




'''simple docstring'''



from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__UpperCAmelCase      :Tuple			    =       logging.get_logger(__name__)



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       ["input_features", "attention_mask"]

     def __init__(    self					:		List[Any]							,			snake_case					:		Any=80							,			snake_case					:		Optional[int]=1_6000							,			snake_case					:		Any=0.0							,			snake_case					:		Optional[Any]=10							,			snake_case					:		int=25							,			snake_case					:		Union[str, Any]="hamming_window"							,			snake_case					:		str=3_2768.0							,			snake_case					:		List[str]=0.97							,			snake_case					:		Dict=1.0							,			snake_case					:		Tuple=True							,			snake_case					:		Any=True							,			snake_case					:		Optional[int]=False							,			**snake_case					:		List[str]							,			)						->							List[str]:
          super().__init__(feature_size=snake_case							,			sampling_rate=snake_case							,			padding_value=snake_case							,			**snake_case      )

          __UpperCAmelCase     :			Dict  							=      feature_size
          __UpperCAmelCase     :			Any  							=      sampling_rate
          __UpperCAmelCase     :			Tuple  							=      padding_value
          __UpperCAmelCase     :			Optional[int]  							=      hop_length
          __UpperCAmelCase     :			str  							=      win_length
          __UpperCAmelCase     :			Tuple  							=      frame_signal_scale
          __UpperCAmelCase     :			Tuple  							=      preemphasis_coeff
          __UpperCAmelCase     :			Any  							=      mel_floor
          __UpperCAmelCase     :			Dict  							=      normalize_means
          __UpperCAmelCase     :			Union[str, Any]  							=      normalize_vars
          __UpperCAmelCase     :			Optional[int]  							=      win_function
          __UpperCAmelCase     :			Tuple  							=      return_attention_mask

          __UpperCAmelCase     :			int  							=      win_length * sampling_rate // 1000
          __UpperCAmelCase     :			List[Any]  							=      hop_length * sampling_rate // 1000

          __UpperCAmelCase     :			List[Any]  							=      optimal_fft_length(self.sample_size      )
          __UpperCAmelCase     :			int  							=      (self.n_fft // 2) + 1

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		np.array      )						->							np.ndarray:
          if self.win_function == "hamming_window":
               __UpperCAmelCase     :			Union[str, Any]  							=      window_function(window_length=self.sample_size							,			name=self.win_function							,			periodic=snake_case      )
          else:
               __UpperCAmelCase     :			Optional[int]  							=      window_function(window_length=self.sample_size							,			name=self.win_function      )

          __UpperCAmelCase     :			Tuple  							=      mel_filter_bank(
              num_frequency_bins=self.n_freqs							,			num_mel_filters=self.feature_size							,			min_frequency=0.0							,			max_frequency=self.sampling_rate / 2.0							,			sampling_rate=self.sampling_rate							,			)

          __UpperCAmelCase     :			int  							=      spectrogram(
              one_waveform * self.frame_signal_scale							,			window=snake_case							,			frame_length=self.sample_size							,			hop_length=self.sample_stride							,			fft_length=self.n_fft							,			center=snake_case							,			preemphasis=self.preemphasis_coeff							,			mel_filters=snake_case							,			mel_floor=self.mel_floor							,			log_mel='''log'''							,			)
          return msfc_features.T

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		Any							,			snake_case					:		int      )						->							Dict:
          # make sure we normalize float32 arrays
          if self.normalize_means:
               __UpperCAmelCase     :			int  							=      x[:input_length].mean(axis=0      )
               __UpperCAmelCase     :			List[str]  							=      np.subtract(snake_case							,			snake_case      )
          if self.normalize_vars:
               __UpperCAmelCase     :			List[str]  							=      x[:input_length].std(axis=0      )
               __UpperCAmelCase     :			Dict  							=      np.divide(snake_case							,			snake_case      )

          if input_length < x.shape[0]:
               __UpperCAmelCase     :			int  							=      padding_value

