




"""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__						(  lowercase_    :  Optional[int]	)     ->       Any:
   __lowerCamelCase								=				TimesformerConfig()

   if "large" in model_name:
      __lowerCamelCase								=				96

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

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

   return config



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

   return name



def 			A__						(  lowercase_    :  Union[str, Any]			,   lowercase_    :  List[str]	)     ->       Union[str, Any]:
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase								=				orig_state_dict.pop(UpperCamelCase__	)

      if key.startswith('model.'	):
         __lowerCamelCase								=				key.replace('model.'			,   ''	)

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

   return orig_state_dict



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



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  Optional[Any]			,   lowercase_    :  Optional[int]			,   lowercase_    :  Tuple	)     ->       List[Any]:
   __lowerCamelCase								=				get_timesformer_config(UpperCamelCase__	)

   __lowerCamelCase								=				TimesformerForVideoClassification(UpperCamelCase__	)

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

   model.load_state_dict(UpperCamelCase__	)
   model.eval()

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

   __lowerCamelCase								=				model(**UpperCamelCase__	)
   __lowerCamelCase								=				outputs.logits

   __lowerCamelCase								=				[
       # 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								=				torch.Size([1, 400]	)
      __lowerCamelCase								=				torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]	)
   elif model_name == "timesformer-base-finetuned-k600":
      __lowerCamelCase								=				torch.Size([1, 600]	)
      __lowerCamelCase								=				torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]	)
   elif model_name == "timesformer-base-finetuned-ssv2":
      __lowerCamelCase								=				torch.Size([1, 174]	)
      __lowerCamelCase								=				torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]	)
   elif model_name == "timesformer-large-finetuned-k400":
      __lowerCamelCase								=				torch.Size([1, 400]	)
      __lowerCamelCase								=				torch.tensor([0, 0, 0]	)
   elif model_name == "timesformer-large-finetuned-k600":
      __lowerCamelCase								=				torch.Size([1, 600]	)
      __lowerCamelCase								=				torch.tensor([0, 0, 0]	)
   elif model_name == "timesformer-large-finetuned-ssv2":
      __lowerCamelCase								=				torch.Size([1, 174]	)
      __lowerCamelCase								=				torch.tensor([0, 0, 0]	)
   elif model_name == "timesformer-hr-finetuned-k400":
      __lowerCamelCase								=				torch.Size([1, 400]	)
      __lowerCamelCase								=				torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]	)
   elif model_name == "timesformer-hr-finetuned-k600":
      __lowerCamelCase								=				torch.Size([1, 600]	)
      __lowerCamelCase								=				torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]	)
   elif model_name == "timesformer-hr-finetuned-ssv2":
      __lowerCamelCase								=				torch.Size([1, 174]	)
      __lowerCamelCase								=				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]			,   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__":
     _SCREAMING_SNAKE_CASE  :		int        =      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.'''
     )

     _SCREAMING_SNAKE_CASE  :		str        =      parser.parse_args()
     convert_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )







"""simple docstring"""







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode



def 			A__						(  lowercase_    :  Dict			,   **lowercase_    :  Union[str, Any]	)     ->       Dict:
   return RunBeamCommand(
       args.dataset			,   args.name			,   args.cache_dir			,   args.beam_pipeline_options			,   args.data_dir			,   args.all_configs			,   args.save_info or args.save_infos			,   args.ignore_verifications			,   args.force_redownload			,   **lowercase_			,   )






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   @staticmethod
   def       _A							(  _SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				parser.add_parser('run_beam'   ,							help='Run a Beam dataset processing pipeline'  )
      run_beam_parser.add_argument('dataset'   ,							type=_SCREAMING_SNAKE_CASE   ,							help='Name of the dataset to download'  )
      run_beam_parser.add_argument('--name'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Dataset config name'  )
      run_beam_parser.add_argument(
          '--cache_dir'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Cache directory where the datasets are stored'   ,							)
      run_beam_parser.add_argument(
          '--beam_pipeline_options'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=''   ,							help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'   ,							)
      run_beam_parser.add_argument(
          '--data_dir'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Can be used to specify a manual directory to get the files from'   ,							)
      run_beam_parser.add_argument('--all_configs'   ,							action='store_true'   ,							help='Test all dataset configurations'  )
      run_beam_parser.add_argument('--save_info'   ,							action='store_true'   ,							help='Save the dataset infos file'  )
      run_beam_parser.add_argument(
          '--ignore_verifications'   ,							action='store_true'   ,							help='Run the test without checksums and splits checks'  )
      run_beam_parser.add_argument('--force_redownload'   ,							action='store_true'   ,							help='Force dataset redownload'  )
      # aliases
      run_beam_parser.add_argument('--save_infos'   ,							action='store_true'   ,							help='alias for save_info'  )
      run_beam_parser.set_defaults(func=_SCREAMING_SNAKE_CASE  )







