


import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
def     UpperCamelCase_(			_A				:Tuple					)->						Optional[Any]:
			UpperCamelCase__   =    TimesformerConfig()

			if "large" in model_name:
						UpperCamelCase__   =    96

			if "hr" in model_name:
						UpperCamelCase__   =    16
						UpperCamelCase__   =    4_48

			UpperCamelCase__   =    "huggingface/label-files"
			if "k400" in model_name:
						UpperCamelCase__   =    4_00
						UpperCamelCase__   =    "kinetics400-id2label.json"
			elif "k600" in model_name:
						UpperCamelCase__   =    6_00
						UpperCamelCase__   =    "kinetics600-id2label.json"
			elif "ssv2" in model_name:
						UpperCamelCase__   =    1_74
						UpperCamelCase__   =    "something-something-v2-id2label.json"
			else:
						raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."					)
			UpperCamelCase__   =    json.load(open(hf_hub_download(_A			,       _A			,       repo_type="dataset"					)			,       "r"					)					)
			UpperCamelCase__   =    {int(_A					): v for k, v in idalabel.items()}
			UpperCamelCase__   =    idalabel
			UpperCamelCase__   =    {v: k for k, v in idalabel.items()}

			return config
def     UpperCamelCase_(			_A				:Dict					)->						Dict:
			if "encoder." in name:
						UpperCamelCase__   =    name.replace("encoder."			,       ""					)
			if "cls_token" in name:
						UpperCamelCase__   =    name.replace("cls_token"			,       "timesformer.embeddings.cls_token"					)
			if "pos_embed" in name:
						UpperCamelCase__   =    name.replace("pos_embed"			,       "timesformer.embeddings.position_embeddings"					)
			if "time_embed" in name:
						UpperCamelCase__   =    name.replace("time_embed"			,       "timesformer.embeddings.time_embeddings"					)
			if "patch_embed.proj" in name:
						UpperCamelCase__   =    name.replace("patch_embed.proj"			,       "timesformer.embeddings.patch_embeddings.projection"					)
			if "patch_embed.norm" in name:
						UpperCamelCase__   =    name.replace("patch_embed.norm"			,       "timesformer.embeddings.norm"					)
			if "blocks" in name:
						UpperCamelCase__   =    name.replace("blocks"			,       "timesformer.encoder.layer"					)
			if "attn.proj" in name:
						UpperCamelCase__   =    name.replace("attn.proj"			,       "attention.output.dense"					)
			if "attn" in name and "bias" not in name and "temporal" not in name:
						UpperCamelCase__   =    name.replace("attn"			,       "attention.self"					)
			if "attn" in name and "temporal" not in name:
						UpperCamelCase__   =    name.replace("attn"			,       "attention.attention"					)
			if "temporal_norm1" in name:
						UpperCamelCase__   =    name.replace("temporal_norm1"			,       "temporal_layernorm"					)
			if "temporal_attn.proj" in name:
						UpperCamelCase__   =    name.replace("temporal_attn"			,       "temporal_attention.output.dense"					)
			if "temporal_fc" in name:
						UpperCamelCase__   =    name.replace("temporal_fc"			,       "temporal_dense"					)
			if "norm1" in name and "temporal" not in name:
						UpperCamelCase__   =    name.replace("norm1"			,       "layernorm_before"					)
			if "norm2" in name:
						UpperCamelCase__   =    name.replace("norm2"			,       "layernorm_after"					)
			if "mlp.fc1" in name:
						UpperCamelCase__   =    name.replace("mlp.fc1"			,       "intermediate.dense"					)
			if "mlp.fc2" in name:
						UpperCamelCase__   =    name.replace("mlp.fc2"			,       "output.dense"					)
			if "norm.weight" in name and "fc" not in name and "temporal" not in name:
						UpperCamelCase__   =    name.replace("norm.weight"			,       "timesformer.layernorm.weight"					)
			if "norm.bias" in name and "fc" not in name and "temporal" not in name:
						UpperCamelCase__   =    name.replace("norm.bias"			,       "timesformer.layernorm.bias"					)
			if "head" in name:
						UpperCamelCase__   =    name.replace("head"			,       "classifier"					)

			return name
def     UpperCamelCase_(			_A				:int			,       _A				:List[Any]					)->						List[str]:
			for key in orig_state_dict.copy().keys():
						UpperCamelCase__   =    orig_state_dict.pop(_A					)

						if key.startswith("model."					):
									UpperCamelCase__   =    key.replace("model."			,       ""					)

						if "qkv" in key:
									UpperCamelCase__   =    key.split("."					)
									UpperCamelCase__   =    int(key_split[1]					)
									UpperCamelCase__   =    "timesformer.encoder.layer."
									if "temporal" in key:
												UpperCamelCase__   =    ".temporal_attention.attention.qkv."
									else:
												UpperCamelCase__   =    ".attention.attention.qkv."
									if "weight" in key:
												UpperCamelCase__   =    val
									else:
												UpperCamelCase__   =    val
						else:
									UpperCamelCase__   =    val

			return orig_state_dict
def     UpperCamelCase_(			)->						Any:
			UpperCamelCase__   =    hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"			,       filename="eating_spaghetti.npy"			,       repo_type="dataset"					)
			UpperCamelCase__   =    np.load(_A					)
			return list(_A					)
def     UpperCamelCase_(			_A				:int			,       _A				:Union[str, Any]			,       _A				:Optional[int]			,       _A				:List[str]					)->						str:
			UpperCamelCase__   =    get_timesformer_config(_A					)

			UpperCamelCase__   =    TimesformerForVideoClassification(_A					)

			# download original checkpoint, hosted on Google Drive
			UpperCamelCase__   =    "pytorch_model.bin"
			gdown.cached_download(_A			,       _A			,       quiet=_A					)
			UpperCamelCase__   =    torch.load(_A			,       map_location="cpu"					)
			if "model" in files:
						UpperCamelCase__   =    files["model"]
			elif "module" in files:
						UpperCamelCase__   =    files["module"]
			else:
						UpperCamelCase__   =    files["model_state"]
			UpperCamelCase__   =    convert_state_dict(_A			,       _A					)

			model.load_state_dict(_A					)
			model.eval()

			# verify model on basic input
			UpperCamelCase__   =    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,       image_std=[0.5, 0.5, 0.5]					)
			UpperCamelCase__   =    prepare_video()
			UpperCamelCase__   =    image_processor(video[:8]			,       return_tensors="pt"					)

			UpperCamelCase__   =    model(**_A					)
			UpperCamelCase__   =    outputs.logits

			UpperCamelCase__   =    [
			    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-k400",
			    "timesformer-large-finetuned-k400",
			    "timesformer-hr-finetuned-k400",
			    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-k600",
			    "timesformer-large-finetuned-k600",
			    "timesformer-hr-finetuned-k600",
			    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-ssv2",
			    "timesformer-large-finetuned-ssv2",
			    "timesformer-hr-finetuned-ssv2",
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "timesformer-base-finetuned-k400":
						UpperCamelCase__   =    torch.Size([1, 4_00]					)
						UpperCamelCase__   =    torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]					)
			elif model_name == "timesformer-base-finetuned-k600":
						UpperCamelCase__   =    torch.Size([1, 6_00]					)
						UpperCamelCase__   =    torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]					)
			elif model_name == "timesformer-base-finetuned-ssv2":
						UpperCamelCase__   =    torch.Size([1, 1_74]					)
						UpperCamelCase__   =    torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]					)
			elif model_name == "timesformer-large-finetuned-k400":
						UpperCamelCase__   =    torch.Size([1, 4_00]					)
						UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-large-finetuned-k600":
						UpperCamelCase__   =    torch.Size([1, 6_00]					)
						UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-large-finetuned-ssv2":
						UpperCamelCase__   =    torch.Size([1, 1_74]					)
						UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-hr-finetuned-k400":
						UpperCamelCase__   =    torch.Size([1, 4_00]					)
						UpperCamelCase__   =    torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]					)
			elif model_name == "timesformer-hr-finetuned-k600":
						UpperCamelCase__   =    torch.Size([1, 6_00]					)
						UpperCamelCase__   =    torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]					)
			elif model_name == "timesformer-hr-finetuned-ssv2":
						UpperCamelCase__   =    torch.Size([1, 1_74]					)
						UpperCamelCase__   =    torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]					)
			else:
						raise ValueError(F'''Model name not supported. Should be one of {model_names}'''					)

			# verify logits
			assert logits.shape == expected_shape
			assert torch.allclose(logits[0, :3]			,       _A			,       atol=1e-4					)
			print("Logits ok!"					)

			if pytorch_dump_folder_path is not None:
						print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''					)
						image_processor.save_pretrained(_A					)
						model.save_pretrained(_A					)

			if push_to_hub:
						print("Pushing to the hub..."					)
						model.push_to_hub(F'''fcakyon/{model_name}'''					)


if __name__ == "__main__":
						__UpperCamelCase      						=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download',
						    type=str,
						    help=(
						        'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
						        ' download link.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument('--model_name', default='timesformer-base-finetuned-k400', type=str, help='Name of the model.')
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						__UpperCamelCase      						=    parser.parse_args()
						convert_timesformer_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
						)






from __future__ import annotations
def     UpperCamelCase_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


