




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       __magic_name__    (			A		: Optional[int]       ):
			a   =      TimesformerConfig()

			if "large" in model_name:
						a   =      96

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

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

			return config




def       __magic_name__    (			A		: Union[str, Any]       ):







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

			return name




def       __magic_name__    (			A		: Tuple,  A		: List[str]       ):







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

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

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

			return orig_state_dict




def       __magic_name__    (			):
			a   =      hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video",  filename="eating_spaghetti.npy",  repo_type="dataset"       )
			a   =      np.load(A       )
			return list(A       )




def       __magic_name__    (			A		: Optional[Any],  A		: Tuple,  A		: str,  A		: int       ):
			a   =      get_timesformer_config(A       )

			a   =      TimesformerForVideoClassification(A       )

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

			model.load_state_dict(A       )
			model.eval()

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

			a   =      model(**A       )
			a   =      outputs.logits

			a   =      [
			    # 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":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([-0.30_16, -0.77_13, -0.42_05]       )
			elif model_name == "timesformer-base-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([-0.72_67, -0.74_66, 3.24_04]       )
			elif model_name == "timesformer-base-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      torch.tensor([-0.90_59, 0.64_33, -3.14_57]       )
			elif model_name == "timesformer-large-finetuned-k400":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-large-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-large-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-hr-finetuned-k400":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([-0.96_17, -3.73_11, -3.77_08]       )
			elif model_name == "timesformer-hr-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([2.52_73, 0.71_27, 1.88_48]       )
			elif model_name == "timesformer-hr-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      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],  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		:    Union[str, 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		:    Optional[Any]	      =		parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."							)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."							)

							super().__init__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