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				dataset
      __lowerCamelCase								=				name
      __lowerCamelCase								=				cache_dir
      __lowerCamelCase								=				beam_pipeline_options
      __lowerCamelCase								=				data_dir
      __lowerCamelCase								=				all_configs
      __lowerCamelCase								=				save_infos
      __lowerCamelCase								=				ignore_verifications
      __lowerCamelCase								=				force_redownload
      __lowerCamelCase								=				config_kwargs







   def       _A							(  self  ):


      '''simple docstring'''

      import apache_beam as beam

      if self._name is not None and self._all_configs:
         print('Both parameters `name` and `all_configs` can\'t be used at once.'  )
         exit(1  )
      __lowerCamelCase		,__lowerCamelCase								=				self._dataset, self._name
      __lowerCamelCase								=				dataset_module_factory(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				import_main_class(dataset_module.module_path  )
      __lowerCamelCase								=				[]
      if self._beam_pipeline_options:
         __lowerCamelCase								=				beam.options.pipeline_options.PipelineOptions(
             flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','  ) if opt]  )
      else:
         __lowerCamelCase								=				None
      if self._all_configs and len(builder_cls.BUILDER_CONFIGS  ) > 0:
         for builder_config in builder_cls.BUILDER_CONFIGS:
            builders.append(
                builder_cls(
                    config_name=builder_config.name   ,							data_dir=self._data_dir   ,							hash=dataset_module.hash   ,							beam_options=_SCREAMING_SNAKE_CASE   ,							cache_dir=self._cache_dir   ,							base_path=dataset_module.builder_kwargs.get('base_path'  )   ,							)  )
      else:
         builders.append(
             builder_cls(
                 config_name=_SCREAMING_SNAKE_CASE   ,							data_dir=self._data_dir   ,							beam_options=_SCREAMING_SNAKE_CASE   ,							cache_dir=self._cache_dir   ,							base_path=dataset_module.builder_kwargs.get('base_path'  )   ,							**self._config_kwargs   ,							)  )

      for builder in builders:
         builder.download_and_prepare(
             download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
             if not self._force_redownload
             else DownloadMode.FORCE_REDOWNLOAD   ,							download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH  )   ,							verification_mode=VerificationMode.NO_CHECKS
             if self._ignore_verifications
             else VerificationMode.ALL_CHECKS   ,							try_from_hf_gcs=_SCREAMING_SNAKE_CASE   ,							)
         if self._save_infos:
            builder._save_infos()

      print('Apache beam run successful.'  )

      # If save_infos=True, the dataset infos file is created next to the loaded module file.
      # Let's move it to the original directory of the dataset script, to allow the user to
      # upload them on S3 at the same time afterwards.
      if self._save_infos:
         __lowerCamelCase								=				os.path.join(builder_cls.get_imported_module_dir()   ,							config.DATASETDICT_INFOS_FILENAME  )

         __lowerCamelCase								=				Path(_SCREAMING_SNAKE_CASE  ).name + '.py'

         __lowerCamelCase								=				os.path.join(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
         if os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				os.path.dirname(_SCREAMING_SNAKE_CASE  )
         elif os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				path
         else:  # in case of a remote dataset
            print(F'''Dataset Infos file saved at {dataset_infos_path}'''  )
            exit(1  )

         # Move datasetinfo back to the user
         __lowerCamelCase								=				os.path.join(_SCREAMING_SNAKE_CASE   ,							config.DATASETDICT_INFOS_FILENAME  )
         copyfile(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
         print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''  )






