


"""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


a_       							=    logging.get_logger(__name__)

a_       							=    {
    "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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'deberta-v2'







		def __init__(					self      :       Dict    ,   a__      :       Dict=12_81_00    ,   a__      :       Any=15_36    ,   a__      :       List[Any]=24    ,   a__      :       Optional[Any]=24    ,   a__      :       Optional[int]=61_44    ,   a__      :       Union[str, Any]="gelu"    ,   a__      :       Optional[Any]=0.1    ,   a__      :       List[Any]=0.1    ,   a__      :       Optional[Any]=5_12    ,   a__      :       Optional[Any]=0    ,   a__      :       int=0.0_2    ,   a__      :       Optional[int]=1E-7    ,   a__      :       Union[str, Any]=False    ,   a__      :       Union[str, Any]=-1    ,   a__      :       Union[str, Any]=0    ,   a__      :       Tuple=True    ,   a__      :       Union[str, Any]=None    ,   a__      :       Dict=0    ,   a__      :       int="gelu"    ,   **a__      :       Optional[Any]    ,   )       ->						Union[str, Any]:





						'''simple docstring'''







						super().__init__(**a__		)

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

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

						_A					  =			pos_att_type
						_A					  =			vocab_size
						_A					  =			layer_norm_eps

						_A					  =			kwargs.get("pooler_hidden_size"    ,   a__		)
						_A					  =			pooler_dropout
						_A					  =			pooler_hidden_act





class snake_case      (  _UpperCamelCase):







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





						'''simple docstring'''







						if self.task == "multiple-choice":
										_A					  =			{0: "batch", 1: "choice", 2: "sequence"}
						else:
										_A					  =			{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      :       Union[str, Any]		)       ->						int:





						'''simple docstring'''







						return 12







		def  a_ (					self      :       Any    ,   a__      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,   a__      :       int = -1    ,   a__      :       int = -1    ,   a__      :       int = -1    ,   a__      :       bool = False    ,   a__      :       Optional["TensorType"] = None    ,   a__      :       int = 3    ,   a__      :       int = 40    ,   a__      :       int = 40    ,   a__      :       "PreTrainedTokenizerBase" = None    ,   )       ->						Mapping[str, Any]:





						'''simple docstring'''







						_A					  =			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



"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





@nightly
@require_torch_gpu
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			VersatileDiffusionPipeline.from_pretrained(a__    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"cyberpunk 2077"
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

						_A					  =			"A painting of a squirrel eating a burger "
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.text_to_image(
						    prompt=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

						_A					  =			pipe.image_variation(a__    ,   generator=a__    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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