

"""simple docstring"""




from typing import Callable, Optional, Union

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


A_							:			Any											= logging.get_logger(__name__)

A_							:			List[Any]											= {
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		str							=							'xlm-prophetnet'
			lowerCamelCase__   :		Optional[int]							=							['past_key_values']
			lowerCamelCase__   :		List[str]							=							{
			    'num_attention_heads': 'num_encoder_attention_heads',
			}

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[float] = 0.1    ,				__UpperCAmelCase				:						Optional[Union[str, Callable]] = "gelu"    ,				__UpperCAmelCase				:						Optional[int] = 3_0_5_2_2    ,				__UpperCAmelCase				:						Optional[int] = 1_0_2_4    ,				__UpperCAmelCase				:						Optional[int] = 4_0_9_6    ,				__UpperCAmelCase				:						Optional[int] = 1_2    ,				__UpperCAmelCase				:						Optional[int] = 1_6    ,				__UpperCAmelCase				:						Optional[int] = 4_0_9_6    ,				__UpperCAmelCase				:						Optional[int] = 1_2    ,				__UpperCAmelCase				:						Optional[int] = 1_6    ,				__UpperCAmelCase				:						Optional[float] = 0.1    ,				__UpperCAmelCase				:						Optional[float] = 0.1    ,				__UpperCAmelCase				:						Optional[int] = 5_1_2    ,				__UpperCAmelCase				:						Optional[float] = 0.02    ,				__UpperCAmelCase				:						Optional[bool] = True    ,				__UpperCAmelCase				:						Optional[bool] = True    ,				__UpperCAmelCase				:						Optional[int] = 0    ,				__UpperCAmelCase				:						Optional[int] = 2    ,				__UpperCAmelCase				:						Optional[int] = 3_2    ,				__UpperCAmelCase				:						Optional[int] = 1_2_8    ,				__UpperCAmelCase				:						Optional[bool] = False    ,				__UpperCAmelCase				:						Optional[float] = 0.0    ,				__UpperCAmelCase				:						Optional[bool] = True    ,				__UpperCAmelCase				:						Optional[int] = 0    ,				__UpperCAmelCase				:						Optional[int] = 1    ,				__UpperCAmelCase				:						Optional[int] = 2    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      encoder_ffn_dim
						SCREAMING_SNAKE_CASE__								=      num_encoder_layers
						SCREAMING_SNAKE_CASE__								=      num_encoder_attention_heads
						SCREAMING_SNAKE_CASE__								=      decoder_ffn_dim
						SCREAMING_SNAKE_CASE__								=      num_decoder_layers
						SCREAMING_SNAKE_CASE__								=      num_decoder_attention_heads
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      init_std  # Normal(0, this parameter)
						SCREAMING_SNAKE_CASE__								=      activation_function

						# parameters for xlmprophetnet
						SCREAMING_SNAKE_CASE__								=      ngram
						SCREAMING_SNAKE_CASE__								=      num_buckets
						SCREAMING_SNAKE_CASE__								=      relative_max_distance
						SCREAMING_SNAKE_CASE__								=      disable_ngram_loss
						SCREAMING_SNAKE_CASE__								=      eps

						# 3 Types of Dropout
						SCREAMING_SNAKE_CASE__								=      attention_dropout
						SCREAMING_SNAKE_CASE__								=      activation_dropout
						SCREAMING_SNAKE_CASE__								=      dropout

						SCREAMING_SNAKE_CASE__								=      use_cache

						super().__init__(
						    pad_token_id=__UpperCAmelCase    ,				bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase    ,				is_encoder_decoder=__UpperCAmelCase    ,				add_cross_attention=__UpperCAmelCase    ,				decoder_start_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						return self.num_encoder_layers + self.num_decoder_layers





			@num_hidden_layers.setter
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Tuple:
						raise NotImplementedError(
						    """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
						    """ `num_decoder_layers`."""       )





"""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    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						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, 3_2, 3_2, 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    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						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=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      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


