'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Dict  					=       TimesformerConfig()

     if "large" in model_name:
          lowercase_       :							Dict  					=       96

     if "hr" in model_name:
          lowercase_       :							Tuple  					=       16
          lowercase_       :							List[Any]  					=       448

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

     return config

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):






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

     return name

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):






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

          if key.startswith('model.'				):
               lowercase_       :							Optional[int]  					=       key.replace('model.'	,							''				)

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

     return orig_state_dict

def _UpperCamelCase (							):
     lowercase_       :							int  					=       hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'	,							filename='eating_spaghetti.npy'	,							repo_type='dataset'				)
     lowercase_       :							Tuple  					=       np.load(__lowerCamelCase				)
     return list(__lowerCamelCase				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							str  					=       get_timesformer_config(__lowerCamelCase				)

     lowercase_       :							Optional[int]  					=       TimesformerForVideoClassification(__lowerCamelCase				)

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

     model.load_state_dict(__lowerCamelCase				)
     model.eval()

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

     lowercase_       :							Optional[Any]  					=       model(**__lowerCamelCase				)
     lowercase_       :							Dict  					=       outputs.logits

     lowercase_       :							Tuple  					=       [
         # 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_       :							Any  					=       torch.Size([1, 400]				)
          lowercase_       :							Any  					=       torch.tensor([-0.3016, -0.7713, -0.4205]				)
     elif model_name == "timesformer-base-finetuned-k600":
          lowercase_       :							Optional[Any]  					=       torch.Size([1, 600]				)
          lowercase_       :							str  					=       torch.tensor([-0.7267, -0.7466, 3.2404]				)
     elif model_name == "timesformer-base-finetuned-ssv2":
          lowercase_       :							Tuple  					=       torch.Size([1, 174]				)
          lowercase_       :							List[Any]  					=       torch.tensor([-0.9059, 0.6433, -3.1457]				)
     elif model_name == "timesformer-large-finetuned-k400":
          lowercase_       :							str  					=       torch.Size([1, 400]				)
          lowercase_       :							Any  					=       torch.tensor([0, 0, 0]				)
     elif model_name == "timesformer-large-finetuned-k600":
          lowercase_       :							Union[str, Any]  					=       torch.Size([1, 600]				)
          lowercase_       :							Optional[int]  					=       torch.tensor([0, 0, 0]				)
     elif model_name == "timesformer-large-finetuned-ssv2":
          lowercase_       :							Any  					=       torch.Size([1, 174]				)
          lowercase_       :							Dict  					=       torch.tensor([0, 0, 0]				)
     elif model_name == "timesformer-hr-finetuned-k400":
          lowercase_       :							Union[str, Any]  					=       torch.Size([1, 400]				)
          lowercase_       :							List[Any]  					=       torch.tensor([-0.9617, -3.7311, -3.7708]				)
     elif model_name == "timesformer-hr-finetuned-k600":
          lowercase_       :							Optional[Any]  					=       torch.Size([1, 600]				)
          lowercase_       :							Optional[int]  					=       torch.tensor([2.5273, 0.7127, 1.8848]				)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          lowercase_       :							Tuple  					=       torch.Size([1, 174]				)
          lowercase_       :							Tuple  					=       torch.tensor([-3.6756, -0.7513, 0.7180]				)
     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__":
 _A										= 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										= parser.parse_args()
 convert_timesformer_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
 )








'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






     if not all((nth_term, start_term_a, common_ratio_r)					):
          return []
     _a            =		[]
     _a            =		1
     _a            =		common_ratio_r
     for _ in range(int(__lowerCamelCase					)					):
          if not series:
               series.append(__lowerCamelCase					)
          else:
               power += 1
               series.append(float(start_term_a * multiple					)					)
               _a            =		pow(float(__lowerCamelCase					)   ,	__lowerCamelCase					)
     return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		float(
       input("Enter the common ratio between two terms (r) of the Geometric Series")
   )
   print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

