'''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 __a			(						__lowerCamelCase      :    Optional[Any]					)						->				Optional[Any]:
      '''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      :    int					)						->				Any:
      '''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      :    Tuple			,		__lowerCamelCase      :    int					)						->				Any:
      '''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			(						)						->				List[Any]:
      '''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      :    Any			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Any					)						->				List[str]:
      '''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([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]					)
      elif model_name == "timesformer-base-finetuned-k600":
            lowercase_     			=   torch.Size([1, 600]					)
            lowercase_     			=   torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]					)
      elif model_name == "timesformer-base-finetuned-ssv2":
            lowercase_     			=   torch.Size([1, 174]					)
            lowercase_     			=   torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]					)
      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([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]					)
      elif model_name == "timesformer-hr-finetuned-k600":
            lowercase_     			=   torch.Size([1, 600]					)
            lowercase_     			=   torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]					)
      elif model_name == "timesformer-hr-finetuned-ssv2":
            lowercase_     			=   torch.Size([1, 174]					)
            lowercase_     			=   torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]					)
      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__":
       lowerCAmelCase_			:			Tuple    	=   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_			:			List[Any]    	=   parser.parse_args()
       convert_timesformer_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
       )







'''simple docstring'''

from hashlib import mda, shaaaa

lowerCAmelCase_			:			Union[str, Any]    	=   (shaaaa, mda)





class 				lowercase    :


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						int = 8)   ->       None:
         lowercase_     			=   0B0
         lowercase_     			=   size


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						str)   ->       None:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         self.bitarray |= h


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       bool:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         return (h & self.bitarray) == h


   def __contains__( self					:						str ,		__lowerCAmelCase					:						str)   ->       bool:
         return self.exists(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						int)   ->       str:
         lowercase_     			=   bin(__lowerCAmelCase)[2:]
         return res.zfill(self.size)


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       str:
         return self.format_bin(self.bitarray)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str)   ->       int:
         lowercase_     			=   0B0
         for func in HASH_FUNCTIONS:
               lowercase_     			=   (
                   int.from_bytes(func(value.encode()).digest() ,		"little") % self.size
               )
               res |= 2**position
         return res


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       str:
         return self.format_bin(self.hash_(__lowerCAmelCase))


   @property
   def __UpperCAmelCase							( self					:						str)   ->       float:
         lowercase_     			=   bin(self.bitarray).count("1")
         return (n_ones / self.size) ** len(__lowerCAmelCase)






