


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				:Tuple					)->						Optional[Any]:
   UpperCamelCase__   =    TimesformerConfig()

   if "large" in model_name:
      UpperCamelCase__   =    96

   if "hr" in model_name:
      UpperCamelCase__   =    16
      UpperCamelCase__   =    4_48

   UpperCamelCase__   =    "huggingface/label-files"
   if "k400" in model_name:
      UpperCamelCase__   =    4_00
      UpperCamelCase__   =    "kinetics400-id2label.json"
   elif "k600" in model_name:
      UpperCamelCase__   =    6_00
      UpperCamelCase__   =    "kinetics600-id2label.json"
   elif "ssv2" in model_name:
      UpperCamelCase__   =    1_74
      UpperCamelCase__   =    "something-something-v2-id2label.json"
   else:
      raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."					)
   UpperCamelCase__   =    json.load(open(hf_hub_download(_A			,       _A			,       repo_type="dataset"					)			,       "r"					)					)
   UpperCamelCase__   =    {int(_A					): v for k, v in idalabel.items()}
   UpperCamelCase__   =    idalabel
   UpperCamelCase__   =    {v: k for k, v in idalabel.items()}

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

   return name
def     UpperCamelCase_(			_A				:int			,       _A				:List[Any]					)->						List[str]:
   for key in orig_state_dict.copy().keys():
      UpperCamelCase__   =    orig_state_dict.pop(_A					)

      if key.startswith("model."					):
         UpperCamelCase__   =    key.replace("model."			,       ""					)

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

   return orig_state_dict
def     UpperCamelCase_(			)->						Any:
   UpperCamelCase__   =    hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"			,       filename="eating_spaghetti.npy"			,       repo_type="dataset"					)
   UpperCamelCase__   =    np.load(_A					)
   return list(_A					)
def     UpperCamelCase_(			_A				:int			,       _A				:Union[str, Any]			,       _A				:Optional[int]			,       _A				:List[str]					)->						str:
   UpperCamelCase__   =    get_timesformer_config(_A					)

   UpperCamelCase__   =    TimesformerForVideoClassification(_A					)

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

   model.load_state_dict(_A					)
   model.eval()

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

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

   UpperCamelCase__   =    [
       # 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":
      UpperCamelCase__   =    torch.Size([1, 4_00]					)
      UpperCamelCase__   =    torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]					)
   elif model_name == "timesformer-base-finetuned-k600":
      UpperCamelCase__   =    torch.Size([1, 6_00]					)
      UpperCamelCase__   =    torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]					)
   elif model_name == "timesformer-base-finetuned-ssv2":
      UpperCamelCase__   =    torch.Size([1, 1_74]					)
      UpperCamelCase__   =    torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]					)
   elif model_name == "timesformer-large-finetuned-k400":
      UpperCamelCase__   =    torch.Size([1, 4_00]					)
      UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
   elif model_name == "timesformer-large-finetuned-k600":
      UpperCamelCase__   =    torch.Size([1, 6_00]					)
      UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
   elif model_name == "timesformer-large-finetuned-ssv2":
      UpperCamelCase__   =    torch.Size([1, 1_74]					)
      UpperCamelCase__   =    torch.tensor([0, 0, 0]					)
   elif model_name == "timesformer-hr-finetuned-k400":
      UpperCamelCase__   =    torch.Size([1, 4_00]					)
      UpperCamelCase__   =    torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]					)
   elif model_name == "timesformer-hr-finetuned-k600":
      UpperCamelCase__   =    torch.Size([1, 6_00]					)
      UpperCamelCase__   =    torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]					)
   elif model_name == "timesformer-hr-finetuned-ssv2":
      UpperCamelCase__   =    torch.Size([1, 1_74]					)
      UpperCamelCase__   =    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]			,       _A			,       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(_A					)
      model.save_pretrained(_A					)

   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
      )






import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			@staticmethod
			def 			snake_case__			(							snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    parser.add_parser("login" ,  help="Log in using the same credentials as on huggingface.co"       )
						login_parser.set_defaults(func=lambda snake_case       : LoginCommand(snake_case       )       )
						UpperCamelCase__   =    parser.add_parser("whoami" ,  help="Find out which huggingface.co account you are logged in as."       )
						whoami_parser.set_defaults(func=lambda snake_case       : WhoamiCommand(snake_case       )       )
						UpperCamelCase__   =    parser.add_parser("logout" ,  help="Log out"       )
						logout_parser.set_defaults(func=lambda snake_case       : LogoutCommand(snake_case       )       )

