

'''simple docstring'''
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       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[Any]							)			-> List[str]:


	"""simple docstring"""





	UpperCAmelCase_			:	Tuple      =   TimesformerConfig()

	if "large" in model_name:
		UpperCAmelCase_			:	Union[str, Any]      =   96

	if "hr" in model_name:
		UpperCAmelCase_			:	Optional[Any]      =   16
		UpperCAmelCase_			:	Union[str, Any]      =   4_48

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

	return config


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[str]:


	"""simple docstring"""





	if "encoder." in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("encoder."     ,							""							)
	if "cls_token" in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("cls_token"     ,							"timesformer.embeddings.cls_token"							)
	if "pos_embed" in name:
		UpperCAmelCase_			:	Union[str, Any]      =   name.replace("pos_embed"     ,							"timesformer.embeddings.position_embeddings"							)
	if "time_embed" in name:
		UpperCAmelCase_			:	List[str]      =   name.replace("time_embed"     ,							"timesformer.embeddings.time_embeddings"							)
	if "patch_embed.proj" in name:
		UpperCAmelCase_			:	Optional[int]      =   name.replace("patch_embed.proj"     ,							"timesformer.embeddings.patch_embeddings.projection"							)
	if "patch_embed.norm" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("patch_embed.norm"     ,							"timesformer.embeddings.norm"							)
	if "blocks" in name:
		UpperCAmelCase_			:	Optional[Any]      =   name.replace("blocks"     ,							"timesformer.encoder.layer"							)
	if "attn.proj" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("attn.proj"     ,							"attention.output.dense"							)
	if "attn" in name and "bias" not in name and "temporal" not in name:
		UpperCAmelCase_			:	Union[str, Any]      =   name.replace("attn"     ,							"attention.self"							)
	if "attn" in name and "temporal" not in name:
		UpperCAmelCase_			:	Dict      =   name.replace("attn"     ,							"attention.attention"							)
	if "temporal_norm1" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("temporal_norm1"     ,							"temporal_layernorm"							)
	if "temporal_attn.proj" in name:
		UpperCAmelCase_			:	int      =   name.replace("temporal_attn"     ,							"temporal_attention.output.dense"							)
	if "temporal_fc" in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("temporal_fc"     ,							"temporal_dense"							)
	if "norm1" in name and "temporal" not in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("norm1"     ,							"layernorm_before"							)
	if "norm2" in name:
		UpperCAmelCase_			:	Optional[int]      =   name.replace("norm2"     ,							"layernorm_after"							)
	if "mlp.fc1" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("mlp.fc1"     ,							"intermediate.dense"							)
	if "mlp.fc2" in name:
		UpperCAmelCase_			:	Tuple      =   name.replace("mlp.fc2"     ,							"output.dense"							)
	if "norm.weight" in name and "fc" not in name and "temporal" not in name:
		UpperCAmelCase_			:	Optional[Any]      =   name.replace("norm.weight"     ,							"timesformer.layernorm.weight"							)
	if "norm.bias" in name and "fc" not in name and "temporal" not in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("norm.bias"     ,							"timesformer.layernorm.bias"							)
	if "head" in name:
		UpperCAmelCase_			:	Optional[Any]      =   name.replace("head"     ,							"classifier"							)

	return name


def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    Dict							)			-> List[str]:


	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_			:	str      =   orig_state_dict.pop(_SCREAMING_SNAKE_CASE							)

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

		if "qkv" in key:
			UpperCAmelCase_			:	List[Any]      =   key.split("."							)
			UpperCAmelCase_			:	Union[str, Any]      =   int(key_split[1]							)
			UpperCAmelCase_			:	Optional[Any]      =   "timesformer.encoder.layer."
			if "temporal" in key:
				UpperCAmelCase_			:	Optional[Any]      =   ".temporal_attention.attention.qkv."
			else:
				UpperCAmelCase_			:	Optional[Any]      =   ".attention.attention.qkv."
			if "weight" in key:
				UpperCAmelCase_			:	List[str]      =   val
			else:
				UpperCAmelCase_			:	List[Any]      =   val
		else:
			UpperCAmelCase_			:	List[Any]      =   val

	return orig_state_dict


def       a__    (  )			-> Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	str      =   hf_hub_download(
	    repo_id="hf-internal-testing/spaghetti-video"     ,							filename="eating_spaghetti.npy"     ,							repo_type="dataset"							)
	UpperCAmelCase_			:	Any      =   np.load(_SCREAMING_SNAKE_CASE							)
	return list(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    Dict							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Union[str, Any]      =   get_timesformer_config(_SCREAMING_SNAKE_CASE							)

