

"""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   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   TimesformerConfig()

					if "large" in model_name:
										A     =   96

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

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

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

					return name
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					for key in orig_state_dict.copy().keys():
										A     =   orig_state_dict.pop(lowercase__ )

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

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

					return orig_state_dict
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video"     ,				filename="eating_spaghetti.npy"     ,				repo_type="dataset" )
					A     =   np.load(lowercase__ )
					return list(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   get_timesformer_config(lowercase__ )

					A     =   TimesformerForVideoClassification(lowercase__ )

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

					model.load_state_dict(lowercase__ )
					model.eval()

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

					A     =   model(**lowercase__ )
					A     =   outputs.logits

					A     =   [
					    # 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":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([-0.30_16, -0.77_13, -0.42_05] )
					elif model_name == "timesformer-base-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([-0.72_67, -0.74_66, 3.24_04] )
					elif model_name == "timesformer-base-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([-0.90_59, 0.64_33, -3.14_57] )
					elif model_name == "timesformer-large-finetuned-k400":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-large-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-large-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-hr-finetuned-k400":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([-0.96_17, -3.73_11, -3.77_08] )
					elif model_name == "timesformer-hr-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([2.52_73, 0.71_27, 1.88_48] )
					elif model_name == "timesformer-hr-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([-3.67_56, -0.75_13, 0.71_80] )
					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__":
	__A	:       Union[str, Any]        =						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	:       int        =						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 shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


_lowerCamelCase	:      Optional[Any]   =							logging.get_logger(__name__)

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







				def __init__(				self :       str   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Any=None   ,  _lowerCamelCase :       List[str]="<s>"   ,  _lowerCamelCase :       int="</s>"   ,  _lowerCamelCase :       Any="</s>"   ,  _lowerCamelCase :       Optional[int]="<s>"   ,  _lowerCamelCase :       List[Any]="<unk>"   ,  _lowerCamelCase :       Optional[int]="<pad>"   ,  **_lowerCamelCase :       List[Any]   ,  ):



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		kwargs.get('''additional_special_tokens'''   ,  []							)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a							) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''							)

					if not os.path.isdir(_lowerCamelCase							):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""							)
						return
					A_	:					Tuple						 =		os.path.join(
					    _lowerCamelCase   ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

					if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
						copyfile(self.vocab_file   ,  _lowerCamelCase							)

					return (out_vocab_file,)





