



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    UpperCAmelCase_  (   __a    ,    __a    ,    __a    ,    unittest.TestCase       ):

						'''simple docstring'''




						UpperCamelCase__			: str				  =  AltDiffusionPipeline
						UpperCamelCase__			: Any				  =  TEXT_TO_IMAGE_PARAMS
						UpperCamelCase__			: Optional[Any]				  =  TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase__			: Any				  =  TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase__			: str				  =  TEXT_TO_IMAGE_IMAGE_PARAMS






						def        _A	(    self		):







													'''simple docstring'''

													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_0_0_8_5      ,							beta_end=0.0_1_2      ,							beta_schedule='scaled_linear'      ,							clip_sample=__a      ,							set_alpha_to_one=__a      ,							)
													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=5_002      ,							)
													__SCREAMING_SNAKE_CASE									=					CLIPTextModel(__a		)

													__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        _A	(    self      ,							_A      ,							_A=0		):







													'''simple docstring'''

													if str(__a		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									=					torch.manual_seed(__a		)
													else:
																				__SCREAMING_SNAKE_CASE									=					torch.Generator(device=__a		).manual_seed(__a		)
													__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        _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'''

													__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=5_002      ,							)
													# TODO: remove after fixing the non-deterministic text encoder
													__SCREAMING_SNAKE_CASE									=					RobertaSeriesModelWithTransformation(__a		)
													__SCREAMING_SNAKE_CASE									=					text_encoder

													__SCREAMING_SNAKE_CASE									=					AltDiffusionPipeline(**__a		)
													__SCREAMING_SNAKE_CASE									=					alt_pipe.to(__a		)
													alt_pipe.set_progress_bar_config(disable=__a		)

													__SCREAMING_SNAKE_CASE									=					self.get_dummy_inputs(__a		)
													__SCREAMING_SNAKE_CASE									=					"""A photo of an astronaut"""
													__SCREAMING_SNAKE_CASE									=					alt_pipe(**__a		)
													__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(
													    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]		)

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






						def        _A	(    self		):







													'''simple docstring'''

													__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=__a		)
													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=5_002      ,							)
													# TODO: remove after fixing the non-deterministic text encoder
													__SCREAMING_SNAKE_CASE									=					RobertaSeriesModelWithTransformation(__a		)
													__SCREAMING_SNAKE_CASE									=					text_encoder
													__SCREAMING_SNAKE_CASE									=					AltDiffusionPipeline(**__a		)
													__SCREAMING_SNAKE_CASE									=					alt_pipe.to(__a		)
													alt_pipe.set_progress_bar_config(disable=__a		)

													__SCREAMING_SNAKE_CASE									=					self.get_dummy_inputs(__a		)
													__SCREAMING_SNAKE_CASE									=					alt_pipe(**__a		)
													__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(
													    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]		)

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







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

						'''simple docstring'''




						def        _A	(    self		):







													'''simple docstring'''

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






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'      ,							safety_checker=__a		)
													__SCREAMING_SNAKE_CASE									=					alt_pipe.to(__a		)
													alt_pipe.set_progress_bar_config(disable=__a		)

													__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=__a      ,							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([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]		)

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






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					DDIMScheduler.from_pretrained('BAAI/AltDiffusion'      ,							subfolder='scheduler'		)

													__SCREAMING_SNAKE_CASE									=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'      ,							scheduler=__a      ,							safety_checker=__a		)
													__SCREAMING_SNAKE_CASE									=					alt_pipe.to(__a		)
													alt_pipe.set_progress_bar_config(disable=__a		)

													__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=__a      ,							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([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]		)

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














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride