





"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

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

				return name

def     __A      (			a_						:Union[str, Any]   ,       a_						:List[str])		->		Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								__a :						List[str]			       =  orig_state_dict.pop(a_)

								if "qkv" in key:
												__a :						int			       =  key.split('''.''')
												__a :						int			       =  int(key_split[1])
												if "decoder_blocks" in key:
																__a :						int			       =  config.decoder_hidden_size
																__a :						List[Any]			       =  '''decoder.decoder_layers.'''
																if "weight" in key:
																				__a :						List[Any]			       =  val[:dim, :]
																				__a :						Optional[Any]			       =  val[dim : dim * 2, :]
																				__a :						Union[str, Any]			       =  val[-dim:, :]
																elif "bias" in key:
																				__a :						Optional[int]			       =  val[:dim]
																				__a :						Tuple			       =  val[dim : dim * 2]
																				__a :						Union[str, Any]			       =  val[-dim:]
												else:
																__a :						Dict			       =  config.hidden_size
																__a :						Optional[int]			       =  '''vit.encoder.layer.'''
																if "weight" in key:
																				__a :						int			       =  val[:dim, :]
																				__a :						Optional[int]			       =  val[dim : dim * 2, :]
																				__a :						Tuple			       =  val[-dim:, :]
																elif "bias" in key:
																				__a :						int			       =  val[:dim]
																				__a :						Dict			       =  val[dim : dim * 2]
																				__a :						Any			       =  val[-dim:]

								else:
												__a :						Tuple			       =  val

				return orig_state_dict

def     __A      (			a_						:Union[str, Any]   ,       a_						:Optional[Any])		->		Any:
				__a :						Optional[int]			       =  ViTMAEConfig()
				if "large" in checkpoint_url:
								__a :						Optional[int]			       =  10_24
								__a :						Union[str, Any]			       =  40_96
								__a :						Optional[int]			       =  24
								__a :						str			       =  16
				elif "huge" in checkpoint_url:
								__a :						Optional[Any]			       =  14
								__a :						Dict			       =  12_80
								__a :						Union[str, Any]			       =  51_20
								__a :						int			       =  32
								__a :						str			       =  16

				__a :						List[Any]			       =  ViTMAEForPreTraining(a_)

				__a :						Optional[Any]			       =  torch.hub.load_state_dict_from_url(a_   ,       map_location='''cpu''')['''model''']

				__a :						Optional[int]			       =  ViTMAEImageProcessor(size=config.image_size)

				__a :						int			       =  convert_state_dict(a_   ,       a_)

				model.load_state_dict(a_)
				model.eval()

				__a :						int			       =  '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				__a :						Optional[int]			       =  Image.open(requests.get(a_   ,       stream=a_).raw)
				__a :						Optional[int]			       =  ViTMAEImageProcessor(size=config.image_size)
				__a :						str			       =  image_processor(images=a_   ,       return_tensors='''pt''')

				# forward pass
				torch.manual_seed(2)
				__a :						List[Any]			       =  model(**a_)
				__a :						int			       =  outputs.logits

				if "large" in checkpoint_url:
								__a :						Union[str, Any]			       =  torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]])
				elif "huge" in checkpoint_url:
								__a :						str			       =  torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]])
				else:
								__a :						Union[str, Any]			       =  torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]])

				# verify logits
				assert torch.allclose(logits[0, :3, :3]   ,       a_   ,       atol=1e-4)

				print(F"""Saving model to {pytorch_dump_folder_path}""")
				model.save_pretrained(a_)

				print(F"""Saving image processor to {pytorch_dump_folder_path}""")
				image_processor.save_pretrained(a_)


if __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
		    type=str,
		    help='''URL of the checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		A 			=  parser.parse_args()
		convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass