



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     __lowerCamelCase							(   __magic_name__						:	Optional[int]    ):
							a__:							str									=TimesformerConfig()

							if "large" in model_name:
														a__:							List[Any]									=96

							if "hr" in model_name:
														a__:							Dict									=16
														a__:							Tuple									=448

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

							return config





def     __lowerCamelCase							(   __magic_name__						:	Dict    ):

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

							return name





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	Dict    ):

							for key in orig_state_dict.copy().keys():
														a__:							List[str]									=orig_state_dict.pop(__magic_name__    )

														if key.startswith("model."    ):
																					a__:							List[str]									=key.replace("model."  ,			""    )

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

							return orig_state_dict





def     __lowerCamelCase							(   ):
							a__:							Optional[Any]									=hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"  ,			filename="eating_spaghetti.npy"  ,			repo_type="dataset"    )
							a__:							Optional[Any]									=np.load(__magic_name__    )
							return list(__magic_name__    )





def     __lowerCamelCase							(   __magic_name__						:	Any  ,			__magic_name__						:	List[str]  ,			__magic_name__						:	Any  ,			__magic_name__						:	Optional[int]    ):
							a__:							Union[str, Any]									=get_timesformer_config(__magic_name__    )

							a__:							Optional[int]									=TimesformerForVideoClassification(__magic_name__    )

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

							model.load_state_dict(__magic_name__    )
							model.eval()

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

							a__:							List[Any]									=model(**__magic_name__    )
							a__:							Dict									=outputs.logits

							a__:							List[str]									=[
							    # 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__:							Dict									=torch.Size([1, 400]    )
														a__:							Tuple									=torch.tensor([-0.3_016, -0.7_713, -0.4_205]    )
							elif model_name == "timesformer-base-finetuned-k600":
														a__:							Optional[Any]									=torch.Size([1, 600]    )
														a__:							Tuple									=torch.tensor([-0.7_267, -0.7_466, 3.2_404]    )
							elif model_name == "timesformer-base-finetuned-ssv2":
														a__:							Tuple									=torch.Size([1, 174]    )
														a__:							Any									=torch.tensor([-0.9_059, 0.6_433, -3.1_457]    )
							elif model_name == "timesformer-large-finetuned-k400":
														a__:							List[str]									=torch.Size([1, 400]    )
														a__:							Union[str, Any]									=torch.tensor([0, 0, 0]    )
							elif model_name == "timesformer-large-finetuned-k600":
														a__:							List[str]									=torch.Size([1, 600]    )
														a__:							List[str]									=torch.tensor([0, 0, 0]    )
							elif model_name == "timesformer-large-finetuned-ssv2":
														a__:							Optional[int]									=torch.Size([1, 174]    )
														a__:							Any									=torch.tensor([0, 0, 0]    )
							elif model_name == "timesformer-hr-finetuned-k400":
														a__:							Optional[int]									=torch.Size([1, 400]    )
														a__:							Union[str, Any]									=torch.tensor([-0.9_617, -3.7_311, -3.7_708]    )
							elif model_name == "timesformer-hr-finetuned-k600":
														a__:							Union[str, Any]									=torch.Size([1, 600]    )
														a__:							Tuple									=torch.tensor([2.5_273, 0.7_127, 1.8_848]    )
							elif model_name == "timesformer-hr-finetuned-ssv2":
														a__:							List[Any]									=torch.Size([1, 174]    )
														a__:							Dict									=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]  ,			__magic_name__  ,			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(__magic_name__    )
														model.save_pretrained(__magic_name__    )

							if push_to_hub:
														print("Pushing to the hub..."    )
														model.push_to_hub(F"fcakyon/{model_name}"    )


if __name__ == "__main__":
		__UpperCAmelCase    	=  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.'''
		)

		__UpperCAmelCase    	=  parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)













"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




