"""simple docstring"""






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class lowerCamelCase		(lowercase_     ,     lowercase_     ,     lowercase_     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		AltDiffusionPipeline
					a 						=		TEXT_TO_IMAGE_PARAMS
					a 						=		TEXT_TO_IMAGE_BATCH_PARAMS
					a 						=		TEXT_TO_IMAGE_IMAGE_PARAMS
					a 						=		TEXT_TO_IMAGE_IMAGE_PARAMS



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							Optional[Any]:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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") ,			cross_attention_dim=32 ,			)
									SCREAMING_SNAKE_CASE__			=					DDIMScheduler(
									    beta_start=0.0_0085 ,			beta_end=0.012 ,			beta_schedule="scaled_linear" ,			clip_sample=lowerCamelCase_ ,			set_alpha_to_one=lowerCamelCase_ ,			)
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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 ,			)

									# TODO: address the non-deterministic text encoder (fails for save-load tests)
									# torch.manual_seed(0)
									# text_encoder_config = RobertaSeriesConfig(
									#     hidden_size=32,
									#     project_dim=32,
									#     intermediate_size=37,
									#     layer_norm_eps=1e-05,
									#     num_attention_heads=4,
									#     num_hidden_layers=5,
									#     vocab_size=5002,
									# )
									# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					CLIPTextConfig(
									    bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=32 ,			projection_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=5002 ,			)
									SCREAMING_SNAKE_CASE__			=					CLIPTextModel(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE__			=					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"		)
									SCREAMING_SNAKE_CASE__			=					77

									SCREAMING_SNAKE_CASE__			=					{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components



					def       lowerCAmelCase_       (    self      :					Optional[Any] ,			_snake_case      :					Optional[int] ,			_snake_case      :					Optional[Any]=0		)				->							Union[str, Any]:



									if str(lowerCamelCase_		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__			=					torch.manual_seed(lowerCamelCase_		)
									else:
													SCREAMING_SNAKE_CASE__			=					torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs



					def       lowerCAmelCase_       (    self      :					str		)				->							List[str]:
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)



					def       lowerCAmelCase_       (    self      :					List[Any]		)				->							Optional[int]:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3		)



					def       lowerCAmelCase_       (    self      :					int		)				->							List[Any]:
									SCREAMING_SNAKE_CASE__			=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					RobertaSeriesConfig(
									    hidden_size=32 ,			project_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			vocab_size=5002 ,			)
									# TODO: remove after fixing the non-deterministic text encoder
									SCREAMING_SNAKE_CASE__			=					RobertaSeriesModelWithTransformation(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					text_encoder

									SCREAMING_SNAKE_CASE__			=					AltDiffusionPipeline(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe.to(lowerCamelCase_		)
									alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_inputs(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					"""A photo of an astronaut"""
									SCREAMING_SNAKE_CASE__			=					alt_pipe(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					output.images
									SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE__			=					np.array(
									    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]		)

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



					def       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							Union[str, Any]:
									SCREAMING_SNAKE_CASE__			=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=					PNDMScheduler(skip_prk_steps=lowerCamelCase_		)
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					RobertaSeriesConfig(
									    hidden_size=32 ,			project_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			vocab_size=5002 ,			)
									# TODO: remove after fixing the non-deterministic text encoder
									SCREAMING_SNAKE_CASE__			=					RobertaSeriesModelWithTransformation(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					text_encoder
									SCREAMING_SNAKE_CASE__			=					AltDiffusionPipeline(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe.to(lowerCamelCase_		)
									alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_inputs(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					output.images
									SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE__			=					np.array(
									    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]		)

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



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






					'''simple docstring'''


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



					def       lowerCAmelCase_       (    self      :					int		)				->							List[str]:
									SCREAMING_SNAKE_CASE__			=					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" ,			safety_checker=lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe.to(lowerCamelCase_		)
									alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

									SCREAMING_SNAKE_CASE__			=					"""A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe([prompt] ,			generator=lowerCamelCase_ ,			guidance_scale=6.0 ,			num_inference_steps=20 ,			output_type="np"		)

									SCREAMING_SNAKE_CASE__			=					output.images

									SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__			=					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]		)

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



					def       lowerCAmelCase_       (    self      :					Dict		)				->							List[str]:
									SCREAMING_SNAKE_CASE__			=					DDIMScheduler.from_pretrained("BAAI/AltDiffusion" ,			subfolder="scheduler"		)

									SCREAMING_SNAKE_CASE__			=					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" ,			scheduler=lowerCamelCase_ ,			safety_checker=lowerCamelCase_		)
									SCREAMING_SNAKE_CASE__			=					alt_pipe.to(lowerCamelCase_		)
									alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

									SCREAMING_SNAKE_CASE__			=					"""A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					alt_pipe([prompt] ,			generator=lowerCamelCase_ ,			num_inference_steps=2 ,			output_type="numpy"		)
									SCREAMING_SNAKE_CASE__			=					output.images

									SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__			=					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]		)

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











'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							except OSError:
									warnings.warn(
									    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
									    """ include a `tokenizer_class` attribute is deprecated and will be """
									    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
									    """ attribute to either your `config.json` or `tokenizer_config.json` """
									    """file to suppress this warning: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							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."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				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:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

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


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



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



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''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."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



