


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		:




							"""simple docstring"""


							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int=13   ,     lowerCamelCase	:  int=30   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Tuple=3   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[Any]=5   ,     lowerCamelCase	:  Tuple=4   ,     lowerCamelCase	:  Union[str, Any]=37   ,     lowerCamelCase	:  int="gelu"   ,     lowerCamelCase	:  Dict=0.1   ,     lowerCamelCase	:  str=0.1   ,     lowerCamelCase	:  List[Any]=10   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  List[str]=3   ,     lowerCamelCase	:  Dict=0.6   ,     lowerCamelCase	:  Dict=None   ,     )     ->   Any:
														__snake_case		:      str										=       parent
														__snake_case		:      Tuple										=       batch_size
														__snake_case		:      Optional[int]										=       image_size
														__snake_case		:      Dict										=       patch_size
														__snake_case		:      List[Any]										=       num_channels
														__snake_case		:      Tuple										=       is_training
														__snake_case		:      Dict										=       use_labels
														__snake_case		:      List[Any]										=       hidden_size
														__snake_case		:      List[str]										=       num_hidden_layers
														__snake_case		:      List[Any]										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      Any										=       hidden_act
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       type_sequence_label_size
														__snake_case		:      List[str]										=       initializer_range
														__snake_case		:      Optional[int]										=       mask_ratio
														__snake_case		:      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)
														__snake_case		:      List[str]										=       (image_size // patch_size) ** 2
														__snake_case		:      List[Any]										=       int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														__snake_case		:      int										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

														__snake_case		:      Optional[Any]										=       self.get_config()

														return config, pixel_values, labels

							def    __snake_case (							self	:  int    )     ->   Union[str, Any]:
														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=lowerCamelCase   ,     initializer_range=self.initializer_range   ,     mask_ratio=self.mask_ratio   ,     )

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

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int    )     ->   Optional[Any]:
														__snake_case		:      Dict										=       ViTMAEForPreTraining(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      List[Any]										=       model(lowerCamelCase    )
														__snake_case		:      List[Any]										=       (self.image_size // self.patch_size) ** 2
														__snake_case		:      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
														__snake_case		:      List[Any]										=       1
														__snake_case		:      int										=       ViTMAEForPreTraining(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Tuple										=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
														__snake_case		:      Optional[int]										=       model(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       self.patch_size**2
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, num_patches, expected_num_channels)    )






							def    __snake_case (							self	:  Dict    )     ->   Tuple:
														__snake_case		:      Optional[int]										=       self.prepare_config_and_inputs()
														__snake_case		:      Optional[int]										=       config_and_inputs
														__snake_case		:      Union[str, Any]										=       {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class     a		(_lowerCAmelCase					,    _lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							__UpperCAmelCase   :							List[Any]					  =  {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

							__UpperCAmelCase   :							Any					  =  False
							__UpperCAmelCase   :							List[str]					  =  False
							__UpperCAmelCase   :							int					  =  False
							__UpperCAmelCase   :							Any					  =  False

							def    __snake_case (							self	:  List[str]    )     ->   Dict:
														__snake_case		:      Union[str, Any]										=       ViTMAEModelTester(self    )
														__snake_case		:      List[Any]										=       ConfigTester(self   ,     config_class=lowerCamelCase   ,     has_text_modality=lowerCamelCase   ,     hidden_size=37    )

							def    __snake_case (							self	:  List[Any]    )     ->   int:
														self.config_tester.run_common_tests()

							@unittest.skip(reason="ViTMAE does not use inputs_embeds"    )
							def    __snake_case (							self	:  str    )     ->   Tuple:
														pass

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														__snake_case		:      Optional[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

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

							def    __snake_case (							self	:  List[str]    )     ->   Optional[int]:
														__snake_case		:      Dict										=       self.model_tester.prepare_config_and_inputs_for_common()

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

																					__snake_case		:      Optional[Any]										=       ["pixel_values"]
																					self.assertListEqual(arg_names[:1]   ,     lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														__snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Any:
														__snake_case		:      Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   str:
														# make masks reproducible
														np.random.seed(2    )

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

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

														super().check_pt_tf_models(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Optional[Any]:
														__snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs_for_common()

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

																					__snake_case		:      List[Any]										=       outputs[0].cpu().numpy()
																					__snake_case		:      Union[str, Any]										=       0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(lowerCamelCase    )
																												__snake_case		:      List[Any]										=       model_class.from_pretrained(lowerCamelCase    )
																												model.to(lowerCamelCase    )
																												# make random mask reproducible
																												torch.manual_seed(2    )
																												with torch.no_grad():
																																			__snake_case		:      str										=       model(**self._prepare_for_class(lowerCamelCase   ,     lowerCamelCase    )    )

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

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

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
							def    __snake_case (							self	:  List[str]    )     ->   Any:
														pass






							@slow
							def    __snake_case (							self	:  int    )     ->   str:
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case		:      List[Any]										=       ViTMAEModel.from_pretrained(lowerCamelCase    )
																					self.assertIsNotNone(lowerCamelCase    )







def     __lowercase					( ):
							__snake_case		:      Tuple										=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							return image

