
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 	a_		(	__lowercase       :     str			)			->  Dict:
			_snake_case          =      VideoMAEConfig()

			set_architecture_configs(__lowercase	, __lowercase			)

			if "finetuned" not in model_name:
						_snake_case          =      False

			if "finetuned" in model_name:
						_snake_case          =      'huggingface/label-files'
						if "kinetics" in model_name:
									_snake_case          =      400
									_snake_case          =      'kinetics400-id2label.json'
						elif "ssv2" in model_name:
									_snake_case          =      174
									_snake_case          =      'something-something-v2-id2label.json'
						else:
									raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'			)
						_snake_case          =      json.load(open(hf_hub_download(__lowercase	, __lowercase	, repo_type='dataset'			)	, 'r'			)			)
						_snake_case          =      {int(__lowercase			): v for k, v in idalabel.items()}
						_snake_case          =      idalabel
						_snake_case          =      {v: k for k, v in idalabel.items()}

			return config






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     Dict			)			->  Dict:
			if "small" in model_name:
						_snake_case          =      384
						_snake_case          =      1_536
						_snake_case          =      12
						_snake_case          =      16
						_snake_case          =      12
						_snake_case          =      3
						_snake_case          =      192
						_snake_case          =      768
			elif "large" in model_name:
						_snake_case          =      1_024
						_snake_case          =      4_096
						_snake_case          =      24
						_snake_case          =      16
						_snake_case          =      12
						_snake_case          =      8
						_snake_case          =      512
						_snake_case          =      2_048
			elif "huge" in model_name:
						_snake_case          =      1_280
						_snake_case          =      5_120
						_snake_case          =      32
						_snake_case          =      16
						_snake_case          =      12
						_snake_case          =      8
						_snake_case          =      640
						_snake_case          =      2_560
			elif "base" not in model_name:
						raise ValueError('Model name should include either "small", "base", "large", or "huge"'			)






def 	a_		(	__lowercase       :     Optional[Any]			)			->  List[str]:
			if "encoder." in name:
						_snake_case          =      name.replace('encoder.'	, ''			)
			if "cls_token" in name:
						_snake_case          =      name.replace('cls_token'	, 'videomae.embeddings.cls_token'			)
			if "decoder_pos_embed" in name:
						_snake_case          =      name.replace('decoder_pos_embed'	, 'decoder.decoder_pos_embed'			)
			if "pos_embed" in name and "decoder" not in name:
						_snake_case          =      name.replace('pos_embed'	, 'videomae.embeddings.position_embeddings'			)
			if "patch_embed.proj" in name:
						_snake_case          =      name.replace('patch_embed.proj'	, 'videomae.embeddings.patch_embeddings.projection'			)
			if "patch_embed.norm" in name:
						_snake_case          =      name.replace('patch_embed.norm'	, 'videomae.embeddings.norm'			)
			if "decoder.blocks" in name:
						_snake_case          =      name.replace('decoder.blocks'	, 'decoder.decoder_layers'			)
			if "blocks" in name:
						_snake_case          =      name.replace('blocks'	, 'videomae.encoder.layer'			)
			if "attn.proj" in name:
						_snake_case          =      name.replace('attn.proj'	, 'attention.output.dense'			)
			if "attn" in name and "bias" not in name:
						_snake_case          =      name.replace('attn'	, 'attention.self'			)
			if "attn" in name:
						_snake_case          =      name.replace('attn'	, 'attention.attention'			)
			if "norm1" in name:
						_snake_case          =      name.replace('norm1'	, 'layernorm_before'			)
			if "norm2" in name:
						_snake_case          =      name.replace('norm2'	, 'layernorm_after'			)
			if "mlp.fc1" in name:
						_snake_case          =      name.replace('mlp.fc1'	, 'intermediate.dense'			)
			if "mlp.fc2" in name:
						_snake_case          =      name.replace('mlp.fc2'	, 'output.dense'			)
			if "decoder_embed" in name:
						_snake_case          =      name.replace('decoder_embed'	, 'decoder.decoder_embed'			)
			if "decoder_norm" in name:
						_snake_case          =      name.replace('decoder_norm'	, 'decoder.decoder_norm'			)
			if "decoder_pred" in name:
						_snake_case          =      name.replace('decoder_pred'	, 'decoder.decoder_pred'			)
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						_snake_case          =      name.replace('norm.weight'	, 'videomae.layernorm.weight'			)
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						_snake_case          =      name.replace('norm.bias'	, 'videomae.layernorm.bias'			)
			if "head" in name and "decoder" not in name:
						_snake_case          =      name.replace('head'	, 'classifier'			)

			return name






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Tuple			)			->  Union[str, Any]:
			for key in orig_state_dict.copy().keys():
						_snake_case          =      orig_state_dict.pop(__lowercase			)

