



'''simple docstring'''






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCAmelCase:      Tuple		 =		{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def    lowerCamelCase__ (		_A       ):
  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts       )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config       )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights       )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def    lowerCamelCase__ (		_A				,    _A       ):
  if args.student_type == "roberta":
    a		:      Dict       =  False
  elif args.student_type == "gpt2":
    a		:      str       =  False
def    lowerCamelCase__ (		_A				,    _A       ):
  if args.student_type == "roberta":
    a		:      List[str]       =  False
def    lowerCamelCase__ (		):
  a		:      Optional[Any]       =  argparse.ArgumentParser(description='Training'       )
  parser.add_argument('--force'				,    action='store_true'				,    help='Overwrite dump_path if it already exists.'       )

  parser.add_argument(
      '--dump_path'				,    type=_A				,    required=_A				,    help='The output directory (log, checkpoints, parameters, etc.)'       )
  parser.add_argument(
      '--data_file'				,    type=_A				,    required=_A				,    help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'				,    )

  parser.add_argument(
      '--student_type'				,    type=_A				,    choices=['distilbert', 'roberta', 'gpt2']				,    required=_A				,    help='The student type (DistilBERT, RoBERTa).'				,    )
  parser.add_argument('--student_config'				,    type=_A				,    required=_A				,    help='Path to the student configuration.'       )
  parser.add_argument(
      '--student_pretrained_weights'				,    default=_A				,    type=_A				,    help='Load student initialization checkpoint.'       )

  parser.add_argument(
      '--teacher_type'				,    choices=['bert', 'roberta', 'gpt2']				,    required=_A				,    help='Teacher type (BERT, RoBERTa).'       )
  parser.add_argument('--teacher_name'				,    type=_A				,    required=_A				,    help='The teacher model.'       )

  parser.add_argument('--temperature'				,    default=2.0				,    type=_A				,    help='Temperature for the softmax temperature.'       )
  parser.add_argument(
      '--alpha_ce'				,    default=0.5				,    type=_A				,    help='Linear weight for the distillation loss. Must be >=0.'       )
  parser.add_argument(
      '--alpha_mlm'				,    default=0.0				,    type=_A				,    help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'				,    )
  parser.add_argument('--alpha_clm'				,    default=0.5				,    type=_A				,    help='Linear weight for the CLM loss. Must be >=0.'       )
  parser.add_argument('--alpha_mse'				,    default=0.0				,    type=_A				,    help='Linear weight of the MSE loss. Must be >=0.'       )
  parser.add_argument(
      '--alpha_cos'				,    default=0.0				,    type=_A				,    help='Linear weight of the cosine embedding loss. Must be >=0.'       )

  parser.add_argument(
      '--mlm'				,    action='store_true'				,    help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'       )
  parser.add_argument(
      '--mlm_mask_prop'				,    default=0.15				,    type=_A				,    help='Proportion of tokens for which we need to make a prediction.'				,    )
  parser.add_argument('--word_mask'				,    default=0.8				,    type=_A				,    help='Proportion of tokens to mask out.'       )
  parser.add_argument('--word_keep'				,    default=0.1				,    type=_A				,    help='Proportion of tokens to keep.'       )
  parser.add_argument('--word_rand'				,    default=0.1				,    type=_A				,    help='Proportion of tokens to randomly replace.'       )
  parser.add_argument(
      '--mlm_smoothing'				,    default=0.7				,    type=_A				,    help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'				,    )
  parser.add_argument('--token_counts'				,    type=_A				,    help='The token counts in the data_file for MLM.'       )

  parser.add_argument(
      '--restrict_ce_to_mask'				,    action='store_true'				,    help='If true, compute the distillation loss only the [MLM] prediction distribution.'				,    )
  parser.add_argument(
      '--freeze_pos_embs'				,    action='store_true'				,    help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'				,    )
  parser.add_argument(
      '--freeze_token_type_embds'				,    action='store_true'				,    help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'				,    )

  parser.add_argument('--n_epoch'				,    type=_A				,    default=3				,    help='Number of pass on the whole dataset.'       )
  parser.add_argument('--batch_size'				,    type=_A				,    default=5				,    help='Batch size (for each process).'       )
  parser.add_argument(
      '--group_by_size'				,    action='store_false'				,    help='If true, group sequences that have similar length into the same batch. Default is true.'				,    )

