





"""simple docstring"""






import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowerCAmelCase_			=							logging.get_logger(__name__)

enable_full_determinism()



class 	lowerCAmelCase					(      __UpperCAmelCase			,	__UpperCAmelCase			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetaDModel
       lowerCAmelCase__						 =  "sample"






       @property
       def 							__A (   self     ):
              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     (32, 32)

              _UpperCAmelCase     =     floats_tensor((batch_size, num_channels) + sizes     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor([10]     ).to(_lowerCamelCase     )

              return {"sample": noise, "timestep": time_step}






       @property
       def 							__A (   self     ):
              return (3, 32, 32)






       @property
       def 							__A (   self     ):
              return (3, 32, 32)






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'block_out_channels': (32, 64),
                  'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
                  'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
                  'attention_head_dim': 3,
                  'out_channels': 3,
                  'in_channels': 3,
                  'layers_per_block': 2,
                  'sample_size': 32,
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict



class 	lowerCAmelCase					(      __UpperCAmelCase			,	__UpperCAmelCase			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetaDModel
       lowerCAmelCase__						 =  "sample"






       @property
       def 							__A (   self     ):
              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     (32, 32)

              _UpperCAmelCase     =     floats_tensor((batch_size, num_channels) + sizes     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor([10]     ).to(_lowerCamelCase     )

              return {"sample": noise, "timestep": time_step}






       @property
       def 							__A (   self     ):
              return (4, 32, 32)






       @property
       def 							__A (   self     ):
              return (4, 32, 32)






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'sample_size': 32,
                  'in_channels': 4,
                  'out_channels': 4,
                  'layers_per_block': 2,
                  'block_out_channels': (32, 64),
                  'attention_head_dim': 32,
                  'down_block_types': ('DownBlock2D', 'DownBlock2D'),
                  'up_block_types': ('UpBlock2D', 'UpBlock2D'),
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,  output_loading_info=_lowerCamelCase     )

              self.assertIsNotNone(_lowerCamelCase     )
              self.assertEqual(len(loading_info['missing_keys']     )    ,  0     )

              model.to(_lowerCamelCase     )
              _UpperCAmelCase     =     model(**self.dummy_input     ).sample

              assert image is not None, "Make sure output is not None"






       @unittest.skipIf(torch_device != 'cuda'    ,  'This test is supposed to run on GPU'     )
       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,  output_loading_info=_lowerCamelCase     )
              model.to(_lowerCamelCase     )
              _UpperCAmelCase     =     model(**self.dummy_input     ).sample

              assert image is not None, "Make sure output is not None"






       @unittest.skipIf(torch_device != 'cuda'    ,  'This test is supposed to run on GPU'     )
       def 							__A (   self     ):
              # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
              _UpperCAmelCase    ,							_UpperCAmelCase     =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,  output_loading_info=_lowerCamelCase     )
              model_accelerate.to(_lowerCamelCase     )
              model_accelerate.eval()

              _UpperCAmelCase     =     torch.randn(
                  1    ,  model_accelerate.config.in_channels    ,  model_accelerate.config.sample_size    ,  model_accelerate.config.sample_size    ,  generator=torch.manual_seed(0     )    ,  )
              _UpperCAmelCase     =     noise.to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor([10] * noise.shape[0]     ).to(_lowerCamelCase     )

              _UpperCAmelCase     =     model_accelerate(_lowerCamelCase    ,  _lowerCamelCase     )['sample']

              # two models don't need to stay in the device at the same time
              del model_accelerate
              torch.cuda.empty_cache()
              gc.collect()

              _UpperCAmelCase    ,							_UpperCAmelCase     =     UNetaDModel.from_pretrained(
                  'fusing/unet-ldm-dummy-update'    ,  output_loading_info=_lowerCamelCase    ,  low_cpu_mem_usage=_lowerCamelCase     )
              model_normal_load.to(_lowerCamelCase     )
              model_normal_load.eval()
              _UpperCAmelCase     =     model_normal_load(_lowerCamelCase    ,  _lowerCamelCase     )['sample']

              assert torch_all_close(_lowerCamelCase    ,  _lowerCamelCase    ,  rtol=1E-3     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'     )
              model.eval()
              model.to(_lowerCamelCase     )

              _UpperCAmelCase     =     torch.randn(
                  1    ,  model.config.in_channels    ,  model.config.sample_size    ,  model.config.sample_size    ,  generator=torch.manual_seed(0     )    ,  )
              _UpperCAmelCase     =     noise.to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor([10] * noise.shape[0]     ).to(_lowerCamelCase     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(_lowerCamelCase    ,  _lowerCamelCase     ).sample

              _UpperCAmelCase     =     output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]     )
              # fmt: on

              self.assertTrue(torch_all_close(_lowerCamelCase    ,  _lowerCamelCase    ,  rtol=1E-3     )     )



class 	lowerCAmelCase					(      __UpperCAmelCase			,	__UpperCAmelCase			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetaDModel
       lowerCAmelCase__						 =  "sample"






