






"""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 						lowercase__(A						)				->List[Any]:




						"""simple docstring"""



						lowercase__				:      Any=       TimesformerConfig()

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

						if "hr" in model_name:
												lowercase__				:      Dict=       16
												lowercase__				:      Optional[Any]=       448

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

						return config





def 						lowercase__(A						)				->str:




						"""simple docstring"""



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

						return name





def 						lowercase__(A					,		A						)				->List[str]:




						"""simple docstring"""



						for key in orig_state_dict.copy().keys():
												lowercase__				:      Dict=       orig_state_dict.pop(A						)

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

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

						return orig_state_dict





def 						lowercase__()				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      Union[str, Any]=       hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video"					,		filename="eating_spaghetti.npy"					,		repo_type="dataset"						)
						lowercase__				:      Tuple=       np.load(A						)
						return list(A						)





def 						lowercase__(A					,		A					,		A					,		A						)				->Dict:




						"""simple docstring"""



						lowercase__				:      Union[str, Any]=       get_timesformer_config(A						)

						lowercase__				:      Union[str, Any]=       TimesformerForVideoClassification(A						)

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

						model.load_state_dict(A						)
						model.eval()

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

						lowercase__				:      int=       model(**A						)
						lowercase__				:      Optional[int]=       outputs.logits

						lowercase__				:      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":
												lowercase__				:      str=       torch.Size([1, 400]						)
												lowercase__				:      str=       torch.tensor([-0.3_016, -0.7_713, -0.4_205]						)
						elif model_name == "timesformer-base-finetuned-k600":
												lowercase__				:      Dict=       torch.Size([1, 600]						)
												lowercase__				:      Optional[Any]=       torch.tensor([-0.7_267, -0.7_466, 3.2_404]						)
						elif model_name == "timesformer-base-finetuned-ssv2":
												lowercase__				:      Optional[Any]=       torch.Size([1, 174]						)
												lowercase__				:      Tuple=       torch.tensor([-0.9_059, 0.6_433, -3.1_457]						)
						elif model_name == "timesformer-large-finetuned-k400":
												lowercase__				:      Tuple=       torch.Size([1, 400]						)
												lowercase__				:      List[Any]=       torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-large-finetuned-k600":
												lowercase__				:      Dict=       torch.Size([1, 600]						)
												lowercase__				:      Optional[int]=       torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-large-finetuned-ssv2":
												lowercase__				:      Tuple=       torch.Size([1, 174]						)
												lowercase__				:      Optional[Any]=       torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-hr-finetuned-k400":
												lowercase__				:      Dict=       torch.Size([1, 400]						)
												lowercase__				:      List[str]=       torch.tensor([-0.9_617, -3.7_311, -3.7_708]						)
						elif model_name == "timesformer-hr-finetuned-k600":
												lowercase__				:      Optional[int]=       torch.Size([1, 600]						)
												lowercase__				:      List[str]=       torch.tensor([2.5_273, 0.7_127, 1.8_848]						)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												lowercase__				:      Tuple=       torch.Size([1, 174]						)
												lowercase__				:      Union[str, Any]=       torch.tensor([-3.6_756, -0.7_513, 0.7_180]						)
						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 :				Dict      					=		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 :				Union[str, Any]      					=		parser.parse_args()
			convert_timesformer_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
			)














"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






