


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@property
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								torch.manual_seed(0)
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						int		=      "cpu"
								snake_case__  :						Tuple		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler()

								snake_case__  :						List[Any]		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Optional[int]		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Any		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy"	,					return_dict=lowerCamelCase__)[0]

								snake_case__  :						Optional[Any]		=      image[0, -3:, -3:, -1]
								snake_case__  :						int		=      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, Any]		=      np.array(
								    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04])

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







							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler(prediction_type="sample")

								snake_case__  :						Any		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      torch.manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.manual_seed(0)
								snake_case__  :						str		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy")[0]

								snake_case__  :						Any		=      image[0, -3:, -3:, -1]
								snake_case__  :						Dict		=      image_eps[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						str		=      1E-2 if torch_device != "mps" else 3E-2
								assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance


@slow
@require_torch_gpu
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Any		=      "google/ddpm-cifar10-32"

								snake_case__  :						Any		=      UNetaDModel.from_pretrained(lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      DDPMScheduler.from_pretrained(lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Tuple		=      torch.manual_seed(0)
								snake_case__  :						Union[str, Any]		=      ddpm(generator=lowerCamelCase__	,					output_type="numpy").images

								snake_case__  :						int		=      image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, Any]		=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85])
								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   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

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

												__lowerCAmelCase       =				use_cache

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




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






