






'''simple docstring'''







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					['vqvae']






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,):
								super().__init__()
								self.register_modules(unet=_lowerCAmelCase   ,scheduler=_lowerCAmelCase   ,mel=_lowerCAmelCase   ,vqvae=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								return 50 if isinstance(self.scheduler   ,_lowerCAmelCase       ) else 10_00






		@torch.no_grad()
		def __call__(      self   ,_lowerCAmelCase = 1   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = None   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase=True   ,):
								lowerCamelCase__         =  steps or self.get_default_steps()
								self.scheduler.set_timesteps(_lowerCAmelCase       )
								lowerCamelCase__         =  step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size       ) == int:
														lowerCamelCase__         =  (self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
														lowerCamelCase__         =  randn_tensor(
														    (
														        batch_size,
														        self.unet.config.in_channels,
														        self.unet.config.sample_size[0],
														        self.unet.config.sample_size[1],
														    )   ,generator=_lowerCAmelCase   ,device=self.device   ,)
								lowerCamelCase__         =  noise
								lowerCamelCase__         =  None

								if audio_file is not None or raw_audio is not None:
														self.mel.load_audio(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  self.mel.audio_slice_to_image(_lowerCAmelCase       )
														lowerCamelCase__         =  np.frombuffer(input_image.tobytes()   ,dtype="""uint8"""       ).reshape(
														    (input_image.height, input_image.width)       )
														lowerCamelCase__         =  (input_image / 2_55) * 2 - 1
														lowerCamelCase__         =  torch.tensor(input_image[np.newaxis, :, :]   ,dtype=torch.float       ).to(self.device       )

														if self.vqvae is not None:
																				lowerCamelCase__         =  self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase   ,0       )       ).latent_dist.sample(
																				    generator=_lowerCAmelCase       )[0]
																				lowerCamelCase__         =  self.vqvae.config.scaling_factor * input_images

														if start_step > 0:
																				lowerCamelCase__         =  self.scheduler.add_noise(_lowerCAmelCase   ,_lowerCAmelCase   ,self.scheduler.timesteps[start_step - 1]       )

														lowerCamelCase__         =  (
														    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
														)
														lowerCamelCase__         =  int(mask_start_secs * pixels_per_second       )
														lowerCamelCase__         =  int(mask_end_secs * pixels_per_second       )
														lowerCamelCase__         =  self.scheduler.add_noise(_lowerCAmelCase   ,_lowerCAmelCase   ,torch.tensor(self.scheduler.timesteps[start_step:]       )       )

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]       )       ):
														if isinstance(self.unet   ,_lowerCAmelCase       ):
																				lowerCamelCase__         =  self.unet(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )["""sample"""]
														else:
																				lowerCamelCase__         =  self.unet(_lowerCAmelCase   ,_lowerCAmelCase       )["""sample"""]

														if isinstance(self.scheduler   ,_lowerCAmelCase       ):
																				lowerCamelCase__         =  self.scheduler.step(
																				    model_output=_lowerCAmelCase   ,timestep=_lowerCAmelCase   ,sample=_lowerCAmelCase   ,eta=_lowerCAmelCase   ,generator=_lowerCAmelCase   ,)["""prev_sample"""]
														else:
																				lowerCamelCase__         =  self.scheduler.step(
																				    model_output=_lowerCAmelCase   ,timestep=_lowerCAmelCase   ,sample=_lowerCAmelCase   ,generator=_lowerCAmelCase   ,)["""prev_sample"""]

														if mask is not None:
																				if mask_start > 0:
																										lowerCamelCase__         =  mask[:, step, :, :mask_start]
																				if mask_end > 0:
																										lowerCamelCase__         =  mask[:, step, :, -mask_end:]

								if self.vqvae is not None:
														# 0.18215 was scaling factor used in training to ensure unit variance
														lowerCamelCase__         =  1 / self.vqvae.config.scaling_factor * images
														lowerCamelCase__         =  self.vqvae.decode(_lowerCAmelCase       )["""sample"""]

								lowerCamelCase__         =  (images / 2 + 0.5).clamp(0   ,1       )
								lowerCamelCase__         =  images.cpu().permute(0   ,2   ,3   ,1       ).numpy()
								lowerCamelCase__         =  (images * 2_55).round().astype("""uint8"""       )
								lowerCamelCase__         =  list(
								    (Image.fromarray(_[:, :, 0]       ) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(_lowerCAmelCase   ,mode="""RGB"""       ).convert("""L"""       ) for _ in images)       )

								lowerCamelCase__         =  [self.mel.image_to_audio(_lowerCAmelCase       ) for _ in images]
								if not return_dict:
														return images, (self.mel.get_sample_rate(), audios)

								return BaseOutput(**AudioPipelineOutput(np.array(_lowerCAmelCase       )[:, np.newaxis, :]       )   ,**ImagePipelineOutput(_lowerCAmelCase       )       )






