
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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @property
  def 							_lowercase							( self			: List[str]    )			->	Tuple:







    """simple docstring"""






    torch.manual_seed(0    )
    __magic_name__				     =       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 							_lowercase							( self			: Any    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       self.dummy_uncond_unet
    __magic_name__				     =       PNDMScheduler()

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

    __magic_name__				     =       torch.manual_seed(0    )
    __magic_name__				     =       pndm(generator=UpperCamelCase__			,							num_inference_steps=20			,							output_type="""numpy"""    ).images

    __magic_name__				     =       torch.manual_seed(0    )
    __magic_name__				     =       pndm(generator=UpperCamelCase__			,							num_inference_steps=20			,							output_type="""numpy"""			,							return_dict=UpperCamelCase__    )[0]

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

    assert image.shape == (1, 32, 32, 3)
    __magic_name__				     =       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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Any    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """google/ddpm-cifar10-32"""

    __magic_name__				     =       UNetaDModel.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       PNDMScheduler()

    __magic_name__				     =       PNDMPipeline(unet=UpperCamelCase__			,							scheduler=UpperCamelCase__    )
    pndm.to(UpperCamelCase__    )
    pndm.set_progress_bar_config(disable=UpperCamelCase__    )
    __magic_name__				     =       torch.manual_seed(0    )
    __magic_name__				     =       pndm(generator=UpperCamelCase__			,							output_type="""numpy"""    ).images

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

    assert image.shape == (1, 32, 32, 3)
    __magic_name__				     =       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    '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        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

