
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


			if "cls_token" in name:
						lowerCAmelCase_						=	name.replace("cls_token"    ,							"vit.embeddings.cls_token"				)
			if "mask_token" in name:
						lowerCAmelCase_						=	name.replace("mask_token"    ,							"decoder.mask_token"				)
			if "decoder_pos_embed" in name:
						lowerCAmelCase_						=	name.replace("decoder_pos_embed"    ,							"decoder.decoder_pos_embed"				)
			if "pos_embed" in name and "decoder" not in name:
						lowerCAmelCase_						=	name.replace("pos_embed"    ,							"vit.embeddings.position_embeddings"				)
			if "patch_embed.proj" in name:
						lowerCAmelCase_						=	name.replace("patch_embed.proj"    ,							"vit.embeddings.patch_embeddings.projection"				)
			if "patch_embed.norm" in name:
						lowerCAmelCase_						=	name.replace("patch_embed.norm"    ,							"vit.embeddings.norm"				)
			if "decoder_blocks" in name:
						lowerCAmelCase_						=	name.replace("decoder_blocks"    ,							"decoder.decoder_layers"				)
			if "blocks" in name:
						lowerCAmelCase_						=	name.replace("blocks"    ,							"vit.encoder.layer"				)
			if "attn.proj" in name:
						lowerCAmelCase_						=	name.replace("attn.proj"    ,							"attention.output.dense"				)
			if "attn" in name:
						lowerCAmelCase_						=	name.replace("attn"    ,							"attention.self"				)
			if "norm1" in name:
						lowerCAmelCase_						=	name.replace("norm1"    ,							"layernorm_before"				)
			if "norm2" in name:
						lowerCAmelCase_						=	name.replace("norm2"    ,							"layernorm_after"				)
			if "mlp.fc1" in name:
						lowerCAmelCase_						=	name.replace("mlp.fc1"    ,							"intermediate.dense"				)
			if "mlp.fc2" in name:
						lowerCAmelCase_						=	name.replace("mlp.fc2"    ,							"output.dense"				)
			if "decoder_embed" in name:
						lowerCAmelCase_						=	name.replace("decoder_embed"    ,							"decoder.decoder_embed"				)
			if "decoder_norm" in name:
						lowerCAmelCase_						=	name.replace("decoder_norm"    ,							"decoder.decoder_norm"				)
			if "decoder_pred" in name:
						lowerCAmelCase_						=	name.replace("decoder_pred"    ,							"decoder.decoder_pred"				)
			if "norm.weight" in name and "decoder" not in name:
						lowerCAmelCase_						=	name.replace("norm.weight"    ,							"vit.layernorm.weight"				)
			if "norm.bias" in name and "decoder" not in name:
						lowerCAmelCase_						=	name.replace("norm.bias"    ,							"vit.layernorm.bias"				)

			return name






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_						=	orig_state_dict.pop(__lowerCAmelCase				)

						if "qkv" in key:
									lowerCAmelCase_						=	key.split("."				)
									lowerCAmelCase_						=	int(key_split[1]				)
									if "decoder_blocks" in key:
												lowerCAmelCase_						=	config.decoder_hidden_size
												lowerCAmelCase_						=	"decoder.decoder_layers."
												if "weight" in key:
															lowerCAmelCase_						=	val[:dim, :]
															lowerCAmelCase_						=	val[dim : dim * 2, :]
															lowerCAmelCase_						=	val[-dim:, :]
												elif "bias" in key:
															lowerCAmelCase_						=	val[:dim]
															lowerCAmelCase_						=	val[dim : dim * 2]
															lowerCAmelCase_						=	val[-dim:]
									else:
												lowerCAmelCase_						=	config.hidden_size
												lowerCAmelCase_						=	"vit.encoder.layer."
												if "weight" in key:
															lowerCAmelCase_						=	val[:dim, :]
															lowerCAmelCase_						=	val[dim : dim * 2, :]
															lowerCAmelCase_						=	val[-dim:, :]
												elif "bias" in key:
															lowerCAmelCase_						=	val[:dim]
															lowerCAmelCase_						=	val[dim : dim * 2]
															lowerCAmelCase_						=	val[-dim:]

