





"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__magic_name__       :					List[str]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__       :					List[Any]						=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=8     ):
						UpperCamelCase				:		Union[str, Any]				   =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase				:		Optional[int]				   =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=512							,     SCREAMING_SNAKE_CASE=512     ):
						UpperCamelCase				:		Optional[Any]				   =			pil_image.resize((w, h)							,     resample=Image.BICUBIC							,     reducing_gap=1     )
						UpperCamelCase				:		Any				   =			np.array(pil_image.convert("""RGB"""     )     )
						UpperCamelCase				:		List[Any]				   =			arr.astype(np.floataa     ) / 1_27.5 - 1
						UpperCamelCase				:		Union[str, Any]				   =			np.transpose(SCREAMING_SNAKE_CASE							,     [2, 0, 1]     )
						UpperCamelCase				:		List[Any]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )
						return image






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=_A						,   scheduler=_A						,   movq=_A						,   )
									UpperCamelCase				:		List[str]				   =			2 ** (len(self.movq.config.block_out_channels    ) - 1)







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			min(int(num_inference_steps * strength    )						,   _A    )

									UpperCamelCase				:		List[str]				   =			max(num_inference_steps - init_timestep						,   0    )
									UpperCamelCase				:		Optional[Any]				   =			self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A=None    ):






									'''simple docstring'''







									if not isinstance(_A						,   (torch.Tensor, PIL.Image.Image, list)    ):
															raise ValueError(
															    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A    )}"""    )

									UpperCamelCase				:		Optional[int]				   =			image.to(device=_A						,   dtype=_A    )

									UpperCamelCase				:		int				   =			batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															UpperCamelCase				:		List[str]				   =			image

									else:
															if isinstance(_A						,   _A    ) and len(_A    ) != batch_size:
																					raise ValueError(
																					    f"""You have passed a list of generators of length {len(_A    )}, but requested an effective batch"""
																					    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

															elif isinstance(_A						,   _A    ):
																					UpperCamelCase				:		str				   =			[
																					    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_A    )
																					]
																					UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A						,   dim=0    )
															else:
																					UpperCamelCase				:		List[str]				   =			self.movq.encode(_A    ).latent_dist.sample(_A    )

															UpperCamelCase				:		Dict				   =			self.movq.config.scaling_factor * init_latents

									UpperCamelCase				:		Tuple				   =			torch.cat([init_latents]						,   dim=0    )

									UpperCamelCase				:		List[str]				   =			init_latents.shape
									UpperCamelCase				:		int				   =			randn_tensor(_A						,   generator=_A						,   device=_A						,   dtype=_A    )

									# get latents
									UpperCamelCase				:		int				   =			self.scheduler.add_noise(_A						,   _A						,   _A    )

									UpperCamelCase				:		Optional[Any]				   =			init_latents

									return latents







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

									UpperCamelCase				:		Optional[int]				   =			[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_A						,   _A    )







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







									if is_accelerate_available() and is_accelerate_version(""">="""						,   """0.17.0.dev0"""    ):
															from accelerate import cpu_offload_with_hook
									else:
															raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

									if self.device.type != "cpu":
															self.to("""cpu"""						,   silence_dtype_warnings=_A    )
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									UpperCamelCase				:		Dict				   =			None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			cpu_offload_with_hook(_A						,   _A						,   prev_module_hook=_A    )

									# We'll offload the last model manually.
									UpperCamelCase				:		Optional[Any]				   =			hook







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def   _a      (      self    ):






									'''simple docstring'''







									if not hasattr(self.unet						,   """_hf_hook"""    ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(_A						,   """_hf_hook"""    )
															    and hasattr(module._hf_hook						,   """execution_device"""    )
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device    )
									return self.device







			@torch.no_grad()
			@replace_example_docstring(_A    )
			def __call__(      self						,   _A						,   _A						,   _A						,   _A = 5_1_2						,   _A = 5_1_2						,   _A = 1_0_0						,   _A = 4.0						,   _A = 0.3						,   _A = 1						,   _A = None						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self._execution_device