						# new system: git-based repo system
						UpperCamelCase__   =    parser.add_parser(
						    "repo" ,  help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos." ,  )
						UpperCamelCase__   =    repo_parser.add_subparsers(
						    help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"       )
						UpperCamelCase__   =    repo_subparsers.add_parser(
						    "create" ,  help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"       )
						repo_create_parser.add_argument(
						    "name" ,  type=snake_case ,  help="Name for your model's repo. Will be namespaced under your username to build the model id." ,  )
						repo_create_parser.add_argument("--organization" ,  type=snake_case ,  help="Optional: organization namespace."       )
						repo_create_parser.add_argument("-y" ,  "--yes" ,  action="store_true" ,  help="Optional: answer Yes to the prompt"       )
						repo_create_parser.set_defaults(func=lambda snake_case       : RepoCreateCommand(snake_case       )       )







class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   int					=						'\u001b[1m'
			_UpperCamelCase						:   List[Any]					=						'\u001b[31m'
			_UpperCamelCase						:   Tuple					=						'\u001b[90m'
			_UpperCamelCase						:   Tuple					=						'\u001b[0m'




			@classmethod
			def 			snake_case__			(							cls ,  snake_case       ):

						'''simple docstring'''


						return F'''{cls._bold}{s}{cls._reset}'''




			@classmethod
			def 			snake_case__			(							cls ,  snake_case       ):

						'''simple docstring'''


						return F'''{cls._bold}{cls._red}{s}{cls._reset}'''







			@classmethod
			def 			snake_case__			(							cls ,  snake_case       ):

						'''simple docstring'''


						return F'''{cls._gray}{s}{cls._reset}'''



def     UpperCamelCase_(			_A				:List[List[Union[str, int]]]			,       _A				:List[str]					)->						str:
			UpperCamelCase__   =    [max(len(str(_A					)					) for x in col					) for col in zip(*_A			,       _A					)]
			UpperCamelCase__   =    ("{{:{}}} " * len(_A					)).format(*_A					)
			UpperCamelCase__   =    []
			lines.append(row_format.format(*_A					)					)
			lines.append(row_format.format(*["-" * w for w in col_widths]					)					)
			for row in rows:
						lines.append(row_format.format(*_A					)					)
			return "\n".join(_A					)







class     lowerCamelCase__		:






			"""simple docstring"""






			def __init__(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    args







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						print(
						    ANSI.red(
						        "ERROR! `huggingface-cli login` uses an outdated login mechanism "
						        "that is not compatible with the Hugging Face Hub backend anymore. "
						        "Please use `huggingface-cli login instead."       )       )







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						print(
						    ANSI.red(
						        "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
						        "`huggingface-cli whoami` instead."       )       )
						UpperCamelCase__   =    HfFolder.get_token()
						if token is None:
									print("Not logged in"       )
									exit()
						try:
									UpperCamelCase__, UpperCamelCase__   =    whoami(snake_case       )
									print(snake_case       )
									if orgs:
												print(ANSI.bold("orgs: "       ) ,  ",".join(snake_case       )       )
						except HTTPError as e:
									print(snake_case       )
									print(ANSI.red(e.response.text       )       )
									exit(1       )







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						print(
						    ANSI.red(
						        "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
						        "that is not compatible with the Hugging Face Hub backend anymore. "
						        "Please use `huggingface-cli logout instead."       )       )







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						print(
						    ANSI.red(
						        "WARNING! Managing repositories through transformers-cli is deprecated. "
						        "Please use `huggingface-cli` instead."       )       )
						UpperCamelCase__   =    HfFolder.get_token()
						if token is None:
									print("Not logged in"       )
									exit(1       )
						try:
									UpperCamelCase__   =    subprocess.check_output(["git", "--version"]       ).decode("utf-8"       )
									print(ANSI.gray(stdout.strip()       )       )
						except FileNotFoundError:
									print("Looks like you do not have git installed, please install."       )

						try:
									UpperCamelCase__   =    subprocess.check_output(["git-lfs", "--version"]       ).decode("utf-8"       )
									print(ANSI.gray(stdout.strip()       )       )
						except FileNotFoundError:
									print(
									    ANSI.red(
									        "Looks like you do not have git-lfs installed, please install."
									        " You can install from https://git-lfs.github.com/."
									        " Then run `git lfs install` (you only have to do this once)."       )       )
						print(""       )

						UpperCamelCase__, UpperCamelCase__   =    whoami(snake_case       )
						UpperCamelCase__   =    self.args.organization if self.args.organization is not None else user
						UpperCamelCase__   =    F'''{namespace}/{self.args.name}'''
						print(F'''You are about to create {ANSI.bold(snake_case       )}'''       )

						if not self.args.yes:
									UpperCamelCase__   =    input("Proceed? [Y/n] "       ).lower()
									if not (choice == "" or choice == "y" or choice == "yes"):
												print("Abort"       )
												exit()
						try:
									UpperCamelCase__   =    create_repo(snake_case ,  name=self.args.name ,  organization=self.args.organization       )
						except HTTPError as e:
									print(snake_case       )
									print(ANSI.red(e.response.text       )       )
									exit(1       )
						print("\nYour repo now lives at:"       )
						print(F'''  {ANSI.bold(snake_case       )}'''       )
						print("\nYou can clone it locally with the command below, and commit/push as usual."       )
						print(F'''\n  git clone {url}'''       )
						print(""       )