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

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

			return orig_state_dict






def 	a_		(	)			->  str:
			_snake_case          =      hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video'	, filename='eating_spaghetti.npy'	, repo_type='dataset'			)
			_snake_case          =      np.load(__lowercase			)
			return list(__lowercase			)






def 	a_		(	__lowercase       :     Any	, __lowercase       :     Optional[int]	, __lowercase       :     Union[str, Any]	, __lowercase       :     Any			)			->  Tuple:
			_snake_case          =      get_videomae_config(__lowercase			)

			if "finetuned" in model_name:
						_snake_case          =      VideoMAEForVideoClassification(__lowercase			)
			else:
						_snake_case          =      VideoMAEForPreTraining(__lowercase			)

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

			model.load_state_dict(__lowercase			)
			model.eval()

			# verify model on basic input
			_snake_case          =      VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	, image_std=[0.5, 0.5, 0.5]			)
			_snake_case          =      prepare_video()
			_snake_case          =      image_processor(__lowercase	, return_tensors='pt'			)

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

			_snake_case          =      model(**__lowercase			)
			_snake_case          =      outputs.logits

			_snake_case          =      [
			    '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":
						_snake_case          =      torch.Size([1, 400]			)
						_snake_case          =      torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]			)
			elif model_name == "videomae-small-finetuned-ssv2":
						_snake_case          =      torch.Size([1, 174]			)
						_snake_case          =      torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]			)
			elif model_name == "videomae-base":
						_snake_case          =      torch.Size([1, 1_408, 1_536]			)
						_snake_case          =      torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]			)
			elif model_name == "videomae-base-short":
						_snake_case          =      torch.Size([1, 1_408, 1_536]			)
						_snake_case          =      torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]			)
						# we verified the loss both for normalized and unnormalized targets for this one
						_snake_case          =      torch.tensor([0.5_1_4_2]			) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]			)
			elif model_name == "videomae-large":
						_snake_case          =      torch.Size([1, 1_408, 1_536]			)
						_snake_case          =      torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]			)
			elif model_name == "videomae-large-finetuned-kinetics":
						_snake_case          =      torch.Size([1, 400]			)
						_snake_case          =      torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]			)
			elif model_name == "videomae-huge-finetuned-kinetics":
						_snake_case          =      torch.Size([1, 400]			)
						_snake_case          =      torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]			)
			elif model_name == "videomae-base-short-finetuned-kinetics":
						_snake_case          =      torch.Size([1, 400]			)
						_snake_case          =      torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]			)
			elif model_name == "videomae-base-finetuned-kinetics":
						_snake_case          =      torch.Size([1, 400]			)
						_snake_case          =      torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]			)
			elif model_name == "videomae-base-short-ssv2":
						_snake_case          =      torch.Size([1, 1_408, 1_536]			)
						_snake_case          =      torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]			)
			elif model_name == "videomae-base-short-finetuned-ssv2":
						_snake_case          =      torch.Size([1, 174]			)
						_snake_case          =      torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]			)
			elif model_name == "videomae-base-ssv2":
						_snake_case          =      torch.Size([1, 1_408, 1_536]			)
						_snake_case          =      torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]			)
			elif model_name == "videomae-base-finetuned-ssv2":
						_snake_case          =      torch.Size([1, 174]			)
						_snake_case          =      torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]			)
			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]	, __lowercase	, atol=1E-4			)
			else:
						print('Logits:'	, logits[0, :3, :3]			)
						assert torch.allclose(logits[0, :3, :3]	, __lowercase	, atol=1E-4			)
			print('Logits ok!'			)

			# verify loss, if applicable
			if model_name == "videomae-base-short":
						_snake_case          =      outputs.loss
						assert torch.allclose(__lowercase	, __lowercase	, 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(__lowercase			)
						model.save_pretrained(__lowercase			)

			if push_to_hub:
						print('Pushing to the hub...'			)
						model.push_to_hub(__lowercase	, organization='nielsr'			)


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      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.'''
			)

			_lowerCamelCase	:    Optional[int]          =      parser.parse_args()
			convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
				    'de': [
				        'Maschinelles Lernen ist großartig, oder?',
				        'Ich esse gerne Bananen',
				        'Morgen ist wieder ein toller Tag!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
				testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )