






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate






@dataclass(frozen=lowercase  )
class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    field(default='''audio-classification'''					,    metadata={'''include_in_asdict_even_if_is_default''': True}  )
      __snake_case	    =    Features({'''audio''': Audio()}  )
      __snake_case	    =    Features({'''labels''': ClassLabel}  )
      __snake_case	    =    "audio"
      __snake_case	    =    "labels"







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		str    )       ->Dict:




       """simple docstring"""


       if self.label_column not in features:
        raise ValueError(F"""Column {self.label_column} is not present in features."""    )
       if not isinstance(features[self.label_column]					,   __UpperCAmelCase    ):
        raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""    )
       a						    = copy.deepcopy(self    )
       a						    = self.label_schema.copy()
       a						    = features[self.label_column]
       a						    = label_schema
       return task_template







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Dict[str, str]:




       """simple docstring"""


       return {
           self.audio_column: "audio",
           self.label_column: "labels",
       }










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 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Dict    )       ->str:




       """simple docstring"""


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







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->Any:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		int    )       ->List[str]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = 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  __lowerCAmelCase   ( self :		str    )       ->str:




       """simple docstring"""


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







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




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = 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(__UpperCAmelCase    )







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = 12
       a						    = 12

       a						    = {
           '''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						    = TransformeraDModel(**__UpperCAmelCase    )
       return model







      def  __lowerCAmelCase   ( self :		Dict    )       ->Any:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.dummy_vqvae
       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_transformer
       a						    = VQDiffusionScheduler(self.num_embed    )
       a						    = LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCAmelCase    )

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

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

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

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

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

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

       a						    = 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  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       a						    = '''cpu'''

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

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

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

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

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

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

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

       a						    = 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  ):






      '''simple docstring'''




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




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


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

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

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

       a						    = output.images[0]

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