						else:
									lowerCAmelCase_						=	val

			return orig_state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	ViTMAEConfig()
			if "large" in checkpoint_url:
						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	16
			elif "huge" in checkpoint_url:
						lowerCAmelCase_						=	14
						lowerCAmelCase_						=	1280
						lowerCAmelCase_						=	5120
						lowerCAmelCase_						=	32
						lowerCAmelCase_						=	16

			lowerCAmelCase_						=	ViTMAEForPreTraining(__lowerCAmelCase				)

			lowerCAmelCase_						=	torch.hub.load_state_dict_from_url(__lowerCAmelCase    ,							map_location="cpu"				)["model"]

			lowerCAmelCase_						=	ViTMAEImageProcessor(size=config.image_size				)

			lowerCAmelCase_						=	convert_state_dict(__lowerCAmelCase    ,							__lowerCAmelCase				)

			model.load_state_dict(__lowerCAmelCase				)
			model.eval()

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

			lowerCAmelCase_						=	Image.open(requests.get(__lowerCAmelCase    ,							stream=__lowerCAmelCase				).raw				)
			lowerCAmelCase_						=	ViTMAEImageProcessor(size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=__lowerCAmelCase    ,							return_tensors="pt"				)

			# forward pass
			torch.manual_seed(2				)
			lowerCAmelCase_						=	model(**__lowerCAmelCase				)
			lowerCAmelCase_						=	outputs.logits

			if "large" in checkpoint_url:
						lowerCAmelCase_						=	torch.tensor(
						    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
			elif "huge" in checkpoint_url:
						lowerCAmelCase_						=	torch.tensor(
						    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
			else:
						lowerCAmelCase_						=	torch.tensor(
						    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

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

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

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


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)





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=None
				_lowercase					=BloomTokenizerFast
				_lowercase					=BloomTokenizerFast
				_lowercase					=True
				_lowercase					=False
				_lowercase					='''tokenizer_file'''
				_lowercase					={'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def      __a						(					self     )				->      Dict:
							super().setUp()
							lowerCAmelCase_						=	BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Tuple:
							kwargs.update(self.special_tokens_map     )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.get_rust_tokenizer()

							lowerCAmelCase_						=	["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase_						=	[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

							lowerCAmelCase_						=	tokenizer.batch_encode_plus(_UpperCamelCase     )["input_ids"]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokenizer.batch_decode(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase=6     )				->      Tuple:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													lowerCAmelCase_						=	"This is a simple input"
													lowerCAmelCase_						=	["This is a simple input 1", "This is a simple input 2"]
													lowerCAmelCase_						=	("This is a simple input", "This is a pair")
													lowerCAmelCase_						=	[
													    ("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
													try:
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )

																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
													except ValueError:
																self.fail("Bloom Tokenizer should be able to deal with padding"     )

													lowerCAmelCase_						=	None  # Hotfixing padding = None
													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      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.get_rust_tokenizer()
							lowerCAmelCase_						=	load_dataset("xnli"				,				"all_languages"				,				split="test"				,				streaming=_UpperCamelCase     )

							lowerCAmelCase_						=	next(iter(_UpperCamelCase     )     )["premise"]  # pick up one data
							lowerCAmelCase_						=	list(sample_data.values()     )

							lowerCAmelCase_						=	list(map(tokenizer.encode				,				_UpperCamelCase     )     )
							lowerCAmelCase_						=	[tokenizer.decode(_UpperCamelCase				,				clean_up_tokenization_spaces=_UpperCamelCase     ) for x in output_tokens]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )				,				1     )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )				,				1     )



