



'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 							a__:
    lowercase__				=					LEDConfig
    lowercase__				=					{}
    lowercase__				=					"""gelu"""







    def __init__(   self				:						List[str]		,    __snake_case				:						str		,    __snake_case				:						List[Any]=13		,    __snake_case				:						Optional[Any]=7		,    __snake_case				:						str=True		,    __snake_case				:						int=False		,    __snake_case				:						int=99		,    __snake_case				:						Optional[int]=32		,    __snake_case				:						Optional[int]=2		,    __snake_case				:						int=4		,    __snake_case				:						Tuple=37		,    __snake_case				:						List[str]=0.1		,    __snake_case				:						Optional[int]=0.1		,    __snake_case				:						Tuple=20		,    __snake_case				:						str=2		,    __snake_case				:						Optional[Any]=1		,    __snake_case				:						Tuple=0		,    __snake_case				:						Tuple=4		,    ):
      a		:      int       =  parent
      a		:      int       =  batch_size
      a		:      int       =  seq_length
      a		:      Any       =  is_training
      a		:      int       =  use_labels
      a		:      int       =  vocab_size
      a		:      Optional[Any]       =  hidden_size
      a		:      Dict       =  num_hidden_layers
      a		:      Any       =  num_attention_heads
      a		:      List[str]       =  intermediate_size

      a		:      Union[str, Any]       =  hidden_dropout_prob
      a		:      str       =  attention_probs_dropout_prob
      a		:      Optional[Any]       =  max_position_embeddings
      a		:      Tuple       =  eos_token_id
      a		:      str       =  pad_token_id
      a		:      Optional[int]       =  bos_token_id
      a		:      Dict       =  attention_window

      # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
      # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
      # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
      # because its local attention only attends to `self.attention_window` and one before and one after
      a		:      Union[str, Any]       =  self.attention_window + 2

      # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
      # the `test_attention_outputs` and `test_hidden_states_output` tests
      a		:      List[str]       =  (
          self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
      )







    def     lowercase_ (   self				:						int			):
      a		:      Tuple       =  ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size			)
      a		:      Union[str, Any]       =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)		,    1			)
      a		:      Optional[int]       =  tf.concat([input_ids, eos_tensor]		,    axis=1			)

      a		:      Any       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[Any]       =  self.config_cls(
          vocab_size=self.vocab_size		,    d_model=self.hidden_size		,    encoder_layers=self.num_hidden_layers		,    decoder_layers=self.num_hidden_layers		,    encoder_attention_heads=self.num_attention_heads		,    decoder_attention_heads=self.num_attention_heads		,    encoder_ffn_dim=self.intermediate_size		,    decoder_ffn_dim=self.intermediate_size		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    eos_token_ids=[2]		,    bos_token_id=self.bos_token_id		,    pad_token_id=self.pad_token_id		,    decoder_start_token_id=self.pad_token_id		,    attention_window=self.attention_window		,    **self.config_updates		,    )
      a		:      List[str]       =  prepare_led_inputs_dict(__snake_case		,    __snake_case		,    __snake_case			)
      a		:      Optional[int]       =  tf.concat(
          [tf.zeros_like(__snake_case			)[:, :-1], tf.ones_like(__snake_case			)[:, -1:]]		,    axis=-1		,    )
      a		:      Any       =  global_attention_mask
      return config, inputs_dict







    def     lowercase_ (   self				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Any			):
      a		:      str       =  TFLEDModel(config=__snake_case			).get_decoder()
      a		:      Tuple       =  inputs_dict['input_ids']

      a		:      int       =  input_ids[:1, :]
      a		:      Union[str, Any]       =  inputs_dict['attention_mask'][:1, :]
      a		:      Tuple       =  1

      # first forward pass
      a		:      Tuple       =  model(__snake_case		,    attention_mask=__snake_case		,    use_cache=__snake_case			)

      a     ,			a		:      Dict       =  outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      a		:      str       =  ids_tensor((self.batch_size, 3)		,    config.vocab_size			)
      a		:      Dict       =  tf.cast(ids_tensor((self.batch_size, 3)		,    2			)		,    tf.inta			)

      # append to next input_ids and
      a		:      Dict       =  tf.concat([input_ids, next_tokens]		,    axis=-1			)
      a		:      str       =  tf.concat([attention_mask, next_attn_mask]		,    axis=-1			)

      a		:      List[str]       =  model(__snake_case		,    attention_mask=__snake_case			)[0]
      a		:      str       =  model(__snake_case		,    attention_mask=__snake_case		,    past_key_values=__snake_case			)[0]

      self.parent.assertEqual(next_tokens.shape[1]		,    output_from_past.shape[1]			)

      # select random slice
      a		:      str       =  int(ids_tensor((1,)		,    output_from_past.shape[-1]			)			)
      a		:      Dict       =  output_from_no_past[:, -3:, random_slice_idx]
      a		:      int       =  output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(__snake_case		,    __snake_case		,    rtol=1e-3			)
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A=None				,    _A=None				,    _A=None				,    _A=None				,    ):
  if attention_mask is None:
    a		:      Union[str, Any]       =  tf.cast(tf.math.not_equal(_A				,    config.pad_token_id       )				,    tf.inta       )
  if decoder_attention_mask is None:
    a		:      Tuple       =  tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape				,    dtype=tf.inta       ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,    config.pad_token_id       )				,    tf.inta       ),
        ]				,    axis=-1				,    )
  if head_mask is None:
    a		:      Union[str, Any]       =  tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
  if decoder_head_mask is None:
    a		:      Optional[Any]       =  tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
      "decoder_input_ids": decoder_input_ids,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
  }

