



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_    ):






				'''simple docstring'''
				lowercase__       			=      TimesformerConfig()

				if "large" in model_name:
								lowercase__       			=      96

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

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

				return config






def   a      (						lowerCamelCase_    ):






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

				return name






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase__       			=      orig_state_dict.pop(lowerCamelCase_    )

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

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

				return orig_state_dict






def   a      (						):






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






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      get_timesformer_config(lowerCamelCase_    )

				lowercase__       			=      TimesformerForVideoClassification(lowerCamelCase_    )

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

				model.load_state_dict(lowerCamelCase_    )
				model.eval()

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

				lowercase__       			=      model(**lowerCamelCase_    )
				lowercase__       			=      outputs.logits

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

				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__     :      str             =			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_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




