
'''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


_UpperCAmelCase :   Optional[Any]			    =	False

class       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		def _lowercase	(     self  )					->     List[Any]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
		@property
		def _lowercase	(     self  )					->     Dict:
				return 12
		@property
		def _lowercase	(     self  )					->     Tuple:
				return 12
		@property
		def _lowercase	(     self  )					->     Tuple:
				return 32
		@property
		def _lowercase	(     self  )					->     List[str]:
				torch.manual_seed(0  )
				_UpperCamelCase							:			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 _lowercase	(     self  )					->     str:
				_UpperCamelCase							:			Optional[int]     =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
				return tokenizer
		@property
		def _lowercase	(     self  )					->     str:
				torch.manual_seed(0  )
				_UpperCamelCase							:			Dict     =   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=1000	,       )
				return CLIPTextModel(lowercase__  )
		@property
		def _lowercase	(     self  )					->     Optional[Any]:
				torch.manual_seed(0  )

				_UpperCamelCase							:			Tuple     =   12
				_UpperCamelCase							:			Dict     =   12

				_UpperCamelCase							:			List[str]     =   {
				    '''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''',
				}

				_UpperCamelCase							:			List[str]     =   TransformeraDModel(**lowercase__  )
				return model
		def _lowercase	(     self  )					->     List[str]:
				_UpperCamelCase							:			str     =   '''cpu'''

				_UpperCamelCase							:			List[str]     =   self.dummy_vqvae
				_UpperCamelCase							:			List[Any]     =   self.dummy_text_encoder
				_UpperCamelCase							:			Tuple     =   self.dummy_tokenizer
				_UpperCamelCase							:			List[str]     =   self.dummy_transformer
				_UpperCamelCase							:			Dict     =   VQDiffusionScheduler(self.num_embed  )
				_UpperCamelCase							:			Dict     =   LearnedClassifierFreeSamplingEmbeddings(learnable=lowercase__  )

				_UpperCamelCase							:			Any     =   VQDiffusionPipeline(
				    vqvae=lowercase__	,       text_encoder=lowercase__	,       tokenizer=lowercase__	,       transformer=lowercase__	,       scheduler=lowercase__	,       learned_classifier_free_sampling_embeddings=lowercase__	,       )
				_UpperCamelCase							:			int     =   pipe.to(lowercase__  )
				pipe.set_progress_bar_config(disable=lowercase__  )

				_UpperCamelCase							:			int     =   '''teddy bear playing in the pool'''

				_UpperCamelCase							:			str     =   torch.Generator(device=lowercase__  ).manual_seed(0  )
				_UpperCamelCase							:			Dict     =   pipe([prompt]	,       generator=lowercase__	,       num_inference_steps=2	,       output_type='''np'''  )
				_UpperCamelCase							:			List[str]     =   output.images

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

				_UpperCamelCase							:			Union[str, Any]     =   image[0, -3:, -3:, -1]
				_UpperCamelCase							:			Optional[int]     =   image_from_tuple[0, -3:, -3:, -1]

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

				_UpperCamelCase							:			int     =   np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]  )

				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 _lowercase	(     self  )					->     Union[str, Any]:
				_UpperCamelCase							:			str     =   '''cpu'''

				_UpperCamelCase							:			Optional[Any]     =   self.dummy_vqvae
				_UpperCamelCase							:			int     =   self.dummy_text_encoder
				_UpperCamelCase							:			Tuple     =   self.dummy_tokenizer
				_UpperCamelCase							:			str     =   self.dummy_transformer
				_UpperCamelCase							:			Any     =   VQDiffusionScheduler(self.num_embed  )
				_UpperCamelCase							:			Union[str, Any]     =   LearnedClassifierFreeSamplingEmbeddings(
				    learnable=lowercase__	,       hidden_size=self.text_embedder_hidden_size	,       length=tokenizer.model_max_length  )

				_UpperCamelCase							:			str     =   VQDiffusionPipeline(
				    vqvae=lowercase__	,       text_encoder=lowercase__	,       tokenizer=lowercase__	,       transformer=lowercase__	,       scheduler=lowercase__	,       learned_classifier_free_sampling_embeddings=lowercase__	,       )
				_UpperCamelCase							:			str     =   pipe.to(lowercase__  )
				pipe.set_progress_bar_config(disable=lowercase__  )

				_UpperCamelCase							:			int     =   '''teddy bear playing in the pool'''

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

				_UpperCamelCase							:			Tuple     =   torch.Generator(device=lowercase__  ).manual_seed(0  )
				_UpperCamelCase							:			Any     =   pipe(
				    [prompt]	,       generator=lowercase__	,       output_type='''np'''	,       return_dict=lowercase__	,       num_inference_steps=2  )[0]

				_UpperCamelCase							:			List[Any]     =   image[0, -3:, -3:, -1]
				_UpperCamelCase							:			str     =   image_from_tuple[0, -3:, -3:, -1]

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

				_UpperCamelCase							:			Union[str, Any]     =   np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]  )

				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       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		def _lowercase	(     self  )					->     Optional[Any]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()




		def _lowercase	(     self  )					->     Optional[int]:
				_UpperCamelCase							:			List[str]     =   load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''  )

				_UpperCamelCase							:			Tuple     =   VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''  )
				_UpperCamelCase							:			Any     =   pipeline.to(lowercase__  )
				pipeline.set_progress_bar_config(disable=lowercase__  )

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

				_UpperCamelCase							:			Tuple     =   output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image  ).max() < 2.0








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


