"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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')    ,)
									return model







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.dummy_uncond_unet
									UpperCAmelCase__             =							KarrasVeScheduler()

									UpperCAmelCase__             =							KarrasVePipeline(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=2    ,generator=lowerCamelCase__    ,output_type='numpy'			).images

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=2    ,generator=lowerCamelCase__    ,output_type='numpy'    ,return_dict=lowerCamelCase__			)[0]

									UpperCAmelCase__             =							image[0, -3:, -3:, -1]
									UpperCAmelCase__             =							image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCAmelCase__             =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							'google/ncsnpp-celebahq-256'
									UpperCAmelCase__             =							UNetaDModel.from_pretrained(lowerCamelCase__			)
									UpperCAmelCase__             =							KarrasVeScheduler()

									UpperCAmelCase__             =							KarrasVePipeline(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=20    ,generator=lowerCamelCase__    ,output_type='numpy'			).images

									UpperCAmelCase__             =							image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									UpperCAmelCase__             =							np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]			)

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




"""simple docstring"""






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


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



