


import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_snake_case	:						Optional[int]         =							2

class 							a						:




							"""simple docstring"""


							def __init__(				self			:    int       ,      *,  # begin keyword-only arguments
							    lowerCamelCase			:    List[Any]="<s>"       ,      lowerCamelCase			:    List[Any]="<pad>"       ,      lowerCamelCase			:    Union[str, Any]="</s>"       ,      lowerCamelCase			:    Any="<unk>"       ,      lowerCamelCase			:    Optional[Any]=None       ,      )      ->       Optional[int]:
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Optional[Any]									=  bos, unk, pad, eos
														__snake_case						:     Tuple									=  []
														__snake_case						:     Dict									=  []
														__snake_case						:     List[str]									=  {}
														__snake_case						:     Tuple									=  self.add_symbol(lowerCamelCase							)
														__snake_case						:     List[Any]									=  self.add_symbol(lowerCamelCase							)
														__snake_case						:     Dict									=  self.add_symbol(lowerCamelCase							)
														__snake_case						:     List[str]									=  self.add_symbol(lowerCamelCase							)
														if extra_special_symbols:
																					for s in extra_special_symbols:
																												self.add_symbol(lowerCamelCase							)
														__snake_case						:     Any									=  len(self.symbols							)

							def __eq__(				self			:    Any       ,      lowerCamelCase			:    Optional[Any]							)      ->       int:
														return self.indices == other.indices

							def __getitem__(				self			:    Union[str, Any]       ,      lowerCamelCase			:    Dict							)      ->       Any:
														if idx < len(self.symbols							):
																					return self.symbols[idx]
														return self.unk_word

							def __len__(				self			:    int							)      ->       Optional[int]:
														return len(self.symbols							)

							def __contains__(				self			:    str       ,      lowerCamelCase			:    str							)      ->       Any:
														return sym in self.indices

							@classmethod
							def        __snake_case   (				cls			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[Any]							)      ->       List[str]:
														__snake_case						:     Optional[Any]									=  cls()
														d.add_from_file(lowerCamelCase							)
														return d

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[str]=1       ,      lowerCamelCase			:    int=False							)      ->       Union[str, Any]:
														if word in self.indices and not overwrite:
																					__snake_case						:     Optional[int]									=  self.indices[word]
																					__snake_case						:     Optional[Any]									=  self.count[idx] + n
																					return idx
														else:
																					__snake_case						:     Dict									=  len(self.symbols							)
																					__snake_case						:     List[Any]									=  idx
																					self.symbols.append(lowerCamelCase							)
																					self.count.append(lowerCamelCase							)
																					return idx

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Tuple							)      ->       Any:
														return 0






							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Tuple							)      ->       List[str]:
														if isinstance(lowerCamelCase       ,      lowerCamelCase							):
																					try:
																												with open(lowerCamelCase       ,      "r"       ,      encoding="utf-8"							) as fd:
																																			self.add_from_file(lowerCamelCase							)
																					except FileNotFoundError as fnfe:
																												raise fnfe
																					except UnicodeError:
																												raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase							)							)
																					return

														__snake_case						:     Dict									=  f.readlines()
														__snake_case						:     Dict									=  self._load_meta(lowerCamelCase							)

														for line in lines[indices_start_line:]:
																					try:
																												__snake_case					,     __snake_case						:     Union[str, Any]									=  line.rstrip().rsplit(" "       ,      1							)
																												if field == "#fairseq:overwrite":
																																			__snake_case						:     int									=  True
																																			__snake_case					,     __snake_case						:     List[str]									=  line.rsplit(" "       ,      1							)
																												else:
																																			__snake_case						:     Union[str, Any]									=  False
																												__snake_case						:     Union[str, Any]									=  int(lowerCamelCase							)
																												__snake_case						:     Dict									=  line
																												if word in self and not overwrite:
																																			raise RuntimeError(
																																			    "Duplicate word found when loading Dictionary: '{}'. "
																																			    "Duplicate words can overwrite earlier ones by adding the "
																																			    "#fairseq:overwrite flag at the end of the corresponding row "
																																			    "in the dictionary file. If using the Camembert model, please "
																																			    "download an updated copy of the model file.".format(lowerCamelCase							)							)
																												self.add_symbol(lowerCamelCase       ,      n=lowerCamelCase       ,      overwrite=lowerCamelCase							)
																					except ValueError:
																												raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"							)







