
"""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 A_      (unittest.TestCase ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )
     UpperCAmelCase_   :   str     	=       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    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       "cpu"
     UpperCAmelCase_   :   Dict     	=       self.dummy_uncond_unet
     UpperCAmelCase_   :   int     	=       DDPMScheduler()

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

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

     UpperCAmelCase_   :   List[Any]     	=       torch.Generator(device=__UpperCamelCase    ).manual_seed(0    )
     UpperCAmelCase_   :   Tuple     	=       ddpm(generator=__UpperCamelCase							,  num_inference_steps=2							,  output_type="numpy"							,  return_dict=__UpperCamelCase    )[0]

     UpperCAmelCase_   :   Union[str, Any]     	=       image[0, -3:, -3:, -1]
     UpperCAmelCase_   :   Optional[int]     	=       image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     UpperCAmelCase_   :   str     	=       np.array(
         [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.0_00, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]    )

     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    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.dummy_uncond_unet
     UpperCAmelCase_   :   Any     	=       DDPMScheduler(prediction_type="sample"    )

     UpperCAmelCase_   :   List[str]     	=       DDPMPipeline(unet=__UpperCamelCase							,  scheduler=__UpperCamelCase    )
     ddpm.to(__UpperCamelCase    )
     ddpm.set_progress_bar_config(disable=__UpperCamelCase    )

     UpperCAmelCase_   :   Dict     	=       torch.manual_seed(0    )
     UpperCAmelCase_   :   Any     	=       ddpm(generator=__UpperCamelCase							,  num_inference_steps=2							,  output_type="numpy"    ).images

     UpperCAmelCase_   :   int     	=       torch.manual_seed(0    )
     UpperCAmelCase_   :   List[Any]     	=       ddpm(generator=__UpperCamelCase							,  num_inference_steps=2							,  output_type="numpy"    )[0]

     UpperCAmelCase_   :   Optional[int]     	=       image[0, -3:, -3:, -1]
     UpperCAmelCase_   :   Optional[Any]     	=       image_eps[0, -3:, -3:, -1]

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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       "google/ddpm-cifar10-32"

     UpperCAmelCase_   :   str     	=       UNetaDModel.from_pretrained(__UpperCamelCase    )
     UpperCAmelCase_   :   List[Any]     	=       DDPMScheduler.from_pretrained(__UpperCamelCase    )

     UpperCAmelCase_   :   Tuple     	=       DDPMPipeline(unet=__UpperCamelCase							,  scheduler=__UpperCamelCase    )
     ddpm.to(__UpperCamelCase    )
     ddpm.set_progress_bar_config(disable=__UpperCamelCase    )

     UpperCAmelCase_   :   Optional[int]     	=       torch.manual_seed(0    )
     UpperCAmelCase_   :   Any     	=       ddpm(generator=__UpperCamelCase							,  output_type="numpy"    ).images

     UpperCAmelCase_   :   Union[str, Any]     	=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     UpperCAmelCase_   :   List[str]     	=       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Dict    =     """opt"""
 A__    :					int    =     ["""past_key_values"""]

 def __init__(   self  ,						__UpperCamelCase=5_0_2_7_2  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=True  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        UpperCamelCase_											=							ffn_dim
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							activation_function
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							use_cache
        UpperCamelCase_											=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        UpperCamelCase_											=							enable_bias
        UpperCamelCase_											=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        UpperCamelCase_											=							_remove_final_layer_norm



