



"""simple docstring"""


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


snake_case_				     =       logging.get_logger(__name__)

snake_case_				     =       {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							"""openai-gpt"""
						__UpperCamelCase						=							{
						    """max_position_embeddings""": """n_positions""",
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(					self :Optional[int]   ,  lowercase_ :Optional[int]=4_04_78   ,  lowercase_ :List[Any]=5_12   ,  lowercase_ :List[str]=7_68   ,  lowercase_ :int=12   ,  lowercase_ :Dict=12   ,  lowercase_ :Union[str, Any]="gelu"   ,  lowercase_ :Union[str, Any]=0.1   ,  lowercase_ :str=0.1   ,  lowercase_ :List[str]=0.1   ,  lowercase_ :Optional[Any]=1E-5   ,  lowercase_ :Optional[int]=0.02   ,  lowercase_ :Optional[Any]="cls_index"   ,  lowercase_ :List[str]=True   ,  lowercase_ :List[str]=None   ,  lowercase_ :str=True   ,  lowercase_ :int=0.1   ,  **lowercase_ :List[Any]   ,  )				->			str:
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = n_positions
												UpperCAmelCase             = n_embd
												UpperCAmelCase             = n_layer
												UpperCAmelCase             = n_head
												UpperCAmelCase             = afn
												UpperCAmelCase             = resid_pdrop
												UpperCAmelCase             = embd_pdrop
												UpperCAmelCase             = attn_pdrop
												UpperCAmelCase             = layer_norm_epsilon
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = summary_type
												UpperCAmelCase             = summary_use_proj
												UpperCAmelCase             = summary_activation
												UpperCAmelCase             = summary_first_dropout
												UpperCAmelCase             = summary_proj_to_labels
												super().__init__(**lowercase_					)






'''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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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 a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

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

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

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

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

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						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 a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

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

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

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						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






