






"""simple docstring"""





import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
				"""simple docstring"""

				UpperCamelCase__         =    VideoMAEConfig()

				set_architecture_configs(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

				if "finetuned" not in model_name:
								UpperCamelCase__         =    False

				if "finetuned" in model_name:
								UpperCamelCase__         =    'huggingface/label-files'
								if "kinetics" in model_name:
												UpperCamelCase__         =    4_00
												UpperCamelCase__         =    'kinetics400-id2label.json'
								elif "ssv2" in model_name:
												UpperCamelCase__         =    1_74
												UpperCamelCase__         =    'something-something-v2-id2label.json'
								else:
												raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'					)
								UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
								UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
								UpperCamelCase__         =    idalabel
								UpperCamelCase__         =    {v: k for k, v in idalabel.items()}

				return config







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[int]:
				"""simple docstring"""

				if "small" in model_name:
								UpperCamelCase__         =    3_84
								UpperCamelCase__         =    15_36
								UpperCamelCase__         =    12
								UpperCamelCase__         =    16
								UpperCamelCase__         =    12
								UpperCamelCase__         =    3
								UpperCamelCase__         =    1_92
								UpperCamelCase__         =    7_68
				elif "large" in model_name:
								UpperCamelCase__         =    10_24
								UpperCamelCase__         =    40_96
								UpperCamelCase__         =    24
								UpperCamelCase__         =    16
								UpperCamelCase__         =    12
								UpperCamelCase__         =    8
								UpperCamelCase__         =    5_12
								UpperCamelCase__         =    20_48
				elif "huge" in model_name:
								UpperCamelCase__         =    12_80
								UpperCamelCase__         =    51_20
								UpperCamelCase__         =    32
								UpperCamelCase__         =    16
								UpperCamelCase__         =    12
								UpperCamelCase__         =    8
								UpperCamelCase__         =    6_40
								UpperCamelCase__         =    25_60
				elif "base" not in model_name:
								raise ValueError('Model name should include either "small", "base", "large", or "huge"'					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	List[str]:
				"""simple docstring"""

				if "encoder." in name:
								UpperCamelCase__         =    name.replace('encoder.' ,	''					)
				if "cls_token" in name:
								UpperCamelCase__         =    name.replace('cls_token' ,	'videomae.embeddings.cls_token'					)
				if "decoder_pos_embed" in name:
								UpperCamelCase__         =    name.replace('decoder_pos_embed' ,	'decoder.decoder_pos_embed'					)
				if "pos_embed" in name and "decoder" not in name:
								UpperCamelCase__         =    name.replace('pos_embed' ,	'videomae.embeddings.position_embeddings'					)
				if "patch_embed.proj" in name:
								UpperCamelCase__         =    name.replace('patch_embed.proj' ,	'videomae.embeddings.patch_embeddings.projection'					)
				if "patch_embed.norm" in name:
								UpperCamelCase__         =    name.replace('patch_embed.norm' ,	'videomae.embeddings.norm'					)
				if "decoder.blocks" in name:
								UpperCamelCase__         =    name.replace('decoder.blocks' ,	'decoder.decoder_layers'					)
				if "blocks" in name:
								UpperCamelCase__         =    name.replace('blocks' ,	'videomae.encoder.layer'					)
				if "attn.proj" in name:
								UpperCamelCase__         =    name.replace('attn.proj' ,	'attention.output.dense'					)
				if "attn" in name and "bias" not in name:
								UpperCamelCase__         =    name.replace('attn' ,	'attention.self'					)
				if "attn" in name:
								UpperCamelCase__         =    name.replace('attn' ,	'attention.attention'					)
				if "norm1" 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 "decoder_embed" in name:
								UpperCamelCase__         =    name.replace('decoder_embed' ,	'decoder.decoder_embed'					)
				if "decoder_norm" in name:
								UpperCamelCase__         =    name.replace('decoder_norm' ,	'decoder.decoder_norm'					)
				if "decoder_pred" in name:
								UpperCamelCase__         =    name.replace('decoder_pred' ,	'decoder.decoder_pred'					)
				if "norm.weight" in name and "decoder" not in name and "fc" not in name:
								UpperCamelCase__         =    name.replace('norm.weight' ,	'videomae.layernorm.weight'					)
				if "norm.bias" in name and "decoder" not in name and "fc" not in name:
								UpperCamelCase__         =    name.replace('norm.bias' ,	'videomae.layernorm.bias'					)
				if "head" in name and "decoder" not in name:
								UpperCamelCase__         =    name.replace('head' ,	'classifier'					)

				return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
				"""simple docstring"""

