





"""simple docstring"""





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__A											=						logging.get_logger(__name__)






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Any						    =   ["""audio_values""", """audio_mask"""]






       def __init__(       self       ,					__UpperCAmelCase=2_0_4_8       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=[1_6, 1_6]       ,					__UpperCAmelCase=1_2_8       ,					__UpperCAmelCase=4_4_1_0_0       ,					__UpperCAmelCase=8_6       ,					__UpperCAmelCase=2_0_4_8       ,					__UpperCAmelCase=0.0       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             feature_size=__UpperCAmelCase       ,					sampling_rate=__UpperCAmelCase       ,					padding_value=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Optional[Any]											=				spectrogram_length
         lowerCAmelCase__     :Tuple											=				num_channels
         lowerCAmelCase__     :Optional[Any]											=				patch_size
         lowerCAmelCase__     :Tuple											=				feature_size // self.patch_size[1]
         lowerCAmelCase__     :Optional[int]											=				n_fft
         lowerCAmelCase__     :Union[str, Any]											=				sampling_rate // hop_length_to_sampling_rate
         lowerCAmelCase__     :List[Any]											=				sampling_rate
         lowerCAmelCase__     :str											=				padding_value
         lowerCAmelCase__     :Tuple											=				mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,					num_mel_filters=__UpperCAmelCase       ,					min_frequency=0.0       ,					max_frequency=2_20_50.0       ,					sampling_rate=__UpperCAmelCase       ,					norm='slaney'       ,					mel_scale='slaney'       ,					).T






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				spectrogram(
             __UpperCAmelCase       ,					window_function(self.n_fft       ,					'hann' )       ,					frame_length=self.n_fft       ,					hop_length=self.hop_length       ,					power=2.0       ,					mel_filters=self.mel_filters.T       ,					log_mel='dB'       ,					db_range=80.0       ,					)
         lowerCAmelCase__     :List[str]											=				log_spec[:, :-1]
         lowerCAmelCase__     :Union[str, Any]											=				log_spec - 20.0
         lowerCAmelCase__     :Optional[Any]											=				np.clip(log_spec / 40.0       ,					-2.0       ,					0.0 ) + 1.0
         return log_spec






       def __call__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = False       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
             raise ValueError(
                 'This feature extractor is set to support sampling rate'
                 F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                 F" with {self.sampling_rate} and not {sampling_rate}." )
         else:
           logger.warning(
               'It is strongly recommended to pass the `sampling_rate` argument to this function. '
               'Failing to do so can result in silent errors that might be hard to debug.' )

         lowerCAmelCase__     :Optional[Any]											=				isinstance(__UpperCAmelCase       ,					np.ndarray ) and len(raw_speech.shape ) > 1
         if is_batched_numpy and len(raw_speech.shape ) > 2:
           raise ValueError(F"Only mono-channel audio is supported for input to {self}" )
         lowerCAmelCase__     :Tuple											=				is_batched_numpy or (
             isinstance(__UpperCAmelCase       ,					(list, tuple) ) and (isinstance(raw_speech[0]       ,					(np.ndarray, tuple, list) ))
         )
         if is_batched:
           lowerCAmelCase__     :Dict											=				[np.asarray([speech]       ,					dtype=np.floataa ).T for speech in raw_speech]
         elif not is_batched and not isinstance(__UpperCAmelCase       ,					np.ndarray ):
           lowerCAmelCase__     :Optional[Any]											=				np.asarray(__UpperCAmelCase       ,					dtype=np.floataa )
         elif isinstance(__UpperCAmelCase       ,					np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
           lowerCAmelCase__     :Tuple											=				raw_speech.astype(np.floataa )
         # always return batch
         if not is_batched:
           lowerCAmelCase__     :Tuple											=				[np.asarray([raw_speech] ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         lowerCAmelCase__     :Dict											=				[
             self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Dict											=				[np.asarray(__UpperCAmelCase       ,					dtype=np.floataa ) for feature in audio_features]

         # Create audio attention mask
         lowerCAmelCase__     :Optional[int]											=				max(
             [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
         if return_attention_mask:
           lowerCAmelCase__     :Dict											=				[
               (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
               + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
               for feature in audio_features
           ]
           lowerCAmelCase__     :Any											=				np.array(__UpperCAmelCase ).astype(np.floataa )

         # convert into correct format for padding
         lowerCAmelCase__     :Optional[Any]											=				max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         lowerCAmelCase__     :Dict											=				np.ones([len(__UpperCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
         lowerCAmelCase__     :Dict											=				padded_audio_features * self.padding_value
         for i in range(len(__UpperCAmelCase ) ):
           lowerCAmelCase__     :Union[str, Any]											=				audio_features[i]
           lowerCAmelCase__     :List[str]											=				feature

         # return as BatchFeature
         if return_attention_mask:
           lowerCAmelCase__     :Union[str, Any]											=				{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
         else:
           lowerCAmelCase__     :Tuple											=				{'audio_values': padded_audio_features}

         lowerCAmelCase__     :Optional[Any]											=				BatchFeature(data=__UpperCAmelCase       ,					tensor_type=__UpperCAmelCase )
         return encoded_inputs







"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __magic_name__	:List[str]						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # prior components
             'prior_tokenizer': prior_tokenizer,
             'prior_text_encoder': prior_text_encoder,
             'prior': prior,
             'prior_scheduler': prior_scheduler,
             # image noising components
             'image_normalizer': image_normalizer,
             'image_noising_scheduler': image_noising_scheduler,
             # regular denoising components
             'tokenizer': tokenizer,
             'text_encoder': text_encoder,
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'prior_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )

         lowerCAmelCase__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # stable unclip will oom when integration tests are run on a V100,
         # so turn on memory savings
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