		@torch.no_grad()
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 50       ):
								assert isinstance(self.scheduler   ,_lowerCAmelCase       )
								self.scheduler.set_timesteps(_lowerCAmelCase       )
								lowerCamelCase__         =  np.array(
								    [np.frombuffer(image.tobytes()   ,dtype="""uint8"""       ).reshape((1, image.height, image.width)       ) for image in images]       )
								lowerCamelCase__         =  (sample / 2_55) * 2 - 1
								lowerCamelCase__         =  torch.Tensor(_lowerCAmelCase       ).to(self.device       )

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,(0,)       )       ):
														lowerCamelCase__         =  t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
														lowerCamelCase__         =  self.scheduler.alphas_cumprod[t]
														lowerCamelCase__         =  (
														    self.scheduler.alphas_cumprod[prev_timestep]
														    if prev_timestep >= 0
														    else self.scheduler.final_alpha_cumprod
														)
														lowerCamelCase__         =  1 - alpha_prod_t
														lowerCamelCase__         =  self.unet(_lowerCAmelCase   ,_lowerCAmelCase       )["""sample"""]
														lowerCamelCase__         =  (1 - alpha_prod_t_prev) ** 0.5 * model_output
														lowerCamelCase__         =  (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
														lowerCamelCase__         =  sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample






		@staticmethod
		def 			UpperCamelCase_					(      _lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  acos(torch.dot(torch.flatten(_lowerCAmelCase       )   ,torch.flatten(_lowerCAmelCase       )       ) / torch.norm(_lowerCAmelCase       ) / torch.norm(_lowerCAmelCase       )       )
								return sin((1 - alpha) * theta       ) * xa / sin(_lowerCAmelCase       ) + sin(alpha * theta       ) * xa / sin(_lowerCAmelCase       )









'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=30   ,_lowerCAmelCase=2   ,_lowerCAmelCase=3   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=32   ,_lowerCAmelCase=2   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=10   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=3   ,_lowerCAmelCase=0.6   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  patch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_labels
								lowerCamelCase__         =  hidden_size
								lowerCamelCase__         =  num_hidden_layers
								lowerCamelCase__         =  num_attention_heads
								lowerCamelCase__         =  intermediate_size
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  hidden_dropout_prob
								lowerCamelCase__         =  attention_probs_dropout_prob
								lowerCamelCase__         =  type_sequence_label_size
								lowerCamelCase__         =  initializer_range
								lowerCamelCase__         =  mask_ratio
								lowerCamelCase__         =  scope

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								lowerCamelCase__         =  None
								if self.use_labels:
														lowerCamelCase__         =  ids_tensor([self.batch_size]   ,self.type_sequence_label_size       )

								lowerCamelCase__         =  self.get_config()

								return config, pixel_values, labels






		def 			UpperCamelCase_					(      self       ):
								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   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_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=_lowerCAmelCase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  TFViTMAEModel(config=_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  TFViTMAEForPreTraining(_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								# expected sequence length = num_patches
								lowerCamelCase__         =  (self.image_size // self.patch_size) ** 2
								lowerCamelCase__         =  self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)       )

								# test greyscale images
								lowerCamelCase__         =  1
								lowerCamelCase__         =  TFViTMAEForPreTraining(_lowerCAmelCase       )

								lowerCamelCase__         =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								lowerCamelCase__         =  self.patch_size**2
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()
								((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  config_and_inputs
								lowerCamelCase__         =  {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCamelCase__    (a   ,a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		_UpperCamelCase				  =					{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFViTMAEModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=_lowerCAmelCase   ,has_text_modality=_lowerCAmelCase   ,hidden_size=37       )






		def 			UpperCamelCase_					(      self       ):
								self.config_tester.run_common_tests()






		@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)       )
														lowerCamelCase__         =  model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_lowerCAmelCase   ,tf.keras.layers.Layer       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  inspect.signature(model.call       )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowerCamelCase__         =  [*signature.parameters.keys()]

														lowerCamelCase__         =  ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								# make the mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														lowerCamelCase__         =  copy.deepcopy(self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )       )
														lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  outputs_dict[0].numpy()
														lowerCamelCase__         =  outputs_keywords[0].numpy()

														self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )   ,1E-6       )






		def 			UpperCamelCase_					(      self       ):
								# make the mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								def prepare_numpy_arrays(_lowerCAmelCase       ):
														lowerCamelCase__         =  {}
														for k, v in inputs_dict.items():
																				if tf.is_tensor(_lowerCAmelCase       ):
																										lowerCamelCase__         =  v.numpy()
																				else:
																										lowerCamelCase__         =  np.array(_lowerCAmelCase       )

														return inputs_np_dict

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  prepare_numpy_arrays(_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								# make masks reproducible
								np.random.seed(2       )

								lowerCamelCase__         =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								lowerCamelCase__         =  tf.constant(_lowerCAmelCase       )

