
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__						(    A__   )       -> Optional[int]:
				snake_case__ :     List[str] 							=				TimesformerConfig()

				if "large" in model_name:
								snake_case__ :     Dict 							=				96

				if "hr" in model_name:
								snake_case__ :     Optional[int] 							=				16
								snake_case__ :     Dict 							=				448

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

				return config
def 			UpperCamelCase__						(    A__   )       -> List[Any]:
				if "encoder." in name:
								snake_case__ :     Optional[Any] 							=				name.replace('encoder.'      ,			''   )
				if "cls_token" in name:
								snake_case__ :     Dict 							=				name.replace('cls_token'      ,			'timesformer.embeddings.cls_token'   )
				if "pos_embed" in name:
								snake_case__ :     Optional[Any] 							=				name.replace('pos_embed'      ,			'timesformer.embeddings.position_embeddings'   )
				if "time_embed" in name:
								snake_case__ :     List[Any] 							=				name.replace('time_embed'      ,			'timesformer.embeddings.time_embeddings'   )
				if "patch_embed.proj" in name:
								snake_case__ :     Any 							=				name.replace('patch_embed.proj'      ,			'timesformer.embeddings.patch_embeddings.projection'   )
				if "patch_embed.norm" in name:
								snake_case__ :     int 							=				name.replace('patch_embed.norm'      ,			'timesformer.embeddings.norm'   )
				if "blocks" in name:
								snake_case__ :     List[Any] 							=				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__ :     Union[str, Any] 							=				name.replace('attn'      ,			'attention.self'   )
				if "attn" in name and "temporal" not in name:
								snake_case__ :     Dict 							=				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__ :     Optional[Any] 							=				name.replace('temporal_fc'      ,			'temporal_dense'   )
				if "norm1" in name and "temporal" not in name:
								snake_case__ :     List[Any] 							=				name.replace('norm1'      ,			'layernorm_before'   )
				if "norm2" in name:
								snake_case__ :     Dict 							=				name.replace('norm2'      ,			'layernorm_after'   )
				if "mlp.fc1" in name:
								snake_case__ :     str 							=				name.replace('mlp.fc1'      ,			'intermediate.dense'   )
				if "mlp.fc2" in name:
								snake_case__ :     List[Any] 							=				name.replace('mlp.fc2'      ,			'output.dense'   )
				if "norm.weight" in name and "fc" not in name and "temporal" not in name:
								snake_case__ :     List[str] 							=				name.replace('norm.weight'      ,			'timesformer.layernorm.weight'   )
				if "norm.bias" in name and "fc" not in name and "temporal" not in name:
								snake_case__ :     List[Any] 							=				name.replace('norm.bias'      ,			'timesformer.layernorm.bias'   )
				if "head" in name:
								snake_case__ :     int 							=				name.replace('head'      ,			'classifier'   )

				return name
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[Any]:
				for key in orig_state_dict.copy().keys():
								snake_case__ :     Union[str, Any] 							=				orig_state_dict.pop(A__   )

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

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

				return orig_state_dict
def 			UpperCamelCase__						(    )       -> List[str]:
				snake_case__ :     Optional[Any] 							=				hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'      ,			filename='eating_spaghetti.npy'      ,			repo_type='dataset'   )
				snake_case__ :     Dict 							=				np.load(A__   )
				return list(A__   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> int:
				snake_case__ :     str 							=				get_timesformer_config(A__   )

				snake_case__ :     int 							=				TimesformerForVideoClassification(A__   )

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

				model.load_state_dict(A__   )
				model.eval()

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

				snake_case__ :     Dict 							=				model(**A__   )
				snake_case__ :     Optional[Any] 							=				outputs.logits

				snake_case__ :     List[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__ :     List[str] 							=				torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]   )
				elif model_name == "timesformer-base-finetuned-k600":
								snake_case__ :     Tuple 							=				torch.Size([1, 600]   )
								snake_case__ :     List[str] 							=				torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]   )
				elif model_name == "timesformer-base-finetuned-ssv2":
								snake_case__ :     Tuple 							=				torch.Size([1, 174]   )
								snake_case__ :     Dict 							=				torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]   )
				elif model_name == "timesformer-large-finetuned-k400":
								snake_case__ :     int 							=				torch.Size([1, 400]   )
								snake_case__ :     List[Any] 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-large-finetuned-k600":
								snake_case__ :     str 							=				torch.Size([1, 600]   )
								snake_case__ :     Tuple 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-large-finetuned-ssv2":
								snake_case__ :     Tuple 							=				torch.Size([1, 174]   )
								snake_case__ :     int 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-hr-finetuned-k400":
								snake_case__ :     List[str] 							=				torch.Size([1, 400]   )
								snake_case__ :     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":
								snake_case__ :     Union[str, Any] 							=				torch.Size([1, 600]   )
								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":
								snake_case__ :     List[Any] 							=				torch.Size([1, 174]   )
								snake_case__ :     str 							=				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__":
				lowerCAmelCase__					:       Optional[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__					:       Tuple		=       parser.parse_args()
				convert_timesformer_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
				)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


