
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()





class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      1
          SCREAMING_SNAKE_CASE_	=      3
          SCREAMING_SNAKE_CASE_	=      (32, 32)

          SCREAMING_SNAKE_CASE_	=      floats_tensor((batch_size, num_channels) + sizes ,     rng=random.Random(0	)	).to(_A	)
          return image



       @property
       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      UNetaDModel(
              block_out_channels=(32, 64) ,     layers_per_block=2 ,     sample_size=32 ,     in_channels=6 ,     out_channels=3 ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,     )
          return model



       @property
       def     _UpperCamelCase	(							self	)				->				List[str]:
          torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      VQModel(
              block_out_channels=[32, 64] ,     in_channels=3 ,     out_channels=3 ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,     latent_channels=3 ,     )
          return model



       def     _UpperCamelCase	(							self	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      '''cpu'''
          SCREAMING_SNAKE_CASE_	=      self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_	=      DDIMScheduler()
          SCREAMING_SNAKE_CASE_	=      self.dummy_vq_model

          SCREAMING_SNAKE_CASE_	=      LDMSuperResolutionPipeline(unet=_A ,     vqvae=_A ,     scheduler=_A	)
          ldm.to(_A	)
          ldm.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.dummy_image.to(_A	)

          SCREAMING_SNAKE_CASE_	=      torch.Generator(device=_A	).manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      ldm(image=_A ,     generator=_A ,     num_inference_steps=2 ,     output_type='''numpy'''	).images

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

          assert image.shape == (1, 64, 64, 3)
          SCREAMING_SNAKE_CASE_	=      np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




       @unittest.skipIf(torch_device != '''cuda''' ,     '''This test requires a GPU'''	)
       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_	=      DDIMScheduler()
          SCREAMING_SNAKE_CASE_	=      self.dummy_vq_model

          # put models in fp16
          SCREAMING_SNAKE_CASE_	=      unet.half()
          SCREAMING_SNAKE_CASE_	=      vqvae.half()

          SCREAMING_SNAKE_CASE_	=      LDMSuperResolutionPipeline(unet=_A ,     vqvae=_A ,     scheduler=_A	)
          ldm.to(_A	)
          ldm.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.dummy_image.to(_A	)

          SCREAMING_SNAKE_CASE_	=      ldm(_A ,     num_inference_steps=2 ,     output_type='''numpy'''	).images

          assert image.shape == (1, 64, 64, 3)







@slow
@require_torch
class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def     _UpperCamelCase	(							self	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/vq_diffusion/teddy_bear_pool.png'''	)
          SCREAMING_SNAKE_CASE_	=      init_image.resize((64, 64) ,     resample=PIL_INTERPOLATION['''lanczos''']	)

          SCREAMING_SNAKE_CASE_	=      LDMSuperResolutionPipeline.from_pretrained('''duongna/ldm-super-resolution''' ,     device_map='''auto'''	)
          ldm.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      ldm(image=_A ,     generator=_A ,     num_inference_steps=20 ,     output_type='''numpy'''	).images

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

          assert image.shape == (1, 256, 256, 3)
          SCREAMING_SNAKE_CASE_	=      np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




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


__UpperCAmelCase   						=     logging.get_logger(__name__)

__UpperCAmelCase   						=     {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="deberta"



       def __init__(							self ,     _A=50265 ,     _A=768 ,     _A=12 ,     _A=12 ,     _A=3072 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=0 ,     _A=0.02 ,     _A=1E-7 ,     _A=False ,     _A=-1 ,     _A=0 ,     _A=True ,     _A=None ,     _A=0 ,     _A="gelu" ,     **_A ,     )				->				Optional[int]:
          super().__init__(**_A	)

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

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

          SCREAMING_SNAKE_CASE_	=      pos_att_type
          SCREAMING_SNAKE_CASE_	=      vocab_size
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps

          SCREAMING_SNAKE_CASE_	=      kwargs.get('''pooler_hidden_size''' ,     _A	)
          SCREAMING_SNAKE_CASE_	=      pooler_dropout
          SCREAMING_SNAKE_CASE_	=      pooler_hidden_act







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             SCREAMING_SNAKE_CASE_	=      {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     _UpperCamelCase	(							self	)				->				int:
          return 12




       def     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     _A = 3 ,     _A = 40 ,     _A = 40 ,     _A = None ,     )				->				Mapping[str, Any]:
          SCREAMING_SNAKE_CASE_	=      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


