

'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= KandinskyVaaPipeline
  __lowerCAmelCase       :			Dict     	= [
      "image_embeds",
      "negative_image_embeds",
  ]
  __lowerCAmelCase       :			Union[str, Any]     	= ["image_embeds", "negative_image_embeds"]
  __lowerCAmelCase       :			Dict     	= [
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  __lowerCAmelCase       :			Any     	= False
  @property
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    return 3_2
  @property
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    return 3_2
  @property
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    return self.time_input_dim
  @property
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    return self.time_input_dim * 4
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return 1_0_0
  @property
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    torch.manual_seed(0  )

    UpperCAmelCase    				=						{
        'in_channels': 4,
        # Out channels is double in channels because predicts mean and variance
        'out_channels': 8,
        'addition_embed_type': 'image',
        'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
        'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
        'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
        'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
        'layers_per_block': 1,
        'encoder_hid_dim': self.text_embedder_hidden_size,
        'encoder_hid_dim_type': 'image_proj',
        'cross_attention_dim': self.cross_attention_dim,
        'attention_head_dim': 4,
        'resnet_time_scale_shift': 'scale_shift',
        'class_embed_type': None,
    }

    UpperCAmelCase    				=						UNetaDConditionModel(**a_  )
    return model
  @property
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    return {
        "block_out_channels": [3_2, 6_4],
        "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
        "in_channels": 3,
        "latent_channels": 4,
        "layers_per_block": 1,
        "norm_num_groups": 8,
        "norm_type": "spatial",
        "num_vq_embeddings": 1_2,
        "out_channels": 3,
        "up_block_types": [
            "AttnUpDecoderBlock2D",
            "UpDecoderBlock2D",
        ],
        "vq_embed_dim": 4,
    }
  @property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    torch.manual_seed(0  )
    UpperCAmelCase    				=						VQModel(**self.dummy_movq_kwargs  )
    return model
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.dummy_unet
    UpperCAmelCase    				=						self.dummy_movq

    UpperCAmelCase    				=						DDIMScheduler(
        num_train_timesteps=1_0_0_0 ,			beta_schedule='linear' ,			beta_start=0.0_0085 ,			beta_end=0.012 ,			clip_sample=a_ ,			set_alpha_to_one=a_ ,			steps_offset=1 ,			prediction_type='epsilon' ,			thresholding=a_ ,			)

    UpperCAmelCase    				=						{
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }
    return components
  def     snake_case_      (  self ,			a_ ,			a_=0  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(a_  )  ).to(a_  )
    UpperCAmelCase    				=						floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(seed + 1  )  ).to(
        a_  )
    if str(a_  ).startswith('mps'  ):
      UpperCAmelCase    				=						torch.manual_seed(a_  )
    else:
      UpperCAmelCase    				=						torch.Generator(device=a_  ).manual_seed(a_  )
    UpperCAmelCase    				=						{
        'image_embeds': image_embeds,
        'negative_image_embeds': negative_image_embeds,
        'generator': generator,
        'height': 6_4,
        'width': 6_4,
        'guidance_scale': 4.0,
        'num_inference_steps': 2,
        'output_type': 'np',
    }
    return inputs





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


    """simple docstring"""
    UpperCAmelCase    				=						'cpu'

    UpperCAmelCase    				=						self.get_dummy_components()

    UpperCAmelCase    				=						self.pipeline_class(**a_  )
    UpperCAmelCase    				=						pipe.to(a_  )

    pipe.set_progress_bar_config(disable=a_  )

    UpperCAmelCase    				=						pipe(**self.get_dummy_inputs(a_  )  )
    UpperCAmelCase    				=						output.images

    UpperCAmelCase    				=						pipe(
        **self.get_dummy_inputs(a_  ) ,			return_dict=a_ ,			)[0]

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

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase    				=						np.array(
        [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]  )

    assert (
        np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
    ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

    assert (
        np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
    ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




@slow
@require_torch_gpu
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'  )

    UpperCAmelCase    				=						KandinskyVaaPriorPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-prior' ,			torch_dtype=torch.floataa  )
    pipe_prior.to(a_  )

    UpperCAmelCase    				=						KandinskyVaaPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-decoder' ,			torch_dtype=torch.floataa  )
    UpperCAmelCase    				=						pipeline.to(a_  )
    pipeline.set_progress_bar_config(disable=a_  )

    UpperCAmelCase    				=						'red cat, 4k photo'

    UpperCAmelCase    				=						torch.Generator(device='cuda'  ).manual_seed(0  )
    UpperCAmelCase				,   UpperCAmelCase    				=						pipe_prior(
        a_ ,			generator=a_ ,			num_inference_steps=5 ,			negative_prompt='' ,			).to_tuple()

    UpperCAmelCase    				=						torch.Generator(device='cuda'  ).manual_seed(0  )
    UpperCAmelCase    				=						pipeline(
        image_embeds=a_ ,			negative_image_embeds=a_ ,			generator=a_ ,			num_inference_steps=1_0_0 ,			output_type='np' ,			)

    UpperCAmelCase    				=						output.images[0]

    assert image.shape == (5_1_2, 5_1_2, 3)

    assert_mean_pixel_difference(a_ ,			a_  )





'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





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


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key].squeeze().tolist()  )


