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 						lowercase    (     unittest.TestCase     ):




							@property
							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.dummy_uncond_unet
												SCREAMING_SNAKE_CASE    			=       KarrasVeScheduler()

												SCREAMING_SNAKE_CASE    			=       KarrasVePipeline(unet=_UpperCamelCase	,							scheduler=_UpperCamelCase      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=2	,							generator=_UpperCamelCase	,							output_type="numpy"      ).images

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=2	,							generator=_UpperCamelCase	,							output_type="numpy"	,							return_dict=_UpperCamelCase      )[0]

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

												assert image.shape == (1, 32, 32, 3)
												SCREAMING_SNAKE_CASE    			=       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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "google/ncsnpp-celebahq-256"
												SCREAMING_SNAKE_CASE    			=       UNetaDModel.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       KarrasVeScheduler()

												SCREAMING_SNAKE_CASE    			=       KarrasVePipeline(unet=_UpperCamelCase	,							scheduler=_UpperCamelCase      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=20	,							generator=_UpperCamelCase	,							output_type="numpy"      ).images

												SCREAMING_SNAKE_CASE    			=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 256, 256, 3)
												SCREAMING_SNAKE_CASE    			=       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



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowerCamelCase					:   Tuple							=		{
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""ernie_m"""
							lowercase__						:       Dict[str, str]								=							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int = 250_002	,							_UpperCamelCase   :					int = 768	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 3_072	,							_UpperCamelCase   :					str = "gelu"	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					int = 514	,							_UpperCamelCase   :					float = 0.0_2	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					float = 1e-05	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					int=0.0	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       classifier_dropout
												SCREAMING_SNAKE_CASE    			=       is_decoder
												SCREAMING_SNAKE_CASE    			=       act_dropout