  parser.add_argument(
      '--gradient_accumulation_steps'				,    type=_A				,    default=50				,    help='Gradient accumulation for larger training batches.'				,    )
  parser.add_argument('--warmup_prop'				,    default=0.05				,    type=_A				,    help='Linear warmup proportion.'       )
  parser.add_argument('--weight_decay'				,    default=0.0				,    type=_A				,    help='Weight decay if we apply some.'       )
  parser.add_argument('--learning_rate'				,    default=5E-4				,    type=_A				,    help='The initial learning rate for Adam.'       )
  parser.add_argument('--adam_epsilon'				,    default=1E-6				,    type=_A				,    help='Epsilon for Adam optimizer.'       )
  parser.add_argument('--max_grad_norm'				,    default=5.0				,    type=_A				,    help='Max gradient norm.'       )
  parser.add_argument('--initializer_range'				,    default=0.02				,    type=_A				,    help='Random initialization range.'       )

  parser.add_argument(
      '--fp16'				,    action='store_true'				,    help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'				,    )
  parser.add_argument(
      '--fp16_opt_level'				,    type=_A				,    default='O1'				,    help=(
          'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
          'See details at https://nvidia.github.io/apex/amp.html'
      )				,    )
  parser.add_argument('--n_gpu'				,    type=_A				,    default=1				,    help='Number of GPUs in the node.'       )
  parser.add_argument('--local_rank'				,    type=_A				,    default=-1				,    help='Distributed training - Local rank'       )
  parser.add_argument('--seed'				,    type=_A				,    default=56				,    help='Random seed'       )

  parser.add_argument('--log_interval'				,    type=_A				,    default=500				,    help='Tensorboard logging interval.'       )
  parser.add_argument('--checkpoint_interval'				,    type=_A				,    default=4000				,    help='Checkpoint interval.'       )
  a		:      Any       =  parser.parse_args()
  sanity_checks(_A       )

  # ARGS #
  init_gpu_params(_A       )
  set_seed(_A       )
  if args.is_master:
    if os.path.exists(args.dump_path       ):
      if not args.force:
        raise ValueError(
            f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
            ' itUse `--force` if you want to overwrite it'       )
      else:
        shutil.rmtree(args.dump_path       )

    if not os.path.exists(args.dump_path       ):
      os.makedirs(args.dump_path       )
    logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""       )

    # SAVE PARAMS #
    logger.info(f"""Param: {args}"""       )
    with open(os.path.join(args.dump_path				,    'parameters.json'       )				,    'w'       ) as f:
      json.dump(vars(_A       )				,    _A				,    indent=4       )
    git_log(args.dump_path       )

  a     ,			a     ,			a		:      Tuple       =  MODEL_CLASSES[args.student_type]
  a     ,			a     ,			a		:      Optional[int]       =  MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  a		:      Optional[Any]       =  teacher_tokenizer_class.from_pretrained(args.teacher_name       )
  a		:      Any       =  {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    a		:      Optional[Any]       =  tokenizer.all_special_tokens.index(_A       )
    a		:      int       =  tokenizer.all_special_ids[idx]
  logger.info(f"""Special tokens {special_tok_ids}"""       )
  a		:      Dict       =  special_tok_ids
  a		:      str       =  tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f"""Loading data from {args.data_file}"""       )
  with open(args.data_file				,    'rb'       ) as fp:
    a		:      List[str]       =  pickle.load(_A       )

  if args.mlm:
    logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""       )
    with open(args.token_counts				,    'rb'       ) as fp:
      a		:      Optional[Any]       =  pickle.load(_A       )

    a		:      List[Any]       =  np.maximum(_A				,    1       ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      a		:      Optional[int]       =  0.0  # do not predict special tokens
    a		:      Any       =  torch.from_numpy(_A       )
  else:
    a		:      int       =  None

  a		:      Tuple       =  LmSeqsDataset(params=_A				,    data=_A       )
  logger.info('Data loader created.'       )

