





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   (     a)			->Union[str, 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(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  repo_type="dataset"),  "r"))
					lowerCamelCase__      =							{int(__SCREAMING_SNAKE_CASE): v for k, v in idalabel.items()}
					lowerCamelCase__      =							idalabel
					lowerCamelCase__      =							{v: k for k, v in idalabel.items()}

					return config





def  __UpperCamelCase   (     a)			->Any:

					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  __UpperCamelCase   (     a,  a)			->List[Any]:

					for key in orig_state_dict.copy().keys():
										lowerCamelCase__      =							orig_state_dict.pop(__SCREAMING_SNAKE_CASE)

										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  __UpperCamelCase   (     )			->int:
					lowerCamelCase__      =							hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video",  filename="eating_spaghetti.npy",  repo_type="dataset")
					lowerCamelCase__      =							np.load(__SCREAMING_SNAKE_CASE)
					return list(__SCREAMING_SNAKE_CASE)





def  __UpperCamelCase   (     a,  a,  a,  a)			->Optional[Any]:
					lowerCamelCase__      =							get_timesformer_config(__SCREAMING_SNAKE_CASE)

					lowerCamelCase__      =							TimesformerForVideoClassification(__SCREAMING_SNAKE_CASE)

					# download original checkpoint, hosted on Google Drive
					lowerCamelCase__      =							"""pytorch_model.bin"""
					gdown.cached_download(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  quiet=__SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							torch.load(__SCREAMING_SNAKE_CASE,  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(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)

					model.load_state_dict(__SCREAMING_SNAKE_CASE)
					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(**__SCREAMING_SNAKE_CASE)
					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],  __SCREAMING_SNAKE_CASE,  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(__SCREAMING_SNAKE_CASE)
										model.save_pretrained(__SCREAMING_SNAKE_CASE)

					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 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=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))