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




							"""simple docstring"""


							@cached_property
							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"    ) if is_vision_available() else None






							@slow
							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														# make random mask reproducible across the PT and TF model
														np.random.seed(2    )

														__snake_case		:      Optional[int]										=       ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"    ).to(lowerCamelCase    )

														__snake_case		:      List[Any]										=       self.default_image_processor
														__snake_case		:      str										=       prepare_img()
														__snake_case		:      List[str]										=       image_processor(images=lowerCamelCase   ,     return_tensors="pt"    ).to(lowerCamelCase    )

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

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

														# verify the logits
														__snake_case		:      Tuple										=       torch.Size((1, 196, 768)    )
														self.assertEqual(outputs.logits.shape   ,     lowerCamelCase    )

														__snake_case		:      Any										=       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(lowerCamelCase    )   ,     atol=1E-4    )    )





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_snake_case					:		int      =    "scheduler_config.json"

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  1
							__UpperCAmelCase   :							Tuple					  =  2
							__UpperCAmelCase   :							Union[str, Any]					  =  3
							__UpperCAmelCase   :							List[Any]					  =  4
							__UpperCAmelCase   :							Tuple					  =  5

@dataclass
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray

class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  SCHEDULER_CONFIG_NAME
							__UpperCAmelCase   :							Union[str, Any]					  =  ["dtype"]
							__UpperCAmelCase   :							Tuple					  =  []
							__UpperCAmelCase   :							int					  =  True

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  Dict[str, Any] = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  List[str]=False   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   List[str]:
														__snake_case			,      __snake_case		:      List[str]										=       cls.load_config(
														    pretrained_model_name_or_path=lowerCamelCase   ,     subfolder=lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase   ,     )
														__snake_case			,      __snake_case		:      Dict										=       cls.from_config(lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase    )

														if hasattr(lowerCamelCase   ,     "create_state"    ) and getattr(lowerCamelCase   ,     "has_state"   ,     lowerCamelCase    ):
																					__snake_case		:      Tuple										=       scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Union[str, os.PathLike]   ,     lowerCamelCase	:  bool = False   ,     **lowerCamelCase	:  List[Any]    )     ->   int:
														self.save_config(save_directory=lowerCamelCase   ,     push_to_hub=lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														return self._get_compatibles()






							@classmethod
							def    __snake_case (							cls	:  int    )     ->   Dict:
														__snake_case		:      Tuple										=       list(set([cls.__name__] + cls._compatibles    )    )
														__snake_case		:      int										=       importlib.import_module(__name__.split("."    )[0]    )
														__snake_case		:      Tuple										=       [
														    getattr(lowerCamelCase   ,     lowerCamelCase    ) for c in compatible_classes_str if hasattr(lowerCamelCase   ,     lowerCamelCase    )
														]
														return compatible_classes







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							assert len(__lowerCamelCase     ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase     ) - x.ndim)     )							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=0.9_9_9							,     __lowerCamelCase=jnp.floataa     ):

							def alpha_bar(__lowerCamelCase     ):
														return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

							__snake_case		:      List[Any]										=       []
							for i in range(__lowerCamelCase     ):
														__snake_case		:      Dict										=       i / num_diffusion_timesteps
														__snake_case		:      str										=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(__lowerCamelCase     ) / alpha_bar(__lowerCamelCase     )							,     __lowerCamelCase     )     )
							return jnp.array(__lowerCamelCase							,     dtype=__lowerCamelCase     )

@flax.struct.dataclass
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray

							@classmethod
							def    __snake_case (							cls	:  Union[str, Any]   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      Dict										=       scheduler.config

														if config.trained_betas is not None:
																					__snake_case		:      Dict										=       jnp.asarray(config.trained_betas   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "linear":
																					__snake_case		:      Optional[int]										=       jnp.linspace(config.beta_start   ,     config.beta_end   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					__snake_case		:      str										=       (
																					    jnp.linspace(
																					        config.beta_start**0.5   ,     config.beta_end**0.5   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
																					    ** 2
																					)
														elif config.beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					__snake_case		:      Optional[Any]										=       betas_for_alpha_bar(config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														else:
																					raise NotImplementedError(
																					    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'    )

														__snake_case		:      Any										=       1.0 - betas

														__snake_case		:      int										=       jnp.cumprod(lowerCamelCase   ,     axis=0    )

														return cls(
														    alphas=lowerCamelCase   ,     betas=lowerCamelCase   ,     alphas_cumprod=lowerCamelCase   ,     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       state.alphas_cumprod

							__snake_case		:      str										=       alphas_cumprod[timesteps] ** 0.5
							__snake_case		:      Dict										=       sqrt_alpha_prod.flatten()
							__snake_case		:      str										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							__snake_case		:      Tuple										=       (1 - alphas_cumprod[timesteps]) ** 0.5
							__snake_case		:      str										=       sqrt_one_minus_alpha_prod.flatten()
							__snake_case		:      Tuple										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Union[str, Any]										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      List[str]										=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Dict										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Optional[int]										=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity

