'''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 							a__(   lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			AltDiffusionPipeline
			UpperCAmelCase_     :	Dict						=			TEXT_TO_IMAGE_PARAMS
			UpperCAmelCase_     :	Union[str, Any]						=			TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCAmelCase_     :	Optional[Any]						=			TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCAmelCase_     :	Optional[Any]						=			TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    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			,       )
					lowerCAmelCase     =    DDIMScheduler(
					    beta_start=0.00085			,       beta_end=0.012			,       beta_schedule="""scaled_linear"""			,       clip_sample=__lowerCAmelCase			,       set_alpha_to_one=__lowerCAmelCase			,       )
					torch.manual_seed(0)
					lowerCAmelCase     =    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)
					lowerCAmelCase     =    CLIPTextConfig(
					    bos_token_id=0			,       eos_token_id=2			,       hidden_size=32			,       projection_dim=32			,       intermediate_size=37			,       layer_norm_eps=1E-0_5			,       num_attention_heads=4			,       num_hidden_layers=5			,       pad_token_id=1			,       vocab_size=5002			,       )
					lowerCAmelCase     =    CLIPTextModel(__lowerCAmelCase)

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

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase     =    torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """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 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""  # ensure determinism for the device-dependent torch.Generator

					lowerCAmelCase     =    self.get_dummy_components()
					torch.manual_seed(0)
					lowerCAmelCase     =    RobertaSeriesConfig(
					    hidden_size=32			,       project_dim=32			,       intermediate_size=37			,       layer_norm_eps=1E-0_5			,       num_attention_heads=4			,       num_hidden_layers=5			,       vocab_size=5002			,       )
					# TODO: remove after fixing the non-deterministic text encoder
					lowerCAmelCase     =    RobertaSeriesModelWithTransformation(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder

					lowerCAmelCase     =    AltDiffusionPipeline(**__lowerCAmelCase)
					lowerCAmelCase     =    alt_pipe.to(__lowerCAmelCase)
					alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

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

					assert image.shape == (1, 64, 64, 3)
					lowerCAmelCase     =    np.array(
					    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093])

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







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""  # ensure determinism for the device-dependent torch.Generator

					lowerCAmelCase     =    self.get_dummy_components()
					lowerCAmelCase     =    PNDMScheduler(skip_prk_steps=__lowerCAmelCase)
					torch.manual_seed(0)
					lowerCAmelCase     =    RobertaSeriesConfig(
					    hidden_size=32			,       project_dim=32			,       intermediate_size=37			,       layer_norm_eps=1E-0_5			,       num_attention_heads=4			,       num_hidden_layers=5			,       vocab_size=5002			,       )
					# TODO: remove after fixing the non-deterministic text encoder
					lowerCAmelCase     =    RobertaSeriesModelWithTransformation(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder
					lowerCAmelCase     =    AltDiffusionPipeline(**__lowerCAmelCase)
					lowerCAmelCase     =    alt_pipe.to(__lowerCAmelCase)
					alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

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

					assert image.shape == (1, 64, 64, 3)
					lowerCAmelCase     =    np.array(
					    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237])

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







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


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""			,       safety_checker=__lowerCAmelCase)
					lowerCAmelCase     =    alt_pipe.to(__lowerCAmelCase)
					alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

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

					lowerCAmelCase     =    output.images

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

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase     =    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 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""			,       subfolder="""scheduler""")

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

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

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

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

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase     =    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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