	UpperCAmelCase_			:	Dict      =   TimesformerForVideoClassification(_SCREAMING_SNAKE_CASE							)

	# download original checkpoint, hosted on Google Drive
	UpperCAmelCase_			:	List[str]      =   "pytorch_model.bin"
	gdown.cached_download(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							quiet=_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	int      =   torch.load(_SCREAMING_SNAKE_CASE     ,							map_location="cpu"							)
	if "model" in files:
		UpperCAmelCase_			:	Optional[int]      =   files["model"]
	elif "module" in files:
		UpperCAmelCase_			:	Optional[Any]      =   files["module"]
	else:
		UpperCAmelCase_			:	Optional[Any]      =   files["model_state"]
	UpperCAmelCase_			:	int      =   convert_state_dict(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)

	model.load_state_dict(_SCREAMING_SNAKE_CASE							)
	model.eval()

	# verify model on basic input
	UpperCAmelCase_			:	List[Any]      =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,							image_std=[0.5, 0.5, 0.5]							)
	UpperCAmelCase_			:	Union[str, Any]      =   prepare_video()
	UpperCAmelCase_			:	Tuple      =   image_processor(video[:8]     ,							return_tensors="pt"							)

	UpperCAmelCase_			:	List[Any]      =   model(**_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	List[Any]      =   outputs.logits

	UpperCAmelCase_			:	str      =   [
	    # 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_			:	int      =   torch.Size([1, 4_00]							)
		UpperCAmelCase_			:	Optional[int]      =   torch.tensor([-0.3_016, -0.7_713, -0.4_205]							)
	elif model_name == "timesformer-base-finetuned-k600":
		UpperCAmelCase_			:	Tuple      =   torch.Size([1, 6_00]							)
		UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor([-0.7_267, -0.7_466, 3.2_404]							)
	elif model_name == "timesformer-base-finetuned-ssv2":
		UpperCAmelCase_			:	Optional[int]      =   torch.Size([1, 1_74]							)
		UpperCAmelCase_			:	int      =   torch.tensor([-0.9_059, 0.6_433, -3.1_457]							)
	elif model_name == "timesformer-large-finetuned-k400":
		UpperCAmelCase_			:	Any      =   torch.Size([1, 4_00]							)
		UpperCAmelCase_			:	Any      =   torch.tensor([0, 0, 0]							)
	elif model_name == "timesformer-large-finetuned-k600":
		UpperCAmelCase_			:	Tuple      =   torch.Size([1, 6_00]							)
		UpperCAmelCase_			:	int      =   torch.tensor([0, 0, 0]							)
	elif model_name == "timesformer-large-finetuned-ssv2":
		UpperCAmelCase_			:	List[Any]      =   torch.Size([1, 1_74]							)
		UpperCAmelCase_			:	Optional[Any]      =   torch.tensor([0, 0, 0]							)
	elif model_name == "timesformer-hr-finetuned-k400":
		UpperCAmelCase_			:	List[str]      =   torch.Size([1, 4_00]							)
		UpperCAmelCase_			:	int      =   torch.tensor([-0.9_617, -3.7_311, -3.7_708]							)
	elif model_name == "timesformer-hr-finetuned-k600":
		UpperCAmelCase_			:	Union[str, Any]      =   torch.Size([1, 6_00]							)
		UpperCAmelCase_			:	Optional[int]      =   torch.tensor([2.5_273, 0.7_127, 1.8_848]							)
	elif model_name == "timesformer-hr-finetuned-ssv2":
		UpperCAmelCase_			:	str      =   torch.Size([1, 1_74]							)
		UpperCAmelCase_			:	List[Any]      =   torch.tensor([-3.6_756, -0.7_513, 0.7_180]							)
	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]     ,							_SCREAMING_SNAKE_CASE     ,							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(_SCREAMING_SNAKE_CASE							)
		model.save_pretrained(_SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
					_lowerCamelCase	 =			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."""
					)

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







'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




