'''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    lowerCamelCase__  (   unittest.TestCase     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """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     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ="""cpu"""
        _UpperCamelCase  :Optional[Any]       =self.dummy_uncond_unet
        _UpperCamelCase  :Optional[Any]       =DDPMScheduler()

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

        _UpperCamelCase  :str       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :str       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :Dict       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""					,      return_dict=lowerCAmelCase__     )[0]

        _UpperCamelCase  :List[str]       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Dict       =np.array(
            [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.000, 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     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.dummy_uncond_unet
        _UpperCamelCase  :str       =DDPMScheduler(prediction_type="""sample"""     )

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

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :int       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     )[0]

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :List[str]       =image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Optional[Any]       =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    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       ="""google/ddpm-cifar10-32"""

        _UpperCamelCase  :Optional[int]       =UNetaDModel.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =DDPMScheduler.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =torch.manual_seed(0     )
        _UpperCamelCase  :Optional[int]       =ddpm(generator=lowerCAmelCase__					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Any       =np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]     )
        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''mega'''

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=30522       , _SCREAMING_SNAKE_CASE:			Optional[Any]=128       , _SCREAMING_SNAKE_CASE:			Any=4       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			List[str]=16       , _SCREAMING_SNAKE_CASE:			Dict=True       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=False       , _SCREAMING_SNAKE_CASE:			List[Any]=-1       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , _SCREAMING_SNAKE_CASE:			Optional[int]="scalenorm"       , _SCREAMING_SNAKE_CASE:			int=True       , _SCREAMING_SNAKE_CASE:			List[str]="silu"       , _SCREAMING_SNAKE_CASE:			int="softmax"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2048       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			List[str]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=0.2       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=1.0       , _SCREAMING_SNAKE_CASE:			List[str]=1       , _SCREAMING_SNAKE_CASE:			List[Any]=0       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			Optional[int]="rotary"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]       , )							->			Optional[Any]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	activation
              UpperCamelCase_ 							=	attention_activation
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	ema_projection_size
              UpperCamelCase_ 							=	bidirectional
              UpperCamelCase_ 							=	shared_representation_size
              UpperCamelCase_ 							=	use_chunking
              UpperCamelCase_ 							=	chunk_size
              UpperCamelCase_ 							=	truncation
              UpperCamelCase_ 							=	normalize_before_mega
              UpperCamelCase_ 							=	normalization_type
              UpperCamelCase_ 							=	norm_affine
              UpperCamelCase_ 							=	dropout_prob
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	use_feature_dropout
              UpperCamelCase_ 							=	use_normalized_ffn
              UpperCamelCase_ 							=	nffn_hidden_size
              UpperCamelCase_ 							=	normalize_before_ffn
              UpperCamelCase_ 							=	nffn_activation_dropout_prob
              UpperCamelCase_ 							=	max_positions
              UpperCamelCase_ 							=	add_token_type_embeddings
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	ema_delta_alpha_range
              UpperCamelCase_ 							=	ema_beta_range
              UpperCamelCase_ 							=	ema_gamma_omega_range
              UpperCamelCase_ 							=	relative_positional_bias
              UpperCamelCase_ 							=	use_cache
              UpperCamelCase_ 							=	classifier_dropout
              UpperCamelCase_ 							=	add_lm_hidden_dense_layer
              UpperCamelCase_ 							=	1  # not used but required by Hugging Face



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @property
       def lowercase			(		self:			Optional[int]		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


              if self.task == "multiple-choice":
                     UpperCamelCase_ 							=	{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase_ 							=	{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]		)