@require_tf
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    lowercase__				=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
    lowercase__				=					(
        {
            """conversational""": TFLEDForConditionalGeneration,
            """feature-extraction""": TFLEDModel,
            """summarization""": TFLEDForConditionalGeneration,
            """text2text-generation""": TFLEDForConditionalGeneration,
            """translation""": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowercase__				=					True
    lowercase__				=					False
    lowercase__				=					False
    lowercase__				=					False







    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  TFLEDModelTester(self			)
      a		:      Optional[int]       =  ConfigTester(self		,    config_class=__snake_case			)







    def     lowercase_ (   self				:						Dict			):
      self.config_tester.run_common_tests()







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Any       =  self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a     ,			a		:      str       =  self.model_tester.prepare_config_and_inputs_for_common()
      a		:      int       =  tf.zeros_like(inputs_dict['attention_mask']			)
      a		:      Optional[int]       =  2
      a		:      Union[str, Any]       =  tf.where(
          tf.range(self.model_tester.seq_length			)[None, :] < num_global_attn_indices		,    1		,    inputs_dict['global_attention_mask']		,    )

      a		:      int       =  True
      a		:      Union[str, Any]       =  self.model_tester.seq_length
      a		:      Optional[int]       =  self.model_tester.encoder_seq_length

      def check_decoder_attentions_output(__snake_case				:						Tuple			):
        a		:      str       =  outputs.decoder_attentions
        self.assertEqual(len(__snake_case			)		,    self.model_tester.num_hidden_layers			)
        self.assertListEqual(
            list(decoder_attentions[0].shape[-3:]			)		,    [self.model_tester.num_attention_heads, seq_length, seq_length]		,    )

      def check_encoder_attentions_output(__snake_case				:						int			):
        a		:      Union[str, Any]       =  [t.numpy() for t in outputs.encoder_attentions]
        a		:      Optional[Any]       =  [t.numpy() for t in outputs.encoder_global_attentions]
        self.assertEqual(len(__snake_case			)		,    self.model_tester.num_hidden_layers			)
        self.assertEqual(len(__snake_case			)		,    self.model_tester.num_hidden_layers			)
        self.assertListEqual(
            list(attentions[0].shape[-3:]			)		,    [self.model_tester.num_attention_heads, seq_length, seq_length]		,    )
        self.assertListEqual(
            list(global_attentions[0].shape[-3:]			)		,    [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]		,    )

      for model_class in self.all_model_classes:
        a		:      List[Any]       =  True
        a		:      Tuple       =  False
        a		:      Tuple       =  False
        a		:      Tuple       =  model_class(__snake_case			)
        a		:      Optional[int]       =  model(self._prepare_for_class(__snake_case		,    __snake_case			)			)
        a		:      Optional[int]       =  len(__snake_case			)
        self.assertEqual(config.output_hidden_states		,    __snake_case			)
        check_encoder_attentions_output(__snake_case			)

        if self.is_encoder_decoder:
          a		:      Union[str, Any]       =  model_class(__snake_case			)
          a		:      int       =  model(self._prepare_for_class(__snake_case		,    __snake_case			)			)
          self.assertEqual(config.output_hidden_states		,    __snake_case			)
          check_decoder_attentions_output(__snake_case			)

        # Check that output attentions can also be changed via the config
        del inputs_dict["output_attentions"]
        a		:      Union[str, Any]       =  True
        a		:      List[str]       =  model_class(__snake_case			)
        a		:      Dict       =  model(self._prepare_for_class(__snake_case		,    __snake_case			)			)
        self.assertEqual(config.output_hidden_states		,    __snake_case			)
        check_encoder_attentions_output(__snake_case			)

        # Check attention is always last and order is fine
        a		:      Optional[Any]       =  True
        a		:      Optional[Any]       =  True
        a		:      str       =  model_class(__snake_case			)
        a		:      Union[str, Any]       =  model(self._prepare_for_class(__snake_case		,    __snake_case			)			)

        self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,    len(__snake_case			)			)
        self.assertEqual(model.config.output_hidden_states		,    __snake_case			)
        check_encoder_attentions_output(__snake_case			)







    @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'			)
    def     lowercase_ (   self				:						str			):
      pass







    def     lowercase_ (   self				:						int			):
      # TODO: Head-masking not yet implement
      pass
def    lowerCamelCase__ (		_A       ):
  return tf.constant(_A				,    dtype=tf.intaa       )


lowerCAmelCase:      int		 =		1e-4

@slow
@require_tf
class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						List[str]			):
      a		:      List[str]       =  TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'			).led

      # change to intended input here
      a		:      int       =  _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]			)
      a		:      Any       =  _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]			)
      a		:      List[str]       =  prepare_led_inputs_dict(model.config		,    __snake_case		,    __snake_case			)
      a		:      Any       =  model(**__snake_case			)[0]
      a		:      Tuple       =  (1, 10_24, 7_68)
      self.assertEqual(output.shape		,    __snake_case			)
      # change to expected output here
      a		:      Optional[int]       =  tf.convert_to_tensor(
          [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]		,    )
      tf.debugging.assert_near(output[:, :3, :3]		,    __snake_case		,    atol=1e-3			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      int       =  TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'			)

      # change to intended input here
      a		:      Tuple       =  _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]			)
      a		:      int       =  _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]			)
      a		:      Tuple       =  prepare_led_inputs_dict(model.config		,    __snake_case		,    __snake_case			)
      a		:      int       =  model(**__snake_case			)[0]
      a		:      List[Any]       =  (1, 10_24, model.config.vocab_size)
      self.assertEqual(output.shape		,    __snake_case			)
      # change to expected output here
      a		:      Dict       =  tf.convert_to_tensor(
          [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]		,    )
      tf.debugging.assert_near(output[:, :3, :3]		,    __snake_case		,    atol=1e-3		,    rtol=1e-3			)




'''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			)			)