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

								if key.startswith('encoder.'					):
												UpperCamelCase__         =    key.replace('encoder.' ,	''					)

								if "qkv" in key:
												UpperCamelCase__         =    key.split('.'					)
												if key.startswith('decoder.blocks'					):
																UpperCamelCase__         =    config.decoder_hidden_size
																UpperCamelCase__         =    int(key_split[2]					)
																UpperCamelCase__         =    'decoder.decoder_layers.'
																if "weight" in key:
																				UpperCamelCase__         =    val[:dim, :]
																				UpperCamelCase__         =    val[dim : dim * 2, :]
																				UpperCamelCase__         =    val[-dim:, :]
												else:
																UpperCamelCase__         =    config.hidden_size
																UpperCamelCase__         =    int(key_split[1]					)
																UpperCamelCase__         =    'videomae.encoder.layer.'
																if "weight" in key:
																				UpperCamelCase__         =    val[:dim, :]
																				UpperCamelCase__         =    val[dim : dim * 2, :]
																				UpperCamelCase__         =    val[-dim:, :]
								else:
												UpperCamelCase__         =    val

				return orig_state_dict







def   lowerCAmelCase_( )					->	Union[str, Any]:
				"""simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
				"""simple docstring"""

				UpperCamelCase__         =    get_videomae_config(SCREAMING_SNAKE_CASE					)

				if "finetuned" in model_name:
								UpperCamelCase__         =    VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE					)
				else:
								UpperCamelCase__         =    VideoMAEForPreTraining(SCREAMING_SNAKE_CASE					)

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

				model.load_state_dict(SCREAMING_SNAKE_CASE					)
				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(SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

				if "finetuned" not in model_name:
								UpperCamelCase__         =    hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' ,	filename='bool_masked_pos.pt'					)
								UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE					)

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

				UpperCamelCase__         =    [
				    'videomae-small-finetuned-kinetics',
				    'videomae-small-finetuned-ssv2',
				    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
				    'videomae-base-short',
				    'videomae-base-short-finetuned-kinetics',
				    'videomae-base',
				    'videomae-base-finetuned-kinetics',
				    'videomae-large',
				    'videomae-large-finetuned-kinetics',
				    'videomae-huge-finetuned-kinetics',
				    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
				    'videomae-base-short-ssv2',
				    'videomae-base-short-finetuned-ssv2',
				    'videomae-base-ssv2',
				    'videomae-base-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 == "videomae-small-finetuned-kinetics":
								UpperCamelCase__         =    torch.Size([1, 4_00]					)
								UpperCamelCase__         =    torch.tensor([-0.9291, -0.4061, -0.9307]					)
				elif model_name == "videomae-small-finetuned-ssv2":
								UpperCamelCase__         =    torch.Size([1, 1_74]					)
								UpperCamelCase__         =    torch.tensor([0.2671, -0.4689, -0.8235]					)
				elif model_name == "videomae-base":
								UpperCamelCase__         =    torch.Size([1, 14_08, 15_36]					)
								UpperCamelCase__         =    torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]					)
				elif model_name == "videomae-base-short":
								UpperCamelCase__         =    torch.Size([1, 14_08, 15_36]					)
								UpperCamelCase__         =    torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]					)
								# we verified the loss both for normalized and unnormalized targets for this one
								UpperCamelCase__         =    torch.tensor([0.5142]					) if config.norm_pix_loss else torch.tensor([0.6469]					)
				elif model_name == "videomae-large":
								UpperCamelCase__         =    torch.Size([1, 14_08, 15_36]					)
								UpperCamelCase__         =    torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]					)
				elif model_name == "videomae-large-finetuned-kinetics":
								UpperCamelCase__         =    torch.Size([1, 4_00]					)
								UpperCamelCase__         =    torch.tensor([0.0771, 0.0011, -0.3625]					)
				elif model_name == "videomae-huge-finetuned-kinetics":
								UpperCamelCase__         =    torch.Size([1, 4_00]					)
								UpperCamelCase__         =    torch.tensor([0.2433, 0.1632, -0.4894]					)
				elif model_name == "videomae-base-short-finetuned-kinetics":
								UpperCamelCase__         =    torch.Size([1, 4_00]					)
								UpperCamelCase__         =    torch.tensor([0.6588, 0.0990, -0.2493]					)
				elif model_name == "videomae-base-finetuned-kinetics":
								UpperCamelCase__         =    torch.Size([1, 4_00]					)
								UpperCamelCase__         =    torch.tensor([0.3669, -0.0688, -0.2421]					)
				elif model_name == "videomae-base-short-ssv2":
								UpperCamelCase__         =    torch.Size([1, 14_08, 15_36]					)
								UpperCamelCase__         =    torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]					)
				elif model_name == "videomae-base-short-finetuned-ssv2":
								UpperCamelCase__         =    torch.Size([1, 1_74]					)
								UpperCamelCase__         =    torch.tensor([-0.0537, -0.1539, -0.3266]					)
				elif model_name == "videomae-base-ssv2":
								UpperCamelCase__         =    torch.Size([1, 14_08, 15_36]					)
								UpperCamelCase__         =    torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]					)
				elif model_name == "videomae-base-finetuned-ssv2":
								UpperCamelCase__         =    torch.Size([1, 1_74]					)
								UpperCamelCase__         =    torch.tensor([0.1961, -0.8337, -0.6389]					)
				else:
								raise ValueError(F'Model name not supported. Should be one of {model_names}'					)

