
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     (   __UpperCamelCase       :  List[str]    ):
   lowerCAmelCase__ :    int				      =			TimesformerConfig()

   if "large" in model_name:
      lowerCAmelCase__ :    Optional[int]				      =			96

   if "hr" in model_name:
      lowerCAmelCase__ :    List[str]				      =			16
      lowerCAmelCase__ :    Optional[int]				      =			448

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

   return config



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

   return name



def      _a     (   __UpperCamelCase       :  Dict	,__UpperCamelCase       :  Optional[Any]    ):
   for key in orig_state_dict.copy().keys():
      lowerCAmelCase__ :    Dict				      =			orig_state_dict.pop(__UpperCamelCase    )

      if key.startswith('''model.'''    ):
         lowerCAmelCase__ :    List[str]				      =			key.replace('''model.'''	,''''''    )

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

   return orig_state_dict



def      _a     (   ):
   lowerCAmelCase__ :    int				      =			hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''	,filename='''eating_spaghetti.npy'''	,repo_type='''dataset'''    )
   lowerCAmelCase__ :    Union[str, Any]				      =			np.load(__UpperCamelCase    )
   return list(__UpperCamelCase    )



def      _a     (   __UpperCamelCase       :  Dict	,__UpperCamelCase       :  int	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  int    ):
   lowerCAmelCase__ :    str				      =			get_timesformer_config(__UpperCamelCase    )

   lowerCAmelCase__ :    int				      =			TimesformerForVideoClassification(__UpperCamelCase    )

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

   model.load_state_dict(__UpperCamelCase    )
   model.eval()

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

   lowerCAmelCase__ :    List[Any]				      =			model(**__UpperCamelCase    )
   lowerCAmelCase__ :    List[Any]				      =			outputs.logits

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

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


if __name__ == "__main__":
       A__       :   List[str]   						=					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
       )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