									UpperCamelCase				:		List[Any]				   =			guidance_scale > 1.0

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Dict				   =			torch.cat(_A						,   dim=0    )
									UpperCamelCase				:		Tuple				   =			image_embeds.shape[0]
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		int				   =			torch.cat(_A						,   dim=0    )

									if do_classifier_free_guidance:
															UpperCamelCase				:		str				   =			image_embeds.repeat_interleave(_A						,   dim=0    )
															UpperCamelCase				:		Optional[Any]				   =			negative_image_embeds.repeat_interleave(_A						,   dim=0    )

									UpperCamelCase				:		Dict				   =			torch.cat([negative_image_embeds, image_embeds]						,   dim=0    ).to(dtype=self.unet.dtype						,   device=_A    )

									if not isinstance(_A						,   _A    ):
															UpperCamelCase				:		Tuple				   =			[image]
									if not all(isinstance(_A						,   (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
															raise ValueError(
															    f"""Input is in incorrect format: {[type(_A    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

									UpperCamelCase				:		Any				   =			torch.cat([prepare_image(_A						,   _A						,   _A    ) for i in image]						,   dim=0    )
									UpperCamelCase				:		Union[str, Any]				   =			image.to(dtype=image_embeds.dtype						,   device=_A    )

									UpperCamelCase				:		str				   =			self.movq.encode(_A    )["""latents"""]
									UpperCamelCase				:		int				   =			latents.repeat_interleave(_A						,   dim=0    )
									self.scheduler.set_timesteps(_A						,   device=_A    )
									UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			self.get_timesteps(_A						,   _A						,   _A    )
									UpperCamelCase				:		Union[str, Any]				   =			timesteps[:1].repeat(batch_size * num_images_per_prompt    )
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			downscale_height_and_width(_A						,   _A						,   self.movq_scale_factor    )
									UpperCamelCase				:		Tuple				   =			self.prepare_latents(
									    _A						,   _A						,   _A						,   _A						,   image_embeds.dtype						,   _A						,   _A    )
									for i, t in enumerate(self.progress_bar(_A    )    ):
															# expand the latents if we are doing classifier free guidance
															UpperCamelCase				:		Optional[Any]				   =			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

															UpperCamelCase				:		Dict				   =			{"""image_embeds""": image_embeds}
															UpperCamelCase				:		int				   =			self.unet(
															    sample=_A						,   timestep=_A						,   encoder_hidden_states=_A						,   added_cond_kwargs=_A						,   return_dict=_A						,   )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			noise_pred.split(latents.shape[1]						,   dim=1    )
																					UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			noise_pred.chunk(2    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[Any]				   =			variance_pred.chunk(2    )
																					UpperCamelCase				:		Tuple				   =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					UpperCamelCase				:		Union[str, Any]				   =			torch.cat([noise_pred, variance_pred_text]						,   dim=1    )

															if not (
															    hasattr(self.scheduler.config						,   """variance_type"""    )
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					UpperCamelCase   ,						UpperCamelCase				:		int				   =			noise_pred.split(latents.shape[1]						,   dim=1    )

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase				:		List[Any]				   =			self.scheduler.step(
															    _A						,   _A						,   _A						,   generator=_A						,   )[0]

									# post-processing
									UpperCamelCase				:		Union[str, Any]				   =			self.movq.decode(_A						,   force_not_quantize=_A    )["""sample"""]

									if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

									if output_type in ["np", "pil"]:
															UpperCamelCase				:		Optional[int]				   =			image * 0.5 + 0.5
															UpperCamelCase				:		Optional[Any]				   =			image.clamp(0						,   1    )
															UpperCamelCase				:		str				   =			image.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Optional[Any]				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )









import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	str=13			,      __lowercase				:	Optional[Any]=7			,      __lowercase				:	Tuple=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Dict=True			,      __lowercase				:	Any=99			,      __lowercase				:	Any=32			,      __lowercase				:	List[Any]=5			,      __lowercase				:	Any=4			,      __lowercase				:	List[str]=37			,      __lowercase				:	Union[str, Any]="gelu"			,      __lowercase				:	Union[str, Any]=0.1			,      __lowercase				:	str=0.1			,      __lowercase				:	List[str]=5_12			,      __lowercase				:	Tuple=16			,      __lowercase				:	List[str]=2			,      __lowercase				:	Tuple=0.02			,      __lowercase				:	Optional[Any]=False			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Union[str, Any]="None"			,      __lowercase				:	Union[str, Any]=3			,      __lowercase				:	int=4			,      __lowercase				:	Tuple=None			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	relative_attention
							snake_case_						       =	position_biased_input
							snake_case_						       =	pos_att_type
							snake_case_						       =	scope






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size		)

							snake_case_						       =	None
							if self.use_input_mask:
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      vocab_size=2		)

							snake_case_						       =	None
							if self.use_token_type_ids:
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.type_vocab_size		)

							snake_case_						       =	None
							snake_case_						       =	None
							snake_case_						       =	None
							if self.use_labels:
								snake_case_						       =	ids_tensor([self.batch_size]			,      self.type_sequence_label_size		)
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels		)
								snake_case_						       =	ids_tensor([self.batch_size]			,      self.num_choices		)

							snake_case_						       =	self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return DebertaConfig(
							    vocab_size=self.vocab_size			,      hidden_size=self.hidden_size			,      num_hidden_layers=self.num_hidden_layers			,      num_attention_heads=self.num_attention_heads			,      intermediate_size=self.intermediate_size			,      hidden_act=self.hidden_act			,      hidden_dropout_prob=self.hidden_dropout_prob			,      attention_probs_dropout_prob=self.attention_probs_dropout_prob			,      max_position_embeddings=self.max_position_embeddings			,      type_vocab_size=self.type_vocab_size			,      initializer_range=self.initializer_range			,      relative_attention=self.relative_attention			,      position_biased_input=self.position_biased_input			,      pos_att_type=self.pos_att_type			,      )






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.get_config()
							snake_case_						       =	3_00
							return config






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							self.parent.assertListEqual(list(result.loss.size()		)			,      []		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[int]			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase		)[0]

							self.parent.assertListEqual(list(sequence_output.size()		)			,      [self.batch_size, self.seq_length, self.hidden_size]		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForMaskedLM(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)

							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	List[str]			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForSequenceClassification(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertListEqual(list(result.logits.size()		)			,      [self.batch_size, self.num_labels]		)
							self.check_loss_output(__lowercase		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[str]			,      __lowercase				:	Any			,      __lowercase				:	List[Any]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForTokenClassification(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForQuestionAnswering(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(
							    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      start_positions=__lowercase			,      end_positions=__lowercase			,      )
							self.parent.assertEqual(result.start_logits.shape			,      (self.batch_size, self.seq_length)		)
							self.parent.assertEqual(result.end_logits.shape			,      (self.batch_size, self.seq_length)		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	config_and_inputs
							snake_case_						       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        DebertaModel,
						        DebertaForMaskedLM,
						        DebertaForSequenceClassification,
						        DebertaForTokenClassification,
						        DebertaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {
						        '''feature-extraction''': DebertaModel,
						        '''fill-mask''': DebertaForMaskedLM,
						        '''question-answering''': DebertaForQuestionAnswering,
						        '''text-classification''': DebertaForSequenceClassification,
						        '''token-classification''': DebertaForTokenClassification,
						        '''zero-shot''': DebertaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__lowercase			,      hidden_size=37		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*__lowercase		)






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	DebertaModel.from_pretrained(__lowercase		)
								self.assertIsNotNone(__lowercase		)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@unittest.skip(reason="Model not available yet"		)
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							pass






						@slow
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel.from_pretrained("microsoft/deberta-base"		)

							snake_case_						       =	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]		)
							snake_case_						       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
							with torch.no_grad():
								snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase		)[0]
							# compare the actual values for a slice.
							snake_case_						       =	torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]		)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,      __lowercase			,      atol=1E-4		)			,      f"{output[:, 1:4, 1:4]}"		)
