
"""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__          = False

class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Union[str, Any]			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







     @property
     def      lowercase	(							self			:			List[Any]			):
       return 12







     @property
     def      lowercase	(							self			:			str			):
       return 12







     @property
     def      lowercase	(							self			:			int			):
       return 32







     @property
     def      lowercase	(							self			:			Optional[Any]			):
       torch.manual_seed(0			)
       _snake_case					     =       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			:			int			):
       _snake_case					     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
       return tokenizer







     @property
     def      lowercase	(							self			:			Optional[Any]			):
       torch.manual_seed(0			)
       _snake_case					     =       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(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			List[Any]			):
       torch.manual_seed(0			)

       _snake_case					     =       12
       _snake_case					     =       12

       _snake_case					     =       {
           '''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''',
       }

       _snake_case					     =       TransformeraDModel(**_lowerCamelCase			)
       return model







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       '''cpu'''

       _snake_case					     =       self.dummy_vqvae
       _snake_case					     =       self.dummy_text_encoder
       _snake_case					     =       self.dummy_tokenizer
       _snake_case					     =       self.dummy_transformer
       _snake_case					     =       VQDiffusionScheduler(self.num_embed			)
       _snake_case					     =       LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase			)

       _snake_case					     =       VQDiffusionPipeline(
           vqvae=_lowerCamelCase    ,      text_encoder=_lowerCamelCase    ,      tokenizer=_lowerCamelCase    ,      transformer=_lowerCamelCase    ,      scheduler=_lowerCamelCase    ,      learned_classifier_free_sampling_embeddings=_lowerCamelCase    ,      )
       _snake_case					     =       pipe.to(_lowerCamelCase			)
       pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       '''teddy bear playing in the pool'''

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

       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(0			)
       _snake_case					     =       pipe(
           [prompt]    ,      generator=_lowerCamelCase    ,      output_type='''np'''    ,      return_dict=_lowerCamelCase    ,      num_inference_steps=2			)[0]

       _snake_case					     =       image[0, -3:, -3:, -1]
       _snake_case					     =       image_from_tuple[0, -3:, -3:, -1]

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

       _snake_case					     =       np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]			)

       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			:			Optional[Any]			):
       _snake_case					     =       '''cpu'''

       _snake_case					     =       self.dummy_vqvae
       _snake_case					     =       self.dummy_text_encoder
       _snake_case					     =       self.dummy_tokenizer
       _snake_case					     =       self.dummy_transformer
       _snake_case					     =       VQDiffusionScheduler(self.num_embed			)
       _snake_case					     =       LearnedClassifierFreeSamplingEmbeddings(
           learnable=_lowerCamelCase    ,      hidden_size=self.text_embedder_hidden_size    ,      length=tokenizer.model_max_length			)

       _snake_case					     =       VQDiffusionPipeline(
           vqvae=_lowerCamelCase    ,      text_encoder=_lowerCamelCase    ,      tokenizer=_lowerCamelCase    ,      transformer=_lowerCamelCase    ,      scheduler=_lowerCamelCase    ,      learned_classifier_free_sampling_embeddings=_lowerCamelCase    ,      )
       _snake_case					     =       pipe.to(_lowerCamelCase			)
       pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       '''teddy bear playing in the pool'''

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

       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(0			)
       _snake_case					     =       pipe(
           [prompt]    ,      generator=_lowerCamelCase    ,      output_type='''np'''    ,      return_dict=_lowerCamelCase    ,      num_inference_steps=2			)[0]

       _snake_case					     =       image[0, -3:, -3:, -1]
       _snake_case					     =       image_from_tuple[0, -3:, -3:, -1]

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

       _snake_case					     =       np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]			)

       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       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Any			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







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

       _snake_case					     =       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''			)
       _snake_case					     =       pipeline.to(_lowerCamelCase			)
       pipeline.set_progress_bar_config(disable=_lowerCamelCase			)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(0			)
       _snake_case					     =       pipeline(
           '''teddy bear playing in the pool'''    ,      num_images_per_prompt=1    ,      generator=_lowerCamelCase    ,      output_type='''np'''    ,      )

       _snake_case					     =       output.images[0]

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





"""simple docstring"""



UpperCAmelCase__          = {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}



