import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''MCTCTFeatureExtractor'''
				lowerCamelCase_    = '''AutoTokenizer'''
				def __init__(						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						super().__init__(lowercase		,     lowercase )
						A_		:    Dict         =				self.feature_extractor
						A_		:    Tuple         =				False
				def __call__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





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

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

						if text is None:
								return inputs
						elif audio is None:
								return encodings
						else:
								A_		:    Tuple         =				encodings['input_ids']
								return inputs
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						return self.tokenizer.batch_decode(*lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						if self._in_target_context_manager:
								return self.current_processor.pad(*lowercase		,     **lowercase )

						A_		:    Optional[int]         =				kwargs.pop('input_features'		,     lowercase )
						A_		:    List[Any]         =				kwargs.pop('labels'		,     lowercase )
						if len(lowercase ) > 0:
								A_		:    str         =				args[0]
								A_		:    Union[str, Any]         =				args[1:]

						if input_features is not None:
								A_		:    List[Any]         =				self.feature_extractor.pad(lowercase		,     *lowercase		,     **lowercase )
						if labels is not None:
								A_		:    List[str]         =				self.tokenizer.pad(lowercase		,     **lowercase )

						if labels is None:
								return input_features
						elif input_features is None:
								return labels
						else:
								A_		:    int         =				labels['input_ids']
								return input_features
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						return self.tokenizer.decode(*lowercase		,     **lowercase )







				@contextmanager
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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.' )
						A_		:    Optional[int]         =				True
						A_		:    str         =				self.tokenizer
						yield
						A_		:    Any         =				self.feature_extractor
						A_		:    Dict         =				False







import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				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
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=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		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





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


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

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

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






