



"""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    __snake_case       (  __A			:	Optional[Any]    )    ->					Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    TimesformerConfig()

				if "large" in model_name:
								SCREAMING_SNAKE_CASE	:       Any 							=    96

				if "hr" in model_name:
								SCREAMING_SNAKE_CASE	:       Any 							=    16
								SCREAMING_SNAKE_CASE	:       str 							=    448

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

				return config
def    __snake_case       (  __A			:	Union[str, Any]    )    ->					Optional[int]:






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

				return name
def    __snake_case       (  __A			:	Tuple       ,		__A			:	List[str]    )    ->					Dict:






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    orig_state_dict.pop(__A    )

								if key.startswith('model.'    ):
												SCREAMING_SNAKE_CASE	:       Any 							=    key.replace('model.'       ,		''    )

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

				return orig_state_dict
def    __snake_case       (  )    ->					List[str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'       ,		filename='eating_spaghetti.npy'       ,		repo_type='dataset'    )
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    np.load(__A    )
				return list(__A    )
def    __snake_case       (  __A			:	int       ,		__A			:	int       ,		__A			:	int       ,		__A			:	List[Any]    )    ->					str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       int 							=    get_timesformer_config(__A    )

				SCREAMING_SNAKE_CASE	:       List[str] 							=    TimesformerForVideoClassification(__A    )

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

				model.load_state_dict(__A    )
				model.eval()

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

				SCREAMING_SNAKE_CASE	:       int 							=    model(**__A    )
				SCREAMING_SNAKE_CASE	:       int 							=    outputs.logits

				SCREAMING_SNAKE_CASE	:       Dict 							=    [
				    # 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":
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.Size([1, 400]    )
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]    )
				elif model_name == "timesformer-base-finetuned-k600":
								SCREAMING_SNAKE_CASE	:       int 							=    torch.Size([1, 600]    )
								SCREAMING_SNAKE_CASE	:       int 							=    torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]    )
				elif model_name == "timesformer-base-finetuned-ssv2":
								SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.Size([1, 174]    )
								SCREAMING_SNAKE_CASE	:       str 							=    torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]    )
				elif model_name == "timesformer-large-finetuned-k400":
								SCREAMING_SNAKE_CASE	:       Dict 							=    torch.Size([1, 400]    )
								SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.tensor([0, 0, 0]    )
				elif model_name == "timesformer-large-finetuned-k600":
								SCREAMING_SNAKE_CASE	:       Dict 							=    torch.Size([1, 600]    )
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.tensor([0, 0, 0]    )
				elif model_name == "timesformer-large-finetuned-ssv2":
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.Size([1, 174]    )
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.tensor([0, 0, 0]    )
				elif model_name == "timesformer-hr-finetuned-k400":
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.Size([1, 400]    )
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]    )
				elif model_name == "timesformer-hr-finetuned-k600":
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.Size([1, 600]    )
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]    )
				elif model_name == "timesformer-hr-finetuned-ssv2":
								SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.Size([1, 174]    )
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    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__":
							A_	:						List[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.'
							)

							A_	:						Tuple				=			parser.parse_args()
							convert_timesformer_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
							)










"""simple docstring"""

def    __snake_case       (  __A			:	str       ,		__A			:	str    )    ->					int:






				'''simple docstring'''
				# The longer word should come first
				if len(__A    ) < len(__A    ):
								return levenshtein_distance(__A       ,		__A    )

				if len(__A    ) == 0:
								return len(__A    )

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    list(range(len(__A    ) + 1    )    )

				for i, ca in enumerate(__A    ):
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [i + 1]

								for j, ca in enumerate(__A    ):
												# Calculate insertions, deletions and substitutions
												SCREAMING_SNAKE_CASE	:       str 							=    previous_row[j + 1] + 1
												SCREAMING_SNAKE_CASE	:       List[str] 							=    current_row[j] + 1
												SCREAMING_SNAKE_CASE	:       Any 							=    previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(__A       ,		__A       ,		__A    )    )

								# Store the previous row
								SCREAMING_SNAKE_CASE	:       Tuple 							=    current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
							A_	:						Optional[int]				=			input('Enter the first word:\n').strip()
							A_	:						Optional[Any]				=			input('Enter the second word:\n').strip()

							A_	:						str				=			levenshtein_distance(first_word, second_word)
							print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





