"""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


__SCREAMING_SNAKE_CASE    	=False







class        UpperCamelCase		(			unittest.TestCase  ):
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return 12
   @property
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        return 12
   @property
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        return 32
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        torch.manual_seed(0			)
        lowercase_	:				List[str]													=	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 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
        return tokenizer
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        torch.manual_seed(0			)
        lowercase_	:				List[str]													=	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(__UpperCamelCase			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        torch.manual_seed(0			)

        lowercase_	:				Optional[Any]													=	12
        lowercase_	:				Dict													=	12

        lowercase_	:				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',
        }

        lowercase_	:				Tuple													=	TransformeraDModel(**__UpperCamelCase			)
        return model
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				int													=	'cpu'

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

        lowercase_	:				Tuple													=	VQDiffusionPipeline(
            vqvae=__UpperCamelCase     ,text_encoder=__UpperCamelCase     ,tokenizer=__UpperCamelCase     ,transformer=__UpperCamelCase     ,scheduler=__UpperCamelCase     ,learned_classifier_free_sampling_embeddings=__UpperCamelCase     ,)
        lowercase_	:				Optional[Any]													=	pipe.to(__UpperCamelCase			)
        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				List[str]													=	'teddy bear playing in the pool'

        lowercase_	:				Dict													=	torch.Generator(device=__UpperCamelCase			).manual_seed(0			)
        lowercase_	:				Optional[int]													=	pipe([prompt]     ,generator=__UpperCamelCase     ,num_inference_steps=2     ,output_type='np'			)
        lowercase_	:				Optional[int]													=	output.images

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

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

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

        lowercase_	:				Optional[Any]													=	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 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	'cpu'

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

        lowercase_	:				Dict													=	VQDiffusionPipeline(
            vqvae=__UpperCamelCase     ,text_encoder=__UpperCamelCase     ,tokenizer=__UpperCamelCase     ,transformer=__UpperCamelCase     ,scheduler=__UpperCamelCase     ,learned_classifier_free_sampling_embeddings=__UpperCamelCase     ,)
        lowercase_	:				Optional[Any]													=	pipe.to(__UpperCamelCase			)
        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				Dict													=	'teddy bear playing in the pool'

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

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

        lowercase_	:				str													=	image[0, -3:, -3:, -1]
        lowercase_	:				int													=	image_from_tuple[0, -3:, -3:, -1]

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

        lowercase_	:				Optional[int]													=	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        UpperCamelCase		(			unittest.TestCase  ):
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'			)

        lowercase_	:				Dict													=	VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'			)
        lowercase_	:				Optional[Any]													=	pipeline.to(__UpperCamelCase			)
        pipeline.set_progress_bar_config(disable=__UpperCamelCase			)

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

        lowercase_	:				str													=	output.images[0]

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


"""simple docstring"""







__SCREAMING_SNAKE_CASE    	=[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

