






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


lowercase_          =		False
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       str:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Tuple:





     """simple docstring"""







     return 12


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Union[str, Any]:





     """simple docstring"""







     return 12


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Tuple:





     """simple docstring"""







     return 32


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Optional[Any]:





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Optional[int]:





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  CLIPTextConfig(
         bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=self.text_embedder_hidden_size      ,					intermediate_size=37      ,					layer_norm_eps=1E-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1_000      ,					)
     return CLIPTextModel(__lowerCamelCase	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[Any]:





     """simple docstring"""







     torch.manual_seed(0	)

     lowercase__           =  12
     lowercase__           =  12

     lowercase__           =  {
         '''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__           =  TransformeraDModel(**__lowerCamelCase	)
     return model


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  '''cpu'''

     lowercase__           =  self.dummy_vqvae
     lowercase__           =  self.dummy_text_encoder
     lowercase__           =  self.dummy_tokenizer
     lowercase__           =  self.dummy_transformer
     lowercase__           =  VQDiffusionScheduler(self.num_embed	)
     lowercase__           =  LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase	)

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

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

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

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

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

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  '''cpu'''

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

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

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

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

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

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

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

     lowercase__           =  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     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       int:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







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

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

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

     lowercase__           =  output.images[0]

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









from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowercase_          =		{
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
      from .config import (
          EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
          OnnxConfig,
          OnnxConfigWithPast,
          OnnxSeqaSeqConfigWithPast,
          PatchingSpec,
      )
      from .convert import export, validate_model_outputs
      from .features import FeaturesManager
      from .utils import ParameterFormat, compute_serialized_parameters_size

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


