


"""simple docstring"""






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class 			__lowerCamelCase     (    lowerCAmelCase_ ):

     '''simple docstring'''
     A_				:		Optional[Any]			=						'EncodecFeatureExtractor'
     A_				:		Tuple			=						('T5Tokenizer', 'T5TokenizerFast')


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase							)				->							List[Any]:
          super().__init__(__a			, __a							)
          _a					= self.feature_extractor
          _a					= False


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase=None			, __UpperCAmelCase=None			, __UpperCAmelCase=True							)				->							Tuple:
          return self.tokenizer.get_decoder_prompt_ids(task=__a			, language=__a			, no_timestamps=__a							)


     def __call__(					self			, *__UpperCAmelCase			, **__UpperCAmelCase							)				->							str:





          if self._in_target_context_manager:
               return self.current_processor(*__a			, **__a							)

          _a					= kwargs.pop('''audio'''			, __a							)
          _a					= kwargs.pop('''sampling_rate'''			, __a							)
          _a					= kwargs.pop('''text'''			, __a							)
          if len(__a							) > 0:
               _a					= args[0]
               _a					= args[1:]

          if audio is None and text is None:
               raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

          if text is not None:
               _a					= self.tokenizer(__a			, **__a							)

          if audio is not None:
               _a					= self.feature_extractor(__a			, *__a			, sampling_rate=__a			, **__a							)

          if audio is None:
               return inputs

          elif text is None:
               return audio_inputs

          else:
               _a					= audio_inputs['input_values']
               if "padding_mask" in audio_inputs:
                    _a					= audio_inputs['padding_mask']
               return inputs


     def 							_UpperCAmelCase					(					self			, *__UpperCAmelCase			, **__UpperCAmelCase							)				->							List[Any]:
          _a					= kwargs.pop('''audio'''			, __a							)
          _a					= kwargs.pop('''padding_mask'''			, __a							)

          if len(__a							) > 0:
               _a					= args[0]
               _a					= args[1:]

          if audio_values is not None:
               return self._decode_audio(__a			, padding_mask=__a							)
          else:
               return self.tokenizer.batch_decode(*__a			, **__a							)


     def 							_UpperCAmelCase					(					self			, *__UpperCAmelCase			, **__UpperCAmelCase							)				->							str:
          return self.tokenizer.decode(*__a			, **__a							)





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							Optional[int]:
          _a					= to_numpy(__a							)
          _a					= audio_values.shape

          if padding_mask is None:
               return list(__a							)

          _a					= to_numpy(__a							)

          # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
          # token (so that the generated audio values are **not** treated as padded tokens)
          _a					= seq_len - padding_mask.shape[-1]
          _a					= 1 - self.feature_extractor.padding_value
          _a					= np.pad(__a			, ((0, 0), (0, difference))			, '''constant'''			, constant_values=__a							)

          _a					= audio_values.tolist()
          for i in range(__a							):
               _a					= np.asarray(audio_values[i]							)[
                   padding_mask[i][None, :] != self.feature_extractor.padding_value
               ]
               _a					= sliced_audio.reshape(__a			, -1							)

          return audio_values



'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionInpaintPipeline
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=9						,				out_channels=4						,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,				cross_attention_dim=32						,				attention_head_dim=(2, 4)						,				use_linear_projection=__a						,				)
						__a			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  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							)
						__a			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




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

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': init_image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a			:       str					    =  self.get_dummy_components()
						__a			:       Union[str, Any]					    =  StableDiffusionInpaintPipeline(**__a							)
						__a			:       List[Any]					    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  self.get_dummy_inputs(__a							)
						__a			:       Dict					    =  sd_pipe(**__a							).images
						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[Any]					    =  np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_inference_batch_single_identical(expected_max_diff=3E-3							)





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       str					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy'							)

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       Any					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       List[str]					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				torch_dtype=torch.floataa						,				safety_checker=__a						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       int					    =  torch.manual_seed(0							)
						__a			:       Optional[Any]					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       int					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5E-1





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       str					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Any					    =  PNDMScheduler.from_pretrained(__a						,				subfolder='scheduler'							)
						__a			:       str					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				safety_checker=__a						,				scheduler=__a						,				torch_dtype=torch.floataa						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



