




"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase  :			List[str]        =       {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			List[Any]        =       [
		    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMAEForPreTraining',
		    'ViTMAELayer',
		    'ViTMAEModel',
		    'ViTMAEPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			List[str]        =       [
		    'TFViTMAEForPreTraining',
		    'TFViTMAEModel',
		    'TFViTMAEPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_mae import (
				    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMAEForPreTraining,
				    ViTMAELayer,
				    ViTMAEModel,
				    ViTMAEPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
		import sys

		_lowerCamelCase  :			Union[str, Any]        =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase  :			Any        =       False

class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					return 32







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Union[str, Any]	   =		VQModel(
					    block_out_channels=[32, 64]      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,     latent_channels=3      ,     num_vq_embeddings=self.num_embed      ,     vq_embed_dim=3      ,     )
					return model







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				int	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Optional[int]	   =		CLIPTextConfig(
					    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     layer_norm_eps=1E-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=10_00      ,     )
					return CLIPTextModel(_lowerCamelCase       )







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					torch.manual_seed(0       )

					A_  :				Optional[Any]	   =		12
					A_  :				Optional[int]	   =		12

					A_  :				int	   =		{
					    '''attention_bias''': True,
					    '''cross_attention_dim''': 32,
					    '''attention_head_dim''': height * width,
					    '''num_attention_heads''': 1,
					    '''num_vector_embeds''': self.num_embed,
					    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
					    '''norm_num_groups''': 32,
					    '''sample_size''': width,
					    '''activation_fn''': '''geglu-approximate''',
					}

					A_  :				Tuple	   =		TransformeraDModel(**_lowerCamelCase       )
					return model







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				Union[str, Any]	   =		self.dummy_vqvae
					A_  :				str	   =		self.dummy_text_encoder
					A_  :				List[Any]	   =		self.dummy_tokenizer
					A_  :				int	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[Any]	   =		LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase       )

					A_  :				Dict	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				List[Any]	   =		'''teddy bear playing in the pool'''

					A_  :				List[Any]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Any	   =		output.images

					A_  :				List[str]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[int]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				Optional[int]	   =		np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				int	   =		self.dummy_vqvae
					A_  :				List[str]	   =		self.dummy_text_encoder
					A_  :				Optional[Any]	   =		self.dummy_tokenizer
					A_  :				Any	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[int]	   =		LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_lowerCamelCase      ,     hidden_size=self.text_embedder_hidden_size      ,     length=tokenizer.model_max_length       )

					A_  :				int	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Any	   =		'''teddy bear playing in the pool'''

					A_  :				str	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Optional[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Tuple	   =		output.images

					A_  :				Optional[int]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[str]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[Any]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				str	   =		np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

@slow
@require_torch_gpu
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''       )

					A_  :				int	   =		VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''       )
					A_  :				Tuple	   =		pipeline.to(_lowerCamelCase       )
					pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					A_  :				Dict	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Union[str, Any]	   =		pipeline(
					    '''teddy bear playing in the pool'''      ,     num_images_per_prompt=1      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     )

					A_  :				Optional[int]	   =		output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image       ).max() < 2.0





