"""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_		(   lowerCamelCase			):
				UpperCAmelCase__			 =				TimesformerConfig()

				if "large" in model_name:
								UpperCAmelCase__			 =				9_6

				if "hr" in model_name:
								UpperCAmelCase__			 =				1_6
								UpperCAmelCase__			 =				4_4_8

				UpperCAmelCase__			 =				'huggingface/label-files'
				if "k400" in model_name:
								UpperCAmelCase__			 =				4_0_0
								UpperCAmelCase__			 =				'kinetics400-id2label.json'
				elif "k600" in model_name:
								UpperCAmelCase__			 =				6_0_0
								UpperCAmelCase__			 =				'kinetics600-id2label.json'
				elif "ssv2" in model_name:
								UpperCAmelCase__			 =				1_7_4
								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(lowercase__   ,     lowercase__   ,     repo_type='dataset'			)   ,     'r'			)			)
				UpperCAmelCase__			 =				{int(lowercase__			): v for k, v in idalabel.items()}
				UpperCAmelCase__			 =				idalabel
				UpperCAmelCase__			 =				{v: k for k, v in idalabel.items()}

				return config



def     a_		(   lowerCamelCase			):
				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     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				for key in orig_state_dict.copy().keys():
								UpperCAmelCase__			 =				orig_state_dict.pop(lowercase__			)

								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     a_		(   ):
				UpperCAmelCase__			 =				hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'   ,     filename='eating_spaghetti.npy'   ,     repo_type='dataset'			)
				UpperCAmelCase__			 =				np.load(lowercase__			)
				return list(lowercase__			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				get_timesformer_config(lowercase__			)

				UpperCAmelCase__			 =				TimesformerForVideoClassification(lowercase__			)

				# download original checkpoint, hosted on Google Drive
				UpperCAmelCase__			 =				'pytorch_model.bin'
				gdown.cached_download(lowercase__   ,     lowercase__   ,     quiet=lowercase__			)
				UpperCAmelCase__			 =				torch.load(lowercase__   ,     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(lowercase__   ,     lowercase__			)

				model.load_state_dict(lowercase__			)
				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(**lowercase__			)
				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_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([-0.3016, -0.7713, -0.4205]			)
				elif model_name == "timesformer-base-finetuned-k600":
								UpperCAmelCase__			 =				torch.Size([1, 6_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([-0.7267, -0.7466, 3.2404]			)
				elif model_name == "timesformer-base-finetuned-ssv2":
								UpperCAmelCase__			 =				torch.Size([1, 1_7_4]			)
								UpperCAmelCase__			 =				torch.tensor([-0.9059, 0.6433, -3.1457]			)
				elif model_name == "timesformer-large-finetuned-k400":
								UpperCAmelCase__			 =				torch.Size([1, 4_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([0, 0, 0]			)
				elif model_name == "timesformer-large-finetuned-k600":
								UpperCAmelCase__			 =				torch.Size([1, 6_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([0, 0, 0]			)
				elif model_name == "timesformer-large-finetuned-ssv2":
								UpperCAmelCase__			 =				torch.Size([1, 1_7_4]			)
								UpperCAmelCase__			 =				torch.tensor([0, 0, 0]			)
				elif model_name == "timesformer-hr-finetuned-k400":
								UpperCAmelCase__			 =				torch.Size([1, 4_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([-0.9617, -3.7311, -3.7708]			)
				elif model_name == "timesformer-hr-finetuned-k600":
								UpperCAmelCase__			 =				torch.Size([1, 6_0_0]			)
								UpperCAmelCase__			 =				torch.tensor([2.5273, 0.7127, 1.8848]			)
				elif model_name == "timesformer-hr-finetuned-ssv2":
								UpperCAmelCase__			 =				torch.Size([1, 1_7_4]			)
								UpperCAmelCase__			 =				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]   ,     lowercase__   ,     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(lowercase__			)
								model.save_pretrained(lowercase__			)

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


if __name__ == "__main__":
						lowerCAmelCase__    :    Union[str, Any]			=      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__    :    int			=      parser.parse_args()
						convert_timesformer_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
						)






"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