       @property
       def 							__A (   self    ,  a__=(32, 32)     ):
              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     3

              _UpperCAmelCase     =     floats_tensor((batch_size, num_channels) + sizes     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor(batch_size * [10]     ).to(dtype=torch.intaa    ,  device=_lowerCamelCase     )

              return {"sample": noise, "timestep": time_step}






       @property
       def 							__A (   self     ):
              return (3, 32, 32)






       @property
       def 							__A (   self     ):
              return (3, 32, 32)






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'block_out_channels': [32, 64, 64, 64],
                  'in_channels': 3,
                  'layers_per_block': 1,
                  'out_channels': 3,
                  'time_embedding_type': 'fourier',
                  'norm_eps': 1E-6,
                  'mid_block_scale_factor': math.sqrt(2.0     ),
                  'norm_num_groups': None,
                  'down_block_types': [
                      'SkipDownBlock2D',
                      'AttnSkipDownBlock2D',
                      'SkipDownBlock2D',
                      'SkipDownBlock2D',
                  ],
                  'up_block_types': [
                      'SkipUpBlock2D',
                      'SkipUpBlock2D',
                      'AttnSkipUpBlock2D',
                      'SkipUpBlock2D',
                  ],
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'    ,  output_loading_info=_lowerCamelCase     )
              self.assertIsNotNone(_lowerCamelCase     )
              self.assertEqual(len(loading_info['missing_keys']     )    ,  0     )

              model.to(_lowerCamelCase     )
              _UpperCAmelCase     =     self.dummy_input
              _UpperCAmelCase     =     floats_tensor((4, 3) + (2_56, 2_56)     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     noise
              _UpperCAmelCase     =     model(**_lowerCamelCase     )

              assert image is not None, "Make sure output is not None"






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'     )
              model.to(_lowerCamelCase     )

              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     (2_56, 2_56)

              _UpperCAmelCase     =     torch.ones((batch_size, num_channels) + sizes     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor(batch_size * [1E-4]     ).to(_lowerCamelCase     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(_lowerCamelCase    ,  _lowerCamelCase     ).sample

              _UpperCAmelCase     =     output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]     )
              # fmt: on

              self.assertTrue(torch_all_close(_lowerCamelCase    ,  _lowerCamelCase    ,  rtol=1E-2     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'     )
              model.to(_lowerCamelCase     )

              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     (32, 32)

              _UpperCAmelCase     =     torch.ones((batch_size, num_channels) + sizes     ).to(_lowerCamelCase     )
              _UpperCAmelCase     =     torch.tensor(batch_size * [1E-4]     ).to(_lowerCamelCase     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(_lowerCamelCase    ,  _lowerCamelCase     ).sample

              _UpperCAmelCase     =     output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]     )
              # fmt: on

              self.assertTrue(torch_all_close(_lowerCamelCase    ,  _lowerCamelCase    ,  rtol=1E-2     )     )






       def 							__A (   self     ):
              # not required for this model
              pass













"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """deberta-v2"""






       def __init__(   self    ,  a__=12_81_00    ,  a__=15_36    ,  a__=24    ,  a__=24    ,  a__=61_44    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=0    ,  a__=0.02    ,  a__=1E-7    ,  a__=False    ,  a__=-1    ,  a__=0    ,  a__=True    ,  a__=None    ,  a__=0    ,  a__="gelu"    ,  **a__    ,  ):
              super().__init__(**a__     )

              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     relative_attention
              _UpperCAmelCase     =     max_relative_positions
              _UpperCAmelCase     =     pad_token_id
              _UpperCAmelCase     =     position_biased_input

              # Backwards compatibility
              if type(a__     ) == str:
                     _UpperCAmelCase     =     [x.strip() for x in pos_att_type.lower().split('|'     )]

              _UpperCAmelCase     =     pos_att_type
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     layer_norm_eps

              _UpperCAmelCase     =     kwargs.get('pooler_hidden_size'    ,  a__     )
              _UpperCAmelCase     =     pooler_dropout
              _UpperCAmelCase     =     pooler_hidden_act



class 	lowerCAmelCase					(      snake_case							):






       @property
       def 							__A (   self     ):
              if self.task == "multiple-choice":
                     _UpperCAmelCase     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
              else:
                     _UpperCAmelCase     =     {0: 'batch', 1: 'sequence'}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
              else:
                     return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )






       @property
       def 							__A (   self     ):
              return 12






       def 							__A (   self    ,  a__    ,  a__ = -1    ,  a__ = -1    ,  a__ = -1    ,  a__ = False    ,  a__ = None    ,  a__ = 3    ,  a__ = 40    ,  a__ = 40    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     super().generate_dummy_inputs(preprocessor=a__    ,  framework=a__     )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs






