






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]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						TimesformerConfig()

 if "large" in model_name:
  SCREAMING_SNAKE_CASE							  =						9_6

 if "hr" in model_name:
  SCREAMING_SNAKE_CASE							  =						1_6
  SCREAMING_SNAKE_CASE							  =						4_4_8

 SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
 if "k400" in model_name:
  SCREAMING_SNAKE_CASE							  =						4_0_0
  SCREAMING_SNAKE_CASE							  =						'kinetics400-id2label.json'
 elif "k600" in model_name:
  SCREAMING_SNAKE_CASE							  =						6_0_0
  SCREAMING_SNAKE_CASE							  =						'kinetics600-id2label.json'
 elif "ssv2" in model_name:
  SCREAMING_SNAKE_CASE							  =						1_7_4
  SCREAMING_SNAKE_CASE							  =						'something-something-v2-id2label.json'
 else:
  raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'	)
 SCREAMING_SNAKE_CASE							  =						json.load(open(hf_hub_download(lowercase_					,       lowercase_					,       repo_type='dataset'	)					,       'r'	)	)
 SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE							  =						idalabel
 SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}

 return config





def 		A_			(		lowercase_	)							->Any:







 """simple docstring"""

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

 return name





def 		A_			(		lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE							  =						orig_state_dict.pop(lowercase_	)

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

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

 return orig_state_dict





def 		A_			(		)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'					,       filename='eating_spaghetti.npy'					,       repo_type='dataset'	)
 SCREAMING_SNAKE_CASE							  =						np.load(lowercase_	)
 return list(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						get_timesformer_config(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						TimesformerForVideoClassification(lowercase_	)

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

 model.load_state_dict(lowercase_	)
 model.eval()

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

 SCREAMING_SNAKE_CASE							  =						model(**lowercase_	)
 SCREAMING_SNAKE_CASE							  =						outputs.logits

 SCREAMING_SNAKE_CASE							  =						[
     # 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":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]	)
 elif model_name == "timesformer-base-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]	)
 elif model_name == "timesformer-base-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]	)
 elif model_name == "timesformer-large-finetuned-k400":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-large-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-large-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-hr-finetuned-k400":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]	)
 elif model_name == "timesformer-hr-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]	)
 elif model_name == "timesformer-hr-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						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]					,       lowercase_					,       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(lowercase_	)
  model.save_pretrained(lowercase_	)

 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
      )












from datetime import date

import requests

__UpperCAmelCase      				=							"https://api.carbonintensity.org.uk/intensity"





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						requests.get(lowercase_	).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def 		A_			(		lowercase_					,       lowercase_	)							->list:







 """simple docstring"""

 return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
            print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'{fetch_last_half_hour() = }')