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							__snake_case						:     List[Any]									=  dict((re.sub(R"@@$"				,      ""				,      __lowerCamelCase      ), v) if k.endswith("@@"      ) else (re.sub(R"$"				,      "</w>"				,      __lowerCamelCase      ), v) for k, v in d.items()      )
							__snake_case						:     str									=  "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														__snake_case						:     Optional[int]									=  d[k]  # restore
							return da







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							# prep
							if not os.path.exists(__lowerCamelCase      ):
														raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'      )
							os.makedirs(__lowerCamelCase				,      exist_ok=__lowerCamelCase      )
							print(F'Writing results to {pytorch_dump_folder_path}'      )

							# handle various types of models

							__snake_case						:     Dict									=  os.path.join(__lowerCamelCase				,      "checkpoint.pt"      )
							if not os.path.isfile(__lowerCamelCase      ):
														raise ValueError(F'path to the file {checkpoint_file} does not exist!'      )
							__snake_case						:     str									=  torch.load(__lowerCamelCase				,      map_location="cpu"      )

							__snake_case						:     List[Any]									=  chkpt["cfg"]["model"]

							# dicts
							__snake_case						:     str									=  os.path.join(__lowerCamelCase				,      "dict.txt"      )
							if not os.path.isfile(__lowerCamelCase      ):
														raise ValueError(F'path to the file {dict_file} does not exist!'      )
							__snake_case						:     int									=  Dictionary.load(__lowerCamelCase      )
							__snake_case						:     Tuple									=  rewrite_dict_keys(src_dict.indices      )
							__snake_case						:     Tuple									=  len(__lowerCamelCase      )
							__snake_case						:     Optional[Any]									=  os.path.join(__lowerCamelCase				,      VOCAB_FILES_NAMES["vocab_file"]      )
							print(F'Generating {src_vocab_file} of {src_vocab_size} records'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# merges_file (bpecodes)
							__snake_case						:     Dict									=  os.path.join(__lowerCamelCase				,      "bpecodes"      )
							if not os.path.isfile(__lowerCamelCase      ):
														raise ValueError(F'path to the file {bpecodes_file} does not exist!'      )

							__snake_case						:     List[Any]									=  os.path.join(__lowerCamelCase				,      VOCAB_FILES_NAMES["merges_file"]      )
							shutil.copyfile(__lowerCamelCase				,      __lowerCamelCase      )

							# model config
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      "config.json"      )

							__snake_case						:     Optional[Any]									=  {
							    "activation_dropout": args["activation_dropout"],
							    "architectures": ["BioGptForCausalLM"],
							    "attention_probs_dropout_prob": args["attention_dropout"],
							    "bos_token_id": 0,
							    "eos_token_id": 2,
							    "hidden_act": args["activation_fn"],
							    "hidden_dropout_prob": args["dropout"],
							    "hidden_size": args["decoder_embed_dim"],
							    "initializer_range": 0.0_2,
							    "intermediate_size": args["decoder_ffn_embed_dim"],
							    "layer_norm_eps": 1e-12,
							    "layerdrop": args["decoder_layerdrop"],
							    "max_position_embeddings": args["max_target_positions"],
							    "model_type": "biogpt",
							    "num_attention_heads": args["decoder_attention_heads"],
							    "num_hidden_layers": args["decoder_layers"],
							    "pad_token_id": 1,
							    "scale_embedding": not args["no_scale_embedding"],
							    "tie_word_embeddings": args["share_decoder_input_output_embed"],
							    "vocab_size": src_vocab_size,
							}

							# good hparam defaults to start with

							print(F'Generating {biogpt_model_config_file}'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# tokenizer config
							__snake_case						:     Tuple									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )

							__snake_case						:     int									=  {
							    "bos_token": "<s>",
							    "eos_token": "</s>",
							    "model_max_length": 1_0_2_4,
							    "pad_token": "<pad>",
							    "special_tokens_map_file": None,
							    "tokenizer_class": "BioGptTokenizer",
							    "unk_token": "<unk>",
							}

							print(F'Generating {biogpt_tokenizer_config_file}'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# model
							__snake_case						:     Dict									=  chkpt["model"]

							# remove unneeded keys
							__snake_case						:     int									=  [
							    "decoder.version",
							]
							for k in ignore_keys:
														model_state_dict.pop(__lowerCamelCase				,      __lowerCamelCase      )

							__snake_case						:     Optional[int]									=  list(model_state_dict.keys()      )
							for layer_name in layer_names:
														if layer_name.endswith("output_projection.weight"      ):
																					__snake_case						:     str									=  model_state_dict.pop(__lowerCamelCase      )
														else:
																					__snake_case						:     Any									=  model_state_dict.pop(__lowerCamelCase      )

							__snake_case						:     Dict									=  BioGptConfig.from_pretrained(__lowerCamelCase      )
							__snake_case						:     Any									=  BioGptForCausalLM(__lowerCamelCase      )

							# check that it loads ok
							model_new.load_state_dict(__lowerCamelCase      )

							# save
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )
							print(F'Generating {pytorch_weights_dump_path}'      )
							torch.save(__lowerCamelCase				,      __lowerCamelCase      )

							print("Conversion is done!"      )


if __name__ == "__main__":
			_snake_case	:						Any         =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--biogpt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_snake_case	:						Union[str, Any]         =							parser.parse_args()
			convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class    A__  :





      def __init__(					self			,				A_			,				A_=13			,				A_=30			,				A_=2			,				A_=3			,				A_=True			,				A_=True			,				A_=32			,				A_=5			,				A_=4			,				A_=37			,				A_="gelu"			,				A_=0.1			,				A_=0.1			,				A_=10			,				A_=0.02			,				A_=3			,				A_=0.6			,				A_=None			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  parent
       UpperCamelCase      :   Union[str, Any]			      =  batch_size
       UpperCamelCase      :   Any			      =  image_size
       UpperCamelCase      :   Tuple			      =  patch_size
       UpperCamelCase      :   Union[str, Any]			      =  num_channels
       UpperCamelCase      :   Tuple			      =  is_training
       UpperCamelCase      :   int			      =  use_labels
       UpperCamelCase      :   Tuple			      =  hidden_size
       UpperCamelCase      :   Optional[Any]			      =  num_hidden_layers
       UpperCamelCase      :   str			      =  num_attention_heads
       UpperCamelCase      :   Optional[int]			      =  intermediate_size
       UpperCamelCase      :   Dict			      =  hidden_act
       UpperCamelCase      :   List[str]			      =  hidden_dropout_prob
       UpperCamelCase      :   str			      =  attention_probs_dropout_prob
       UpperCamelCase      :   Optional[Any]			      =  type_sequence_label_size
       UpperCamelCase      :   Tuple			      =  initializer_range
       UpperCamelCase      :   Any			      =  mask_ratio
       UpperCamelCase      :   int			      =  scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       UpperCamelCase      :   Union[str, Any]			      =  (image_size // patch_size) ** 2
       UpperCamelCase      :   int			      =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       UpperCamelCase      :   int			      =  None
       if self.use_labels:
        UpperCamelCase      :   Optional[Any]			      =  ids_tensor([self.batch_size]			,				self.type_sequence_label_size  )

       UpperCamelCase      :   str			      =  self.get_config()

       return config, pixel_values, labels





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return ViTMAEConfig(
           image_size=self.image_size			,				patch_size=self.patch_size			,				num_channels=self.num_channels			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				is_decoder=A_			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  ViTMAEModel(config=A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   Optional[Any]			      =  model(A_  )
       self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  ViTMAEForPreTraining(A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   List[str]			      =  model(A_  )
       UpperCamelCase      :   Any			      =  (self.image_size // self.patch_size) ** 2
       UpperCamelCase      :   List[str]			      =  self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)  )

       # test greyscale images
       UpperCamelCase      :   str			      =  1
       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEForPreTraining(A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   Union[str, Any]			      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       UpperCamelCase      :   Union[str, Any]			      =  model(A_  )
       UpperCamelCase      :   List[str]			      =  self.patch_size**2
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.prepare_config_and_inputs()
       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  config_and_inputs
       UpperCamelCase      :   Optional[int]			      =  {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class    A__  (			__snake_case			,  __snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :Any		       =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      _UpperCAmelCase      :int		       =  {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

      _UpperCAmelCase      :Optional[Any]		       =  False
      _UpperCAmelCase      :Any		       =  False
      _UpperCAmelCase      :Optional[int]		       =  False
      _UpperCAmelCase      :Dict		       =  False





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEModelTester(self  )
       UpperCamelCase      :   Any			      =  ConfigTester(self			,				config_class=A_			,				has_text_modality=A_			,				hidden_size=37  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   List[Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase      :   Any			      =  model_class(A_  )
        self.assertIsInstance(model.get_input_embeddings()			,				(nn.Module)  )
        UpperCamelCase      :   Any			      =  model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_			,				nn.Linear  )  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase      :   List[str]			      =  model_class(A_  )
        UpperCamelCase      :   Any			      =  inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCamelCase      :   List[str]			      =  [*signature.parameters.keys()]

        UpperCamelCase      :   Optional[int]			      =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   Union[str, Any]			      =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       UpperCamelCase      :   Tuple			      =  torch.from_numpy(A_  )

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       UpperCamelCase      :   Any			      =  pt_noise

       super().check_pt_tf_models(A_			,				A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase      :   Optional[Any]			      =  model_class(A_  )
        model.to(A_  )
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2  )
        with torch.no_grad():
         UpperCamelCase      :   str			      =  model(**self._prepare_for_class(A_			,				A_  )  )

        UpperCamelCase      :   Any			      =  outputs[0].cpu().numpy()
        UpperCamelCase      :   str			      =  0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(A_  )
         UpperCamelCase      :   Optional[int]			      =  model_class.from_pretrained(A_  )
         model.to(A_  )
         # make random mask reproducible
         torch.manual_seed(2  )
         with torch.no_grad():
          UpperCamelCase      :   Tuple			      =  model(**self._prepare_for_class(A_			,				A_  )  )

         # Make sure we don't have nans
         UpperCamelCase      :   Dict			      =  after_outputs[0].cpu().numpy()
         UpperCamelCase      :   int			      =  0
         UpperCamelCase      :   str			      =  np.amax(np.abs(out_a - out_a  )  )
         self.assertLessEqual(A_			,				1e-5  )





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase      :   List[str]			      =  ViTMAEModel.from_pretrained(A_  )
        self.assertIsNotNone(A_  )



def 						A_							(	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image



@require_torch
@require_vision
class    A__  (			unittest.TestCase     ):





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(A_  )

       UpperCamelCase      :   Any			      =  self.default_image_processor
       UpperCamelCase      :   Optional[Any]			      =  prepare_img()
       UpperCamelCase      :   int			      =  image_processor(images=A_			,				return_tensors="pt"  ).to(A_  )

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       UpperCamelCase      :   Dict			      =  ViTMAEConfig()
       UpperCamelCase      :   Any			      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(1, num_patches)  )

       # forward pass
       with torch.no_grad():
        UpperCamelCase      :   Optional[Any]			      =  model(**A_			,				noise=torch.from_numpy(A_  ).to(device=A_  )  )

       # verify the logits
       UpperCamelCase      :   str			      =  torch.Size((1, 196, 768)  )
       self.assertEqual(outputs.logits.shape			,				A_  )

       UpperCamelCase      :   str			      =  torch.tensor(
           [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]  )

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,				expected_slice.to(A_  )			,				atol=1e-4  )  )