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								lowerCamelCase__         =  tf_noise

								super().check_pt_tf_models(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  {
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__       ),)
								    for module_member_name in dir(_lowerCAmelCase       )
								    if module_member_name.endswith("""MainLayer"""       )
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len("""MainLayer"""       )] == model_class.__name__[: -len("""Model"""       )]
								    for module_member in (getattr(_lowerCAmelCase   ,_lowerCAmelCase       ),)
								    if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       )
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(_lowerCAmelCase   ,"""_keras_serializable"""   ,_lowerCAmelCase       )
								}

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								lowerCamelCase__         =  tf.convert_to_tensor(_lowerCAmelCase       )
								inputs_dict.update({"""noise""": noise}       )

								for main_layer_class in tf_main_layer_classes:
														lowerCamelCase__         =  main_layer_class(_lowerCAmelCase       )

														lowerCamelCase__         =  {
														    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
														}

														lowerCamelCase__         =  tf.keras.Model(_lowerCAmelCase   ,outputs=main_layer(_lowerCAmelCase       )       )
														lowerCamelCase__         =  model(_lowerCAmelCase       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																				lowerCamelCase__         =  os.path.join(_lowerCAmelCase   ,"""keras_model.h5"""       )
																				model.save(_lowerCAmelCase       )
																				lowerCamelCase__         =  tf.keras.models.load_model(
																				    _lowerCAmelCase   ,custom_objects={main_layer_class.__name__: main_layer_class}       )
																				assert isinstance(_lowerCAmelCase   ,tf.keras.Model       )
																				lowerCamelCase__         =  model(_lowerCAmelCase       )
																				self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														if model_class.__name__ == "TFViTMAEModel":
																				lowerCamelCase__         =  outputs.last_hidden_state.numpy()
																				lowerCamelCase__         =  0
														else:
																				lowerCamelCase__         =  outputs.logits.numpy()
																				lowerCamelCase__         =  0

														with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_pretrained(_lowerCAmelCase   ,saved_model=_lowerCAmelCase       )
																				lowerCamelCase__         =  model_class.from_pretrained(_lowerCAmelCase       )
																				lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

																				if model_class.__name__ == "TFViTMAEModel":
																										lowerCamelCase__         =  after_outputs["""last_hidden_state"""].numpy()
																										lowerCamelCase__         =  0
																				else:
																										lowerCamelCase__         =  after_outputs["""logits"""].numpy()
																										lowerCamelCase__         =  0

																				lowerCamelCase__         =  np.amax(np.abs(out_a - out_a       )       )
																				self.assertLessEqual(_lowerCAmelCase   ,1E-5       )






		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  model.get_config()
														# make sure that returned config is jsonifiable, which is required by keras
														json.dumps(_lowerCAmelCase       )
														lowerCamelCase__         =  model_class.from_config(model.get_config()       )
														# make sure it also accepts a normal config
														lowerCamelCase__         =  model_class.from_config(model.config       )
														lowerCamelCase__         =  new_model(_lowerCAmelCase       )  # Build model
														new_model.set_weights(model.get_weights()       )
														lowerCamelCase__         =  new_model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






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






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






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""       )
								self.assertIsNotNone(_lowerCAmelCase       )



def 						A__			(				):
						lowerCamelCase__         =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image





@require_tf
@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		@cached_property
		def 			UpperCamelCase_					(      self       ):
								return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""       ) if is_vision_available() else None






		@slow
		def 			UpperCamelCase_					(      self       ):
								# make random mask reproducible across the PT and TF model
								np.random.seed(2       )

								lowerCamelCase__         =  TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""       )

								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(images=_lowerCAmelCase   ,return_tensors="""tf"""       )

								# 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)
								lowerCamelCase__         =  ViTMAEConfig()
								lowerCamelCase__         =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(1, num_patches)       )

								# forward pass
								lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )

								# verify the logits
								lowerCamelCase__         =  tf.convert_to_tensor([1, 1_96, 7_68]       )
								self.assertEqual(outputs.logits.shape   ,_lowerCAmelCase       )

								lowerCamelCase__         =  tf.convert_to_tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

								tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,_lowerCAmelCase   ,atol=1E-4       )