				# verify logits
				assert logits.shape == expected_shape
				if "finetuned" in model_name:
								assert torch.allclose(logits[0, :3] ,	SCREAMING_SNAKE_CASE ,	atol=1E-4					)
				else:
								print('Logits:' ,	logits[0, :3, :3]					)
								assert torch.allclose(logits[0, :3, :3] ,	SCREAMING_SNAKE_CASE ,	atol=1E-4					)
				print('Logits ok!'					)

				# verify loss, if applicable
				if model_name == "videomae-base-short":
								UpperCamelCase__         =    outputs.loss
								assert torch.allclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	atol=1E-4					)
								print('Loss 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(SCREAMING_SNAKE_CASE ,	organization='nielsr'					)


if __name__ == "__main__":
		A__					: Optional[Any]=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--checkpoint_url""",
		    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
		    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="""/Users/nielsrogge/Documents/VideoMAE/Test""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--model_name""", default="""videomae-base""", 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__					: Optional[Any]=	parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












"""simple docstring"""





import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


A__					: Union[str, Any]=	logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests."""  )
@require_torch
@require_tf
@slow
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = True ,				)		->       Tuple:
       UpperCamelCase__         =    [file for file in os.listdir(snake_case_      ) if os.path.isfile(os.path.join(snake_case_ ,				snake_case_      )      )]

       if identifier is not None:
           UpperCamelCase__         =    [file for file in files if identifier in file]

       if n_identifier is not None:
           if isinstance(snake_case_ ,				snake_case_      ):
               for n_ in n_identifier:
                   UpperCamelCase__         =    [file for file in files if n_ not in file]
           else:
               UpperCamelCase__         =    [file for file in files if n_identifier not in file]

       UpperCamelCase__         =    ignore_files or []
       ignore_files.append('__init__.py'      )
       UpperCamelCase__         =    [file for file in files if file not in ignore_files]

       for file in files:
           # Open all files
           print('Testing' ,				snake_case_      )

           if only_modules:
               UpperCamelCase__         =    file.split('.'      )[0]
               try:
                   UpperCamelCase__         =    getattr(snake_case_ ,				snake_case_      )
                   UpperCamelCase__         =    doctest.DocTestSuite(snake_case_      )
                   UpperCamelCase__         =    unittest.TextTestRunner().run(snake_case_      )
                   self.assertIs(len(result.failures      ) ,				0      )
               except AttributeError:
                   logger.info(F'{module_identifier} is not a module.'      )
           else:
               UpperCamelCase__         =    doctest.testfile(str('..' / directory / file      ) ,				optionflags=doctest.ELLIPSIS      )
               self.assertIs(result.failed ,				0      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'modeling'
       UpperCamelCase__         =    [
           'modeling_ctrl.py',
           'modeling_tf_ctrl.py',
       ]
       self.analyze_directory(snake_case_ ,				identifier=snake_case_ ,				ignore_files=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'tokenization'
       self.analyze_directory(snake_case_ ,				identifier=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'configuration'
       self.analyze_directory(snake_case_ ,				identifier=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    ['configuration', 'modeling', 'tokenization']
       self.analyze_directory(snake_case_ ,				n_identifier=snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    Path('docs/source'      )
       UpperCamelCase__         =    ['favicon.ico']
       self.analyze_directory(snake_case_ ,				ignore_files=snake_case_ ,				only_modules=snake_case_      )




