



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						@property
						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				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    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				self.dummy_uncond_unet
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				PNDMScheduler()

											SCREAMING_SNAKE_CASE							:     str						=				PNDMPipeline(unet=snake_case       ,	scheduler=snake_case	)
											pndm.to(snake_case	)
											pndm.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Dict						=				pndm(generator=snake_case       ,	num_inference_steps=20       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				pndm(generator=snake_case       ,	num_inference_steps=20       ,	output_type='numpy'       ,	return_dict=snake_case	)[0]

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

											assert image.shape == (1, 32, 32, 3)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				np.array([1.0, 1.0, 0.0, 1.0, 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):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				'google/ddpm-cifar10-32'

											SCREAMING_SNAKE_CASE							:     List[str]						=				UNetaDModel.from_pretrained(snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				PNDMScheduler()

											SCREAMING_SNAKE_CASE							:     Tuple						=				PNDMPipeline(unet=snake_case       ,	scheduler=snake_case	)
											pndm.to(snake_case	)
											pndm.set_progress_bar_config(disable=snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pndm(generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]	)

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




from typing import Callable, Optional, Union

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


_lowerCamelCase    :		int		      =    logging.get_logger(__name__)

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


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Tuple		=		'xlm-prophetnet'
						UpperCAmelCase			:		int		=		['past_key_values']
						UpperCAmelCase			:		List[Any]		=		{
						    'num_attention_heads': 'num_encoder_attention_heads',
						}







						def __init__(  self    :				List[Any]       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[Union[str, Callable]] = "gelu"       ,	snake_case    :				Optional[int] = 30522       ,	snake_case    :				Optional[int] = 1024       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[int] = 512       ,	snake_case    :				Optional[float] = 0.02       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 2       ,	snake_case    :				Optional[int] = 32       ,	snake_case    :				Optional[int] = 128       ,	snake_case    :				Optional[bool] = False       ,	snake_case    :				Optional[float] = 0.0       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 1       ,	snake_case    :				Optional[int] = 2       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_encoder_layers
											SCREAMING_SNAKE_CASE							:     str						=				num_encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				decoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Any						=				num_decoder_layers
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_decoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				init_std  # Normal(0, this parameter)
											SCREAMING_SNAKE_CASE							:     List[str]						=				activation_function

											# parameters for xlmprophetnet
											SCREAMING_SNAKE_CASE							:     Tuple						=				ngram
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				num_buckets
											SCREAMING_SNAKE_CASE							:     Any						=				relative_max_distance
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				disable_ngram_loss
											SCREAMING_SNAKE_CASE							:     List[str]						=				eps

											# 3 Types of Dropout
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     Any						=				activation_dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				dropout

											SCREAMING_SNAKE_CASE							:     Tuple						=				use_cache

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







						@property
						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.num_encoder_layers + self.num_decoder_layers







						@num_hidden_layers.setter
						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											raise NotImplementedError(
											    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
											    ' `num_decoder_layers`.'	)