import argparse

import requests
import torch
from PIL import Image

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

						return name
def    A			(					_lowercase	,						_lowercase		):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							orig_state_dict.pop(_lowercase		)

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

												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							val

						return orig_state_dict
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							ViTMAEConfig()
						if "large" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							1_024
												SCREAMING_SNAKE_CASE							:					Any           =							4_096
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							24
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							16
						elif "huge" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:					Any           =							14
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							1_280
												SCREAMING_SNAKE_CASE							:					List[str]           =							5_120
												SCREAMING_SNAKE_CASE							:					Dict           =							32
												SCREAMING_SNAKE_CASE							:					str           =							16

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ViTMAEForPreTraining(_lowercase		)

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.hub.load_state_dict_from_url(_lowercase	,						map_location='''cpu'''		)['''model''']

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

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							convert_state_dict(_lowercase	,						_lowercase		)

						model.load_state_dict(_lowercase		)
						model.eval()

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

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

						# forward pass
						torch.manual_seed(2		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(**_lowercase		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							outputs.logits

						if "large" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:					Any           =							torch.tensor(
												    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]		)
						elif "huge" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(
												    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]		)
						else:
												SCREAMING_SNAKE_CASE							:					Any           =							torch.tensor(
												    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]		)

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

						print(f"""Saving model to {pytorch_dump_folder_path}"""		)
						model.save_pretrained(_lowercase		)

						print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
						image_processor.save_pretrained(_lowercase		)


if __name__ == "__main__":
					__UpperCamelCase	: Tuple    		=  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.'
					)

					__UpperCamelCase	: Tuple    		=  parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												with open(self.vocab_file					,	'''w'''					,	encoding='''utf-8'''		) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