  # STUDENT #
  logger.info(f"""Loading student config from {args.student_config}"""       )
  a		:      Dict       =  student_config_class.from_pretrained(args.student_config       )
  a		:      List[Any]       =  True

  if args.student_pretrained_weights is not None:
    logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""       )
    a		:      int       =  student_model_class.from_pretrained(args.student_pretrained_weights				,    config=_A       )
  else:
    a		:      Any       =  student_model_class(_A       )

  if args.n_gpu > 0:
    student.to(f"""cuda:{args.local_rank}"""       )
  logger.info('Student loaded.'       )

  # TEACHER #
  a		:      str       =  teacher_model_class.from_pretrained(args.teacher_name				,    output_hidden_states=_A       )
  if args.n_gpu > 0:
    teacher.to(f"""cuda:{args.local_rank}"""       )
  logger.info(f"""Teacher loaded from {args.teacher_name}."""       )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(_A				,    _A       )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(_A				,    _A       )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0       ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  a		:      str       =  Distiller(
      params=_A				,    dataset=_A				,    token_probs=_A				,    student=_A				,    teacher=_A       )
  distiller.train()
  logger.info('Let\'s go get some drinks.'       )


if __name__ == "__main__":
     main()




'''simple docstring'''






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				:						List[str]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Any=13		,    __snake_case				:						int=30		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						Optional[int]=3		,    __snake_case				:						Dict=True		,    __snake_case				:						List[str]=True		,    __snake_case				:						Any=32		,    __snake_case				:						List[Any]=5		,    __snake_case				:						List[Any]=4		,    __snake_case				:						Optional[int]=37		,    __snake_case				:						int="gelu"		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						List[str]=0.1		,    __snake_case				:						List[str]=10		,    __snake_case				:						int=0.02		,    __snake_case				:						Optional[int]=3		,    __snake_case				:						Tuple=0.6		,    __snake_case				:						Union[str, Any]=None		,    ):
      a		:      List[str]       =  parent
      a		:      Tuple       =  batch_size
      a		:      Union[str, Any]       =  image_size
      a		:      List[str]       =  patch_size
      a		:      Optional[Any]       =  num_channels
      a		:      Optional[Any]       =  is_training
      a		:      List[Any]       =  use_labels
      a		:      Union[str, Any]       =  hidden_size
      a		:      Dict       =  num_hidden_layers
      a		:      Optional[Any]       =  num_attention_heads
      a		:      Optional[Any]       =  intermediate_size
      a		:      int       =  hidden_act
      a		:      Dict       =  hidden_dropout_prob
      a		:      Optional[Any]       =  attention_probs_dropout_prob
      a		:      Optional[Any]       =  type_sequence_label_size
      a		:      Optional[Any]       =  initializer_range
      a		:      Union[str, Any]       =  mask_ratio
      a		:      Optional[int]       =  scope

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      a		:      Tuple       =  None
      if self.use_labels:
        a		:      List[str]       =  ids_tensor([self.batch_size]		,    self.type_sequence_label_size			)

      a		:      Optional[int]       =  self.get_config()

      return config, pixel_values, labels







    def     lowercase_ (   self				:						str			):
      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=__snake_case		,    initializer_range=self.initializer_range		,    mask_ratio=self.mask_ratio		,    )







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Dict		,    __snake_case				:						List[Any]		,    __snake_case				:						Union[str, Any]			):
      a		:      Union[str, Any]       =  ViTMAEModel(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Optional[Any]       =  model(__snake_case			)
      self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Dict			):
      a		:      Dict       =  ViTMAEForPreTraining(__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Dict       =  model(__snake_case			)
      a		:      str       =  (self.image_size // self.patch_size) ** 2
      a		:      int       =  self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, num_patches, expected_num_channels)			)

      # test greyscale images
      a		:      Any       =  1
      a		:      Optional[int]       =  ViTMAEForPreTraining(__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      int       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a		:      Optional[Any]       =  model(__snake_case			)
      a		:      Dict       =  self.patch_size**2
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, num_patches, expected_num_channels)			)







    def     lowercase_ (   self				:						Dict			):
      a		:      List[str]       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a		:      Any       =  config_and_inputs
      a		:      Optional[Any]       =  {'pixel_values': pixel_values}
      return config, inputs_dict

