



"""simple docstring"""





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


lowerCamelCase  			=		logging.get_logger(__name__)

lowerCamelCase  			=		{
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	'''pegasus'''
       UpperCamelCase		=	['''past_key_values''']
       UpperCamelCase		=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



       def __init__(				self     :    int						,							_UpperCAmelCase     :    Dict=50265						,							_UpperCAmelCase     :    Dict=1024						,							_UpperCAmelCase     :    str=12						,							_UpperCAmelCase     :    Union[str, Any]=4096						,							_UpperCAmelCase     :    Dict=16						,							_UpperCAmelCase     :    Dict=12						,							_UpperCAmelCase     :    Optional[int]=4096						,							_UpperCAmelCase     :    Union[str, Any]=16						,							_UpperCAmelCase     :    Optional[Any]=0.0						,							_UpperCAmelCase     :    str=0.0						,							_UpperCAmelCase     :    Union[str, Any]=True						,							_UpperCAmelCase     :    int=True						,							_UpperCAmelCase     :    Dict="gelu"						,							_UpperCAmelCase     :    Optional[Any]=1024						,							_UpperCAmelCase     :    List[str]=0.1						,							_UpperCAmelCase     :    Union[str, Any]=0.0						,							_UpperCAmelCase     :    Union[str, Any]=0.0						,							_UpperCAmelCase     :    str=0.02						,							_UpperCAmelCase     :    Any=0						,							_UpperCAmelCase     :    Any=False						,							_UpperCAmelCase     :    Any=0						,							_UpperCAmelCase     :    Any=1						,							_UpperCAmelCase     :    str=1						,							**_UpperCAmelCase     :    Union[str, Any]						,							)					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				vocab_size
             UpperCAmelCase_     					=				max_position_embeddings
             UpperCAmelCase_     					=				d_model
             UpperCAmelCase_     					=				encoder_ffn_dim
             UpperCAmelCase_     					=				encoder_layers
             UpperCAmelCase_     					=				encoder_attention_heads
             UpperCAmelCase_     					=				decoder_ffn_dim
             UpperCAmelCase_     					=				decoder_layers
             UpperCAmelCase_     					=				decoder_attention_heads
             UpperCAmelCase_     					=				dropout
             UpperCAmelCase_     					=				attention_dropout
             UpperCAmelCase_     					=				activation_dropout
             UpperCAmelCase_     					=				activation_function
             UpperCAmelCase_     					=				init_std
             UpperCAmelCase_     					=				encoder_layerdrop
             UpperCAmelCase_     					=				decoder_layerdrop
             UpperCAmelCase_     					=				use_cache
             UpperCAmelCase_     					=				encoder_layers
             UpperCAmelCase_     					=				scale_embedding  # scale factor will be sqrt(d_model) if True
             super().__init__(
                 pad_token_id=__lowercase						,							eos_token_id=__lowercase						,							is_encoder_decoder=__lowercase						,							decoder_start_token_id=__lowercase						,							forced_eos_token_id=__lowercase						,							**__lowercase						,							)



       @property
       def 	lowercase__							(				self     :    Dict       )					->		Any:


             '''simple docstring'''




             return self.encoder_attention_heads







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


             '''simple docstring'''




             return self.d_model







'''simple docstring'''

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 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=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      :			int   ):







            """simple docstring"""







            __lowercase      		=self.dummy_uncond_unet
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'   ).images

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'    ,   return_dict=__lowercase   )[0]

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

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=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 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		='google/ddpm-cifar10-32'

            __lowercase      		=UNetaDModel.from_pretrained(__lowercase   )
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   output_type='numpy'   ).images

            __lowercase      		=image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

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


