



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a     =    logging.get_logger(__name__)

__a     =    {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      str						=					"""reformer"""
					_A		:      Dict						=					["""past_buckets_states"""]
					_A		:      Any						=					{}






					def __init__(	self:							str						,					snake_case:							Dict=64						,					snake_case:							Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,					snake_case:							str=1.0						,					snake_case:							Optional[Any]=True						,					snake_case:							Any=[64, 64]						,					snake_case:							Any=[64, 192]						,					snake_case:							Any=0						,					snake_case:							Tuple=2						,					snake_case:							Union[str, Any]=512						,					snake_case:							Union[str, Any]=None						,					snake_case:							str="relu"						,					snake_case:							Dict=0.0_5						,					snake_case:							Union[str, Any]=256						,					snake_case:							Optional[Any]=0.0_2						,					snake_case:							Dict=False						,					snake_case:							Optional[int]=1E-12						,					snake_case:							Union[str, Any]=1						,					snake_case:							Optional[int]=0						,					snake_case:							List[Any]=0.0_5						,					snake_case:							int=64						,					snake_case:							Tuple=64						,					snake_case:							List[str]=0.0						,					snake_case:							Dict=1						,					snake_case:							Tuple=0						,					snake_case:							str=4_096						,					snake_case:							Optional[Any]=12						,					snake_case:							Any=None						,					snake_case:							Optional[int]=1						,					snake_case:							Union[str, Any]=0						,					snake_case:							int=320						,					snake_case:							Dict=False						,					snake_case:							Tuple=True						,					snake_case:							Optional[Any]=None						,					**snake_case:							Any						,					)       ->     List[str]:
											snake_case_			:Union[str, Any]						=      hash_seed
											snake_case_			:List[str]						=      vocab_size
											snake_case_			:Union[str, Any]						=      attention_head_size
											snake_case_			:Any						=      hidden_size
											snake_case_			:List[Any]						=      num_attention_heads
											snake_case_			:List[Any]						=      num_hashes
											snake_case_			:List[Any]						=      len(snake_case						)
											snake_case_			:Union[str, Any]						=      tuple(snake_case						) if isinstance(snake_case						,					snake_case						) else num_buckets
											snake_case_			:Optional[int]						=      lsh_attn_chunk_length
											snake_case_			:Optional[int]						=      local_attn_chunk_length
											snake_case_			:Tuple						=      lsh_num_chunks_after
											snake_case_			:Dict						=      lsh_num_chunks_before
											snake_case_			:Any						=      local_num_chunks_after
											snake_case_			:str						=      local_num_chunks_before
											snake_case_			:int						=      hidden_act
											snake_case_			:int						=      feed_forward_size
											snake_case_			:Union[str, Any]						=      hidden_dropout_prob
											snake_case_			:str						=      lsh_attention_probs_dropout_prob
											snake_case_			:Union[str, Any]						=      local_attention_probs_dropout_prob
											snake_case_			:Any						=      max_position_embeddings
											snake_case_			:Any						=      initializer_range
											snake_case_			:Optional[int]						=      layer_norm_eps
											snake_case_			:str						=      axial_pos_embds
											snake_case_			:str						=      tuple(snake_case						)
											snake_case_			:str						=      tuple(snake_case						)
											snake_case_			:List[Any]						=      axial_norm_std
											snake_case_			:Optional[int]						=      chunk_size_lm_head
											snake_case_			:List[Any]						=      attn_layers
											snake_case_			:Any						=      use_cache
											snake_case_			:Tuple						=      classifier_dropout
											super().__init__(
											    pad_token_id=snake_case						,					eos_token_id=snake_case						,					is_decoder=snake_case						,					tie_word_embeddings=snake_case						,					**snake_case						,					)





"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					RePaintPipeline
					_A		:      List[Any]						=					IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_A		:      Union[str, Any]						=					PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_A		:      Tuple						=					IMAGE_INPAINTING_BATCH_PARAMS
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[str]:
											torch.manual_seed(0						)
											torch.manual_seed(0						)
											snake_case_			:Union[str, Any]						=      UNetaDModel(
											    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,					)
											snake_case_			:str						=      RePaintScheduler()
											snake_case_			:Any						=      {"""unet""": unet, """scheduler""": scheduler}
											return components






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Dict						,					snake_case:							List[Any]=0						)       ->     Tuple:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_			:int						=      torch.manual_seed(snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_			:Dict						=      np.random.RandomState(snake_case						).standard_normal((1, 3, 32, 32)						)
											snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Dict						=      (image > 0).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Union[str, Any]						=      {
											    """image""": image,
											    """mask_image""": mask,
											    """generator""": generator,
											    """num_inference_steps""": 5,
											    """eta""": 0.0,
											    """jump_length""": 2,
											    """jump_n_sample""": 2,
											    """output_type""": """numpy""",
											}
											return inputs






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											snake_case_			:int						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_			:List[str]						=      self.get_dummy_components()
											snake_case_			:Union[str, Any]						=      RePaintPipeline(**snake_case						)
											snake_case_			:Tuple						=      sd_pipe.to(snake_case						)
											sd_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_			:Tuple						=      self.get_dummy_inputs(snake_case						)
											snake_case_			:Dict						=      sd_pipe(**snake_case						).images
											snake_case_			:List[Any]						=      image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_			:Optional[int]						=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






					@skip_mps
					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""						)
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[Any]:
											return super().test_inference_batch_single_identical()






					@skip_mps
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[Any]:
											return super().test_save_load_optional_components()






					@skip_mps
					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											return super().test_attention_slicing_forward_pass()

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






					'''simple docstring'''





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






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											snake_case_			:Any						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""						)
											snake_case_			:Union[str, Any]						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
											snake_case_			:str						=      load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""						)

											snake_case_			:List[str]						=      """google/ddpm-ema-celebahq-256"""
											snake_case_			:Optional[int]						=      UNetaDModel.from_pretrained(snake_case						)
											snake_case_			:List[Any]						=      RePaintScheduler.from_pretrained(snake_case						)

											snake_case_			:str						=      RePaintPipeline(unet=snake_case						,					scheduler=snake_case						).to(snake_case						)
											repaint.set_progress_bar_config(disable=snake_case						)
											repaint.enable_attention_slicing()

											snake_case_			:int						=      torch.manual_seed(0						)
											snake_case_			:str						=      repaint(
											    snake_case						,					snake_case						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=snake_case						,					output_type="""np"""						,					)
											snake_case_			:List[str]						=      output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).mean() < 1E-2