          # make sure array is in float32
          __UpperCAmelCase     :			Any  							=      x.astype(np.floataa      )

          return x

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[np.ndarray]							,			snake_case					:		Optional[np.ndarray] = None      )						->							List[np.ndarray]:
          __UpperCAmelCase     :			Union[str, Any]  							=      attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(snake_case							,			snake_case							,			self.padding_value      ) for x, n in zip(snake_case							,			snake_case      )]






     def __call__(    self					:		int							,			snake_case					:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,			snake_case					:		Union[bool, str, PaddingStrategy] = False							,			snake_case					:		Optional[int] = None							,			snake_case					:		bool = False							,			snake_case					:		Optional[int] = None							,			snake_case					:		Optional[bool] = None							,			snake_case					:		Optional[Union[str, TensorType]] = None							,			snake_case					:		Optional[int] = None							,			**snake_case					:		List[Any]							,			)						->							BatchFeature:

          if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                        f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                        f' {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.'''      )

          __UpperCAmelCase     :			List[Any]  							=      isinstance(snake_case							,			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}'      )
          __UpperCAmelCase     :			List[Any]  							=      is_batched_numpy or (
              isinstance(snake_case							,			(list, tuple)      ) and (isinstance(raw_speech[0]							,			(np.ndarray, tuple, list)      ))
          )

          if is_batched:
               __UpperCAmelCase     :			Union[str, Any]  							=      [np.asarray(snake_case							,			dtype=np.floataa      ) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case							,			np.ndarray      ):
               __UpperCAmelCase     :			str  							=      np.asarray(snake_case							,			dtype=np.floataa      )
          elif isinstance(snake_case							,			np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
               __UpperCAmelCase     :			Tuple  							=      raw_speech.astype(np.floataa      )

          # always return batch
          if not is_batched:
               __UpperCAmelCase     :			List[str]  							=      [raw_speech]

          # extract fbank features
          __UpperCAmelCase     :			Any  							=      [self._extract_mfsc_features(snake_case      ) for one_waveform in raw_speech]

          # convert into correct format for padding
          __UpperCAmelCase     :			Union[str, Any]  							=      BatchFeature({'''input_features''': features}      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.pad(
              snake_case							,			padding=snake_case							,			max_length=snake_case							,			truncation=snake_case							,			pad_to_multiple_of=snake_case							,			return_attention_mask=snake_case							,			**snake_case							,			)
          # make sure list is in array format
          __UpperCAmelCase     :			Tuple  							=      padded_inputs.get('''input_features'''      )
          if isinstance(input_features[0]							,			snake_case      ):
               __UpperCAmelCase     :			Tuple  							=      [np.asarray(snake_case							,			dtype=np.floataa      ) for feature in input_features]

          __UpperCAmelCase     :			Union[str, Any]  							=      padded_inputs.get('''attention_mask'''      )
          if attention_mask is not None:
               __UpperCAmelCase     :			Any  							=      [np.asarray(snake_case							,			dtype=np.intaa      ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
               __UpperCAmelCase     :			Optional[Any]  							=      (
                   np.array(snake_case							,			dtype=np.intaa      )
                   if self._get_padding_strategies(snake_case							,			max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
                   and padding
                   else None
               )
               __UpperCAmelCase     :			Tuple  							=      self.normalize(
                   padded_inputs['''input_features''']							,			attention_mask=snake_case      )

          if return_tensors is not None:
               __UpperCAmelCase     :			List[str]  							=      padded_inputs.convert_to_tensors(snake_case      )