@require_torch
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowercase__				=					{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

    lowercase__				=					False
    lowercase__				=					False
    lowercase__				=					False
    lowercase__				=					False







    def     lowercase_ (   self				:						Tuple			):
      a		:      List[str]       =  ViTMAEModelTester(self			)
      a		:      List[str]       =  ConfigTester(self		,    config_class=__snake_case		,    has_text_modality=__snake_case		,    hidden_size=37			)







    def     lowercase_ (   self				:						List[str]			):
      self.config_tester.run_common_tests()







    @unittest.skip(reason='ViTMAE does not use inputs_embeds'			)
    def     lowercase_ (   self				:						List[str]			):
      pass







    def     lowercase_ (   self				:						Dict			):
      a     ,			a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:      Optional[Any]       =  model_class(__snake_case			)
        self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)			)
        a		:      Optional[int]       =  model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__snake_case		,    nn.Linear			)			)







    def     lowercase_ (   self				:						List[str]			):
      a     ,			a		:      Dict       =  self.model_tester.prepare_config_and_inputs_for_common()

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

        a		:      List[Any]       =  ['pixel_values']
        self.assertListEqual(arg_names[:1]		,    __snake_case			)







    def     lowercase_ (   self				:						Any			):
      a		:      Optional[int]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__snake_case			)







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Tuple			):
      # make masks reproducible
      np.random.seed(2			)

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

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      a		:      List[str]       =  pt_noise

      super().check_pt_tf_models(__snake_case		,    __snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[int]			):
      a     ,			a		:      List[str]       =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:      Any       =  model_class(__snake_case			)
        model.to(__snake_case			)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2			)
        with torch.no_grad():
          a		:      Tuple       =  model(**self._prepare_for_class(__snake_case		,    __snake_case			)			)

        a		:      List[str]       =  outputs[0].cpu().numpy()
        a		:      str       =  0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(__snake_case			)
          a		:      Dict       =  model_class.from_pretrained(__snake_case			)
          model.to(__snake_case			)
          # make random mask reproducible
          torch.manual_seed(2			)
          with torch.no_grad():
            a		:      int       =  model(**self._prepare_for_class(__snake_case		,    __snake_case			)			)

          # Make sure we don't have nans
          a		:      List[Any]       =  after_outputs[0].cpu().numpy()
          a		:      Any       =  0
          a		:      Any       =  np.amax(np.abs(out_a - out_a			)			)
          self.assertLessEqual(__snake_case		,    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     lowercase_ (   self				:						List[Any]			):
      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     lowercase_ (   self				:						Any			):
      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     lowercase_ (   self				:						int			):
      pass







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







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
    def     lowercase_ (   self				:						Any			):
      pass







    @slow
    def     lowercase_ (   self				:						Dict			):
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:      str       =  ViTMAEModel.from_pretrained(__snake_case			)
        self.assertIsNotNone(__snake_case			)
def    lowerCamelCase__ (		):
  a		:      Tuple       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image

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







    @cached_property
    def     lowercase_ (   self				:						Union[str, Any]			):
      return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'			) if is_vision_available() else None







    @slow
    def     lowercase_ (   self				:						Union[str, Any]			):
      # make random mask reproducible across the PT and TF model
      np.random.seed(2			)

      a		:      Union[str, Any]       =  ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'			).to(__snake_case			)

      a		:      str       =  self.default_image_processor
      a		:      Dict       =  prepare_img()
      a		:      Union[str, Any]       =  image_processor(images=__snake_case		,    return_tensors='pt'			).to(__snake_case			)

      # 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)
      a		:      Tuple       =  ViTMAEConfig()
      a		:      Optional[Any]       =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
      a		:      List[str]       =  np.random.uniform(size=(1, num_patches)			)

      # forward pass
      with torch.no_grad():
        a		:      Tuple       =  model(**__snake_case		,    noise=torch.from_numpy(__snake_case			).to(device=__snake_case			)			)

      # verify the logits
      a		:      int       =  torch.Size((1, 1_96, 7_68)			)
      self.assertEqual(outputs.logits.shape		,    __snake_case			)

      a		:      Optional[Any]       =  torch.tensor(
          [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]			)

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