'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''AutoFeatureExtractor'''
     SCREAMING_SNAKE_CASE__		 =    '''Speech2Text2Tokenizer'''







     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         super().__init__(_lowerCamelCase  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	self.feature_extractor
         __UpperCamelCase						:						Optional[int]		     =	False







     def __call__(		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*_lowerCamelCase  ,      **_lowerCamelCase     )

         if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
             __UpperCamelCase						:						Union[str, Any]		     =	kwargs.pop('raw_speech'     )
         else:
             __UpperCamelCase						:						Dict		     =	kwargs.pop('audio'  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	kwargs.pop('sampling_rate'  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Tuple		     =	kwargs.pop('text'  ,      _lowerCamelCase     )
         if len(_lowerCamelCase     ) > 0:
             __UpperCamelCase						:						str		     =	args[0]
             __UpperCamelCase						:						Dict		     =	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 audio is not None:
             __UpperCamelCase						:						List[str]		     =	self.feature_extractor(_lowerCamelCase  ,      *_lowerCamelCase  ,      sampling_rate=_lowerCamelCase  ,      **_lowerCamelCase     )
         if text is not None:
             __UpperCamelCase						:						Tuple		     =	self.tokenizer(_lowerCamelCase  ,      **_lowerCamelCase     )

         if text is None:
             return inputs
         elif audio is None:
             return encodings
         else:
             __UpperCamelCase						:						List[str]		     =	encodings['input_ids']
             return inputs







     def lowerCAmelCase  (		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         return self.tokenizer.batch_decode(*_lowerCamelCase  ,      **_lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         return self.tokenizer.decode(*_lowerCamelCase  ,      **_lowerCamelCase     )







     @contextmanager
     def lowerCAmelCase  (		self     ):
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'     )
         __UpperCamelCase						:						List[str]		     =	True
         __UpperCamelCase						:						Dict		     =	self.tokenizer
         yield
         __UpperCamelCase						:						Union[str, Any]		     =	self.feature_extractor
         __UpperCamelCase						:						Any		     =	False




'''simple docstring'''
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @property
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	3
         __UpperCamelCase						:						Tuple		     =	(3_2, 3_2)

         __UpperCamelCase						:						List[Any]		     =	floats_tensor((batch_size, num_channels) + sizes  ,      rng=random.Random(0     )     ).to(_lowerCamelCase     )
         return image







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	UNetaDModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=6  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
         return model







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						List[str]		     =	VQModel(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=3  ,      )
         return model







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	'cpu'
         __UpperCamelCase						:						Union[str, Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						List[str]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         __UpperCamelCase						:						Tuple		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.Generator(device=_lowerCamelCase     ).manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						str		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         __UpperCamelCase						:						str		     =	np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]     )

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







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						Optional[int]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         # put models in fp16
         __UpperCamelCase						:						str		     =	unet.half()
         __UpperCamelCase						:						Optional[Any]		     =	vqvae.half()

         __UpperCamelCase						:						Optional[int]		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[Any]		     =	ldm(_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

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






@slow
@require_torch
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool.png'     )
         __UpperCamelCase						:						List[Any]		     =	init_image.resize((6_4, 6_4)  ,      resample=PIL_INTERPOLATION['lanczos']     )

         __UpperCamelCase						:						List[str]		     =	LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'  ,      device_map='auto'     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2_0  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						List[Any]		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         __UpperCamelCase						:						str		     =	np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]     )

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