          return padded_inputs



'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       StableDiffusionXLImgaImgPipeline
     SCREAMING_SNAKE_CASE	:			Tuple							       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       PipelineTesterMixin.required_optional_params - {"latents"}
     SCREAMING_SNAKE_CASE	:			int							       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE	:			Any							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Optional[Any]  							=      UNetaDConditionModel(
              block_out_channels=(32, 64)							,			layers_per_block=2							,			sample_size=32							,			in_channels=4							,			out_channels=4							,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,			attention_head_dim=(2, 4)							,			use_linear_projection=snake_case							,			addition_embed_type='''text_time'''							,			addition_time_embed_dim=8							,			transformer_layers_per_block=(1, 2)							,			projection_class_embeddings_input_dim=80							,			cross_attention_dim=64							,			)
          __UpperCAmelCase     :			int  							=      EulerDiscreteScheduler(
              beta_start=0.00_085							,			beta_end=0.012							,			steps_offset=1							,			beta_schedule='''scaled_linear'''							,			timestep_spacing='''leading'''							,			)
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Tuple  							=      AutoencoderKL(
              block_out_channels=[32, 64]							,			in_channels=3							,			out_channels=3							,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,			latent_channels=4							,			sample_size=128							,			)
          torch.manual_seed(0      )
          __UpperCAmelCase     :			int  							=      CLIPTextConfig(
              bos_token_id=0							,			eos_token_id=2							,			hidden_size=32							,			intermediate_size=37							,			layer_norm_eps=1E-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1000							,			hidden_act='''gelu'''							,			projection_dim=32							,			)
          __UpperCAmelCase     :			Tuple  							=      CLIPTextModel(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      CLIPTextModelWithProjection(snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      {
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''text_encoder_2''': text_encoder_a,
              '''tokenizer_2''': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Optional[int]							,			snake_case					:		List[str]=0      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      floats_tensor((1, 3, 32, 32)							,			rng=random.Random(snake_case      )      ).to(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      image / 2 + 0.5
          if str(snake_case      ).startswith('''mps'''      ):
               __UpperCAmelCase     :			List[str]  							=      torch.manual_seed(snake_case      )
          else:
               __UpperCAmelCase     :			Any  							=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      {
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 5.0,
              '''output_type''': '''numpy''',
              '''strength''': 0.75,
          }
          return inputs

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			Any  							=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Any  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			int  							=      sd_pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __UpperCAmelCase     :			Any  							=      np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Optional[int]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[Any]:
          pass






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			List[Any]  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          # forward without prompt embeds
          __UpperCAmelCase     :			Tuple  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			Optional[int]  							=      negative_prompt
          __UpperCAmelCase     :			Tuple  							=      3 * [inputs['''prompt''']]

          __UpperCAmelCase     :			Optional[Any]  							=      sd_pipe(**snake_case      )
          __UpperCAmelCase     :			int  							=      output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			str  							=      3 * [inputs.pop('''prompt'''      )]

          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      sd_pipe.encode_prompt(snake_case							,			negative_prompt=snake_case      )

          __UpperCAmelCase     :			Dict  							=      sd_pipe(
              **snake_case							,			prompt_embeds=snake_case							,			negative_prompt_embeds=snake_case							,			pooled_prompt_embeds=snake_case							,			negative_pooled_prompt_embeds=snake_case							,			)
          __UpperCAmelCase     :			List[str]  							=      output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4



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


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Any							,			snake_case					:		str="cpu"							,			snake_case					:		Tuple=torch.floataa							,			snake_case					:		List[str]=0      )						->							Tuple:
          __UpperCAmelCase     :			Optional[Any]  							=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )
          __UpperCAmelCase     :			int  							=      np.random.RandomState(snake_case      ).standard_normal((1, 4, 64, 64)      )
          __UpperCAmelCase     :			str  							=      torch.from_numpy(snake_case      ).to(device=snake_case							,			dtype=snake_case      )
          __UpperCAmelCase     :			str  							=      {
              '''prompt''': '''a photograph of an astronaut riding a horse''',
              '''latents''': latents,
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs






     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[Any]:
          __UpperCAmelCase     :			str  							=      DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''      )
          pipe.to(snake_case      )
          pipe.set_progress_bar_config(disable=snake_case      )

          __UpperCAmelCase     :			str  							=      self.get_inputs(snake_case      )
          __UpperCAmelCase     :			Dict  							=      pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			Tuple  							=      np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]      )
          assert np.abs(image_slice - expected_slice      ).max() < 7E-3