070]]				)
			elif model_name == "xclip-base-patch16-ucf-16-shot":
						lowerCAmelCase_						=	torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]				)
			# zero shot
			elif model_name == "xclip-base-patch16-zero-shot":
						lowerCAmelCase_						=	torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]				)
			else:
						raise ValueError(F"""Model name {model_name} not supported"""				)
			assert torch.allclose(__lowerCAmelCase    ,							__lowerCAmelCase    ,							atol=1e-3				)
			print("Looks ok!"				)

			if pytorch_dump_folder_path is not None:
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
						model.save_pretrained(__lowerCAmelCase				)

			if push_to_hub:
						print("Pushing model, processor and slow tokenizer files to the hub..."				)
						model.push_to_hub(__lowerCAmelCase    ,							organization="nielsr"				)
						processor.push_to_hub(__lowerCAmelCase    ,							organization="nielsr"				)
						slow_tokenizer.push_to_hub(__lowerCAmelCase    ,							organization="nielsr"				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="xclip-base-patch32",
			    type=str,
			    help="Name of the model.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument(
			    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
			)

			_A   =     parser.parse_args()
			convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A   =     logging.get_logger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


			lowerCAmelCase_						=	[]
			for i in range(config.num_hidden_layers				):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")				)
						rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")				)
						rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")				)
						rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")				)
						rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")				)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.embeddings.position_embeddings"),
			    ]				)

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.bias"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.bias"),
						    ]				)

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						lowerCAmelCase_						=	[(pair[0], pair[1][4:]) if pair[1].startswith("deit"				) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]				)

			return rename_keys






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


			for i in range(config.num_hidden_layers				):
						if base_model:
									lowerCAmelCase_						=	""
						else:
									lowerCAmelCase_						=	"deit."
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCAmelCase_						=	state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
						lowerCAmelCase_						=	state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
						# next, add query, keys and values (in that order) to the state dict
						lowerCAmelCase_						=	in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCAmelCase_						=	in_proj_bias[: config.hidden_size]
						lowerCAmelCase_						=	in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCAmelCase_						=	in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCAmelCase_						=	in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCAmelCase_						=	in_proj_bias[-config.hidden_size :]






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			lowerCAmelCase_						=	dct.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_						=	Image.open(requests.get(__lowerCAmelCase    ,							stream=__lowerCAmelCase				).raw				)
			return im






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	DeiTConfig()
			# all deit models have fine-tuned heads
			lowerCAmelCase_						=	False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCAmelCase_						=	1000
			lowerCAmelCase_						=	"huggingface/label-files"
			lowerCAmelCase_						=	"imagenet-1k-id2label.json"
			lowerCAmelCase_						=	json.load(open(hf_hub_download(__lowerCAmelCase    ,							__lowerCAmelCase    ,							repo_type="dataset"				)    ,							"r"				)				)
			lowerCAmelCase_						=	{int(__lowerCAmelCase				): v for k, v in idalabel.items()}
			lowerCAmelCase_						=	idalabel
			lowerCAmelCase_						=	{v: k for k, v in idalabel.items()}
			lowerCAmelCase_						=	int(deit_name[-6:-4]				)
			lowerCAmelCase_						=	int(deit_name[-3:]				)
			# size of the architecture
			if deit_name[9:].startswith("tiny"				):
						lowerCAmelCase_						=	192
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	3
			elif deit_name[9:].startswith("small"				):
						lowerCAmelCase_						=	384
						lowerCAmelCase_						=	1536
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	6
			if deit_name[9:].startswith("base"				):
						pass
			elif deit_name[4:].startswith("large"				):
						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	16

			# load original model from timm
			lowerCAmelCase_						=	timm.create_model(__lowerCAmelCase    ,							pretrained=__lowerCAmelCase				)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCAmelCase_						=	timm_model.state_dict()
			lowerCAmelCase_						=	create_rename_keys(__lowerCAmelCase    ,							__lowerCAmelCase				)
			for src, dest in rename_keys:
						rename_key(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
			read_in_q_k_v(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)

			# load HuggingFace model
			lowerCAmelCase_						=	DeiTForImageClassificationWithTeacher(__lowerCAmelCase				).eval()
			model.load_state_dict(__lowerCAmelCase				)

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowerCAmelCase_						=	int(
			    (256 / 224) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			lowerCAmelCase_						=	DeiTImageProcessor(size=__lowerCAmelCase    ,							crop_size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=prepare_img()    ,							return_tensors="pt"				)
			lowerCAmelCase_						=	encoding["pixel_values"]
			lowerCAmelCase_						=	model(__lowerCAmelCase				)

			lowerCAmelCase_						=	timm_model(__lowerCAmelCase				)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(__lowerCAmelCase    ,							outputs.logits    ,							atol=1e-3				)

			Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
			print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""				)
			model.save_pretrained(__lowerCAmelCase				)
			print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
			image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--deit_name",
			    default="vit_deit_base_distilled_patch16_224",
			    type=str,
			    help="Name of the DeiT timm model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)

			_A   =     parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



