

"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def        _snake_case   (			UpperCAmelCase_     :    Optional[int]	):


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

     return name





def        _snake_case   (			UpperCAmelCase_     :    List[str]    ,      UpperCAmelCase_     :    int	):


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

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

          else:
               A__								=     val

     return orig_state_dict





def        _snake_case   (			UpperCAmelCase_     :    int    ,      UpperCAmelCase_     :    Union[str, Any]	):
     A__								=     ViTMAEConfig()
     if "large" in checkpoint_url:
          A__								=     1024
          A__								=     4096
          A__								=     24
          A__								=     16
     elif "huge" in checkpoint_url:
          A__								=     14
          A__								=     1280
          A__								=     5120
          A__								=     32
          A__								=     16

     A__								=     ViTMAEForPreTraining(__lowerCAmelCase	)

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

     A__								=     ViTMAEImageProcessor(size=config.image_size	)

     A__								=     convert_state_dict(__lowerCAmelCase    ,      __lowerCAmelCase	)

     model.load_state_dict(__lowerCAmelCase	)
     model.eval()

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

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

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

     if "large" in checkpoint_url:
          A__								=     torch.tensor(
              [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]	)
     elif "huge" in checkpoint_url:
          A__								=     torch.tensor(
              [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]	)
     else:
          A__								=     torch.tensor(
              [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]	)

     # 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__":
 SCREAMING_SNAKE_CASE_    :		Optional[int]							     =  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.'
 )

 SCREAMING_SNAKE_CASE_    :		List[Any]							     =  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     )



