
"""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     _lowerCamelCase(__UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=TimesformerConfig()

	if "large" in model_name:
		_lowerCAmelCase								=96

	if "hr" in model_name:
		_lowerCAmelCase								=16
		_lowerCAmelCase								=448

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

	return config



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

	return name



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> str:
	for key in orig_state_dict.copy().keys():
		_lowerCAmelCase								=orig_state_dict.pop(UpperCamelCase_       )

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

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

	return orig_state_dict



def     _lowerCamelCase()				-> Tuple:
	_lowerCAmelCase								=hf_hub_download(
	    repo_id="""hf-internal-testing/spaghetti-video"""    , filename="""eating_spaghetti.npy"""    , repo_type="""dataset"""       )
	_lowerCAmelCase								=np.load(UpperCamelCase_       )
	return list(UpperCamelCase_       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> List[Any]:
	_lowerCAmelCase								=get_timesformer_config(UpperCamelCase_       )

	_lowerCAmelCase								=TimesformerForVideoClassification(UpperCamelCase_       )

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

	model.load_state_dict(UpperCamelCase_       )
	model.eval()

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

	_lowerCAmelCase								=model(**UpperCamelCase_       )
	_lowerCAmelCase								=outputs.logits

	_lowerCAmelCase								=[
	    # 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":
		_lowerCAmelCase								=torch.Size([1, 400]       )
		_lowerCAmelCase								=torch.tensor([-0.30_16, -0.77_13, -0.42_05]       )
	elif model_name == "timesformer-base-finetuned-k600":
		_lowerCAmelCase								=torch.Size([1, 600]       )
		_lowerCAmelCase								=torch.tensor([-0.72_67, -0.74_66, 3.24_04]       )
	elif model_name == "timesformer-base-finetuned-ssv2":
		_lowerCAmelCase								=torch.Size([1, 174]       )
		_lowerCAmelCase								=torch.tensor([-0.90_59, 0.64_33, -3.14_57]       )
	elif model_name == "timesformer-large-finetuned-k400":
		_lowerCAmelCase								=torch.Size([1, 400]       )
		_lowerCAmelCase								=torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-k600":
		_lowerCAmelCase								=torch.Size([1, 600]       )
		_lowerCAmelCase								=torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-ssv2":
		_lowerCAmelCase								=torch.Size([1, 174]       )
		_lowerCAmelCase								=torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-hr-finetuned-k400":
		_lowerCAmelCase								=torch.Size([1, 400]       )
		_lowerCAmelCase								=torch.tensor([-0.96_17, -3.73_11, -3.77_08]       )
	elif model_name == "timesformer-hr-finetuned-k600":
		_lowerCAmelCase								=torch.Size([1, 600]       )
		_lowerCAmelCase								=torch.tensor([2.52_73, 0.71_27, 1.88_48]       )
	elif model_name == "timesformer-hr-finetuned-ssv2":
		_lowerCAmelCase								=torch.Size([1, 174]       )
		_lowerCAmelCase								=torch.tensor([-3.67_56, -0.75_13, 0.71_80]       )
	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]    , UpperCamelCase_    , 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(UpperCamelCase_       )
		model.save_pretrained(UpperCamelCase_       )

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


if __name__ == "__main__":
			__A       						=       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.'
			)

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










def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

