

'''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 					lowercase						(  unittest.TestCase ):


							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								torch.manual_seed(0)
								UpperCAmelCase_		:      Dict       	=					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)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.dummy_uncond_unet
								UpperCAmelCase_		:      Any       	=					KarrasVeScheduler()

								UpperCAmelCase_		:      int       	=					KarrasVePipeline(unet=__A    ,			scheduler=__A)
								pipe.to(__A)
								pipe.set_progress_bar_config(disable=__A)

								UpperCAmelCase_		:      List[Any]       	=					torch.manual_seed(0)
								UpperCAmelCase_		:      str       	=					pipe(num_inference_steps=2    ,			generator=__A    ,			output_type='numpy').images

								UpperCAmelCase_		:      Union[str, Any]       	=					torch.manual_seed(0)
								UpperCAmelCase_		:      int       	=					pipe(num_inference_steps=2    ,			generator=__A    ,			output_type='numpy'    ,			return_dict=__A)[0]

								UpperCAmelCase_		:      Dict       	=					image[0, -3:, -3:, -1]
								UpperCAmelCase_		:      str       	=					image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_		:      Tuple       	=					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)							->				List[str]:
								UpperCAmelCase_		:      int       	=					"google/ncsnpp-celebahq-256"
								UpperCAmelCase_		:      List[str]       	=					UNetaDModel.from_pretrained(__A)
								UpperCAmelCase_		:      int       	=					KarrasVeScheduler()

								UpperCAmelCase_		:      Any       	=					KarrasVePipeline(unet=__A    ,			scheduler=__A)
								pipe.to(__A)
								pipe.set_progress_bar_config(disable=__A)

								UpperCAmelCase_		:      int       	=					torch.manual_seed(0)
								UpperCAmelCase_		:      Tuple       	=					pipe(num_inference_steps=20    ,			generator=__A    ,			output_type='numpy').images

								UpperCAmelCase_		:      Optional[int]       	=					image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								UpperCAmelCase_		:      Any       	=					np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586])

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









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
									UpperCAmelCase_		:      Any       	=					{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
									UpperCAmelCase_		:      Optional[int]       	=					{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ])






