
'''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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Union[str, Any]:


		'''simple docstring'''


		snake_case   :    Dict			       =		TimesformerConfig()

		if "large" in model_name:
				snake_case   :    Optional[int]			       =		96

		if "hr" in model_name:
				snake_case   :    str			       =		16
				snake_case   :    List[str]			       =		448

		snake_case   :    List[Any]			       =		'''huggingface/label-files'''
		if "k400" in model_name:
				snake_case   :    str			       =		400
				snake_case   :    Tuple			       =		'''kinetics400-id2label.json'''
		elif "k600" in model_name:
				snake_case   :    List[Any]			       =		600
				snake_case   :    Any			       =		'''kinetics600-id2label.json'''
		elif "ssv2" in model_name:
				snake_case   :    List[str]			       =		174
				snake_case   :    List[Any]			       =		'''something-something-v2-id2label.json'''
		else:
				raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''       )
		snake_case   :    List[str]			       =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    repo_type='''dataset'''       )      ,    '''r'''       )       )
		snake_case   :    List[Any]			       =		{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
		snake_case   :    List[str]			       =		idalabel
		snake_case   :    Dict			       =		{v: k for k, v in idalabel.items()}

		return config




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


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

		return name




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				snake_case   :    Dict			       =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

				if key.startswith('''model.'''       ):
						snake_case   :    Tuple			       =		key.replace('''model.'''      ,    ''''''       )

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

		return orig_state_dict




def 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		hf_hub_download(
		    repo_id='''hf-internal-testing/spaghetti-video'''      ,    filename='''eating_spaghetti.npy'''      ,    repo_type='''dataset'''       )
		snake_case   :    str			       =		np.load(SCREAMING_SNAKE_CASE__       )
		return list(SCREAMING_SNAKE_CASE__       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[Any]			       =		get_timesformer_config(SCREAMING_SNAKE_CASE__       )

		snake_case   :    Dict			       =		TimesformerForVideoClassification(SCREAMING_SNAKE_CASE__       )

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

		model.load_state_dict(SCREAMING_SNAKE_CASE__       )
		model.eval()

		# verify model on basic input
		snake_case   :    int			       =		VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,    image_std=[0.5, 0.5, 0.5]       )
		snake_case   :    Any			       =		prepare_video()
		snake_case   :    Optional[Any]			       =		image_processor(video[:8]      ,    return_tensors='''pt'''       )

		snake_case   :    List[str]			       =		model(**SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[Any]			       =		outputs.logits

		snake_case   :    Optional[Any]			       =		[
		    # 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":
				snake_case   :    int			       =		torch.Size([1, 400]       )
				snake_case   :    Optional[Any]			       =		torch.tensor([-0.3016, -0.7713, -0.4205]       )
		elif model_name == "timesformer-base-finetuned-k600":
				snake_case   :    Optional[int]			       =		torch.Size([1, 600]       )
				snake_case   :    str			       =		torch.tensor([-0.7267, -0.7466, 3.2404]       )
		elif model_name == "timesformer-base-finetuned-ssv2":
				snake_case   :    Optional[int]			       =		torch.Size([1, 174]       )
				snake_case   :    Union[str, Any]			       =		torch.tensor([-0.9059, 0.6433, -3.1457]       )
		elif model_name == "timesformer-large-finetuned-k400":
				snake_case   :    str			       =		torch.Size([1, 400]       )
				snake_case   :    Union[str, Any]			       =		torch.tensor([0, 0, 0]       )
		elif model_name == "timesformer-large-finetuned-k600":
				snake_case   :    Union[str, Any]			       =		torch.Size([1, 600]       )
				snake_case   :    Union[str, Any]			       =		torch.tensor([0, 0, 0]       )
		elif model_name == "timesformer-large-finetuned-ssv2":
				snake_case   :    Any			       =		torch.Size([1, 174]       )
				snake_case   :    Any			       =		torch.tensor([0, 0, 0]       )
		elif model_name == "timesformer-hr-finetuned-k400":
				snake_case   :    List[Any]			       =		torch.Size([1, 400]       )
				snake_case   :    Optional[int]			       =		torch.tensor([-0.9617, -3.7311, -3.7708]       )
		elif model_name == "timesformer-hr-finetuned-k600":
				snake_case   :    Any			       =		torch.Size([1, 600]       )
				snake_case   :    Optional[int]			       =		torch.tensor([2.5273, 0.7127, 1.8848]       )
		elif model_name == "timesformer-hr-finetuned-ssv2":
				snake_case   :    Optional[int]			       =		torch.Size([1, 174]       )
				snake_case   :    List[Any]			       =		torch.tensor([-3.6756, -0.7513, 0.7180]       )
		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__":
						lowercase__		    =	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."
						)

						lowercase__		    =	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

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


