"""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


UpperCamelCase_    :    str											=						logging.get_logger(__name__)

UpperCamelCase_    :    Union[str, Any]											=						{
    """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      _lowercase						(				_a					):
				lowerCamelCase__									=    'deberta'

				def __init__(       self    :      Optional[int]    ,      _lowercase    :      Union[str, Any]=5_0_2_6_5    ,      _lowercase    :      Dict=7_6_8    ,      _lowercase    :      Any=1_2    ,      _lowercase    :      Tuple=1_2    ,      _lowercase    :      str=3_0_7_2    ,      _lowercase    :      List[str]="gelu"    ,      _lowercase    :      int=0.1    ,      _lowercase    :      List[str]=0.1    ,      _lowercase    :      List[str]=5_1_2    ,      _lowercase    :      int=0    ,      _lowercase    :      Optional[Any]=0.02    ,      _lowercase    :      Any=1e-7    ,      _lowercase    :      List[str]=False    ,      _lowercase    :      int=-1    ,      _lowercase    :      Dict=0    ,      _lowercase    :      List[Any]=True    ,      _lowercase    :      List[Any]=None    ,      _lowercase    :      List[str]=0    ,      _lowercase    :      Any="gelu"    ,      **_lowercase    :      List[Any]    ,      ):


										'''simple docstring'''
										super().__init__(**_lowercase       )

										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(_lowercase       ) == 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"""    ,      _lowercase       )
										UpperCAmelCase__   		=						pooler_dropout
										UpperCAmelCase__   		=						pooler_hidden_act


class      _lowercase						(				_a					):

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										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    :      str       ):


										'''simple docstring'''
										return 1_2

				def     A	(       self    :      List[Any]    ,      _lowercase    :      Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,      _lowercase    :      int = -1    ,      _lowercase    :      int = -1    ,      _lowercase    :      int = -1    ,      _lowercase    :      bool = False    ,      _lowercase    :      Optional["TensorType"] = None    ,      _lowercase    :      int = 3    ,      _lowercase    :      int = 4_0    ,      _lowercase    :      int = 4_0    ,      _lowercase    :      "PreTrainedTokenizerBase" = None    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						super().generate_dummy_inputs(preprocessor=_lowercase    ,      framework=_lowercase       )
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs


"""simple docstring"""







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      _lowercase						(				unittest.TestCase					):

				@property
				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						1
										UpperCAmelCase__   		=						3
										UpperCAmelCase__   		=						(3_2, 3_2)

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

				@property
				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										torch.manual_seed(0       )
										UpperCAmelCase__   		=						UNetaDModel(
										    block_out_channels=(3_2, 6_4)    ,      layers_per_block=2    ,      sample_size=3_2    ,      in_channels=6    ,      out_channels=3    ,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,      )
										return model

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										torch.manual_seed(0       )
										UpperCAmelCase__   		=						VQModel(
										    block_out_channels=[3_2, 6_4]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,      latent_channels=3    ,      )
										return model

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""cpu"""
										UpperCAmelCase__   		=						self.dummy_uncond_unet
										UpperCAmelCase__   		=						DDIMScheduler()
										UpperCAmelCase__   		=						self.dummy_vq_model

										UpperCAmelCase__   		=						LDMSuperResolutionPipeline(unet=_lowercase    ,      vqvae=_lowercase    ,      scheduler=_lowercase       )
										ldm.to(_lowercase       )
										ldm.set_progress_bar_config(disable=_lowercase       )

										UpperCAmelCase__   		=						self.dummy_image.to(_lowercase       )

										UpperCAmelCase__   		=						torch.Generator(device=_lowercase       ).manual_seed(0       )
										UpperCAmelCase__   		=						ldm(image=_lowercase    ,      generator=_lowercase    ,      num_inference_steps=2    ,      output_type="""numpy"""       ).images

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

										assert image.shape == (1, 6_4, 6_4, 3)
										UpperCAmelCase__   		=						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     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.dummy_uncond_unet
										UpperCAmelCase__   		=						DDIMScheduler()
										UpperCAmelCase__   		=						self.dummy_vq_model

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

										UpperCAmelCase__   		=						LDMSuperResolutionPipeline(unet=_lowercase    ,      vqvae=_lowercase    ,      scheduler=_lowercase       )
										ldm.to(_lowercase       )
										ldm.set_progress_bar_config(disable=_lowercase       )

										UpperCAmelCase__   		=						self.dummy_image.to(_lowercase       )

										UpperCAmelCase__   		=						ldm(_lowercase    ,      num_inference_steps=2    ,      output_type="""numpy"""       ).images

										assert image.shape == (1, 6_4, 6_4, 3)


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

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/vq_diffusion/teddy_bear_pool.png"""       )
										UpperCAmelCase__   		=						init_image.resize((6_4, 6_4)    ,      resample=PIL_INTERPOLATION["""lanczos"""]       )

										UpperCAmelCase__   		=						LDMSuperResolutionPipeline.from_pretrained("""duongna/ldm-super-resolution"""    ,      device_map="""auto"""       )
										ldm.set_progress_bar_config(disable=_lowercase       )

										UpperCAmelCase__   		=						torch.manual_seed(0       )
										UpperCAmelCase__   		=						ldm(image=_lowercase    ,      generator=_lowercase    ,      num_inference_steps=2_0    ,      output_type="""numpy"""       ).images

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

										assert image.shape == (1, 2_5_6, 2_5_6, 3)
										UpperCAmelCase__   		=						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

