
"""simple docstring"""







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 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[Any]=13							,						snake_case_   :							List[Any]=7							,						snake_case_   :							List[str]=True							,						snake_case_   :							Union[str, Any]=True							,						snake_case_   :							Optional[Any]=True							,						snake_case_   :							Any=True							,						snake_case_   :							int=99							,						snake_case_   :							Union[str, Any]=32							,						snake_case_   :							Optional[int]=5							,						snake_case_   :							Union[str, Any]=4							,						snake_case_   :							Union[str, Any]=37							,						snake_case_   :							List[str]="gelu"							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Union[str, Any]=0.1							,						snake_case_   :							List[str]=512							,						snake_case_   :							Any=16							,						snake_case_   :							List[Any]=2							,						snake_case_   :							Optional[int]=0.02							,						snake_case_   :							str=False							,						snake_case_   :							Union[str, Any]=True							,						snake_case_   :							Dict="None"							,						snake_case_   :							int=3							,						snake_case_   :							Optional[Any]=4							,						snake_case_   :							Union[str, Any]=None							,						):
												__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 							lowerCAmelCase      (				self   :							Tuple    ):
												__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 							lowerCAmelCase      (				self   :							str    ):
												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 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.get_config()
												__snake_case     			=       300
												return config





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							int    ):
												self.parent.assertListEqual(list(result.loss.size()    )							,						[]    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Dict							,						snake_case_   :							Any							,						snake_case_   :							Optional[int]							,						snake_case_   :							int							,						snake_case_   :							List[str]							,						snake_case_   :							List[Any]							,						snake_case_   :							Dict    ):
												__snake_case     			=       DebertaModel(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_    )[0]
												__snake_case     			=       model(snake_case_							,						token_type_ids=snake_case_    )[0]
												__snake_case     			=       model(snake_case_    )[0]

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





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							int							,						snake_case_   :							Any    ):
												__snake_case     			=       DebertaForMaskedLM(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_							,						labels=snake_case_    )

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





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Dict							,						snake_case_   :							List[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							List[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       DebertaForSequenceClassification(snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_							,						labels=snake_case_    )
												self.parent.assertListEqual(list(result.logits.size()    )							,						[self.batch_size, self.num_labels]    )
												self.check_loss_output(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Dict    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       DebertaForTokenClassification(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_							,						labels=snake_case_    )
												self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							int							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[str]    ):
												__snake_case     			=       DebertaForQuestionAnswering(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(
												    snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_							,						start_positions=snake_case_							,						end_positions=snake_case_							,						)
												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 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__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 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   (
					    (
					        DebertaModel,
					        DebertaForMaskedLM,
					        DebertaForSequenceClassification,
					        DebertaForTokenClassification,
					        DebertaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   (
					    {
					        'feature-extraction': DebertaModel,
					        'fill-mask': DebertaForMaskedLM,
					        'question-answering': DebertaForQuestionAnswering,
					        'text-classification': DebertaForSequenceClassification,
					        'token-classification': DebertaForTokenClassification,
					        'zero-shot': DebertaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					_SCREAMING_SNAKE_CASE       :					Tuple     							=   True
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   False
					_SCREAMING_SNAKE_CASE       :					int     							=   False
					_SCREAMING_SNAKE_CASE       :					Any     							=   False
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   False





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       DebertaModelTester(self    )
												__snake_case     			=       ConfigTester(self							,						config_class=snake_case_							,						hidden_size=37    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												self.config_tester.run_common_tests()





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_model(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_    )





					@slow
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__snake_case     			=       DebertaModel.from_pretrained(snake_case_    )
																			self.assertIsNotNone(snake_case_    )







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





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





					@slow
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       DebertaModel.from_pretrained("microsoft/deberta-base"    )

												__snake_case     			=       torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
												__snake_case     			=       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
												with torch.no_grad():
																			__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_    )[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]							,						snake_case_							,						atol=1e-4    )							,						F'''{output[:, 1:4, 1:4]}'''    )


"""simple docstring"""







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort







@nightly
@require_onnxruntime
@require_torch_gpu
class 							__magic_name__     (		unittest.TestCase   ):





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
												__snake_case     			=       load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"    )

												# using the PNDM scheduler by default
												__snake_case     			=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"							,						revision="onnx"							,						safety_checker=snake_case_							,						feature_extractor=snake_case_							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       "A red cat sitting on a park bench"

												__snake_case     			=       np.random.RandomState(0    )
												__snake_case     			=       pipe(
												    prompt=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						strength=0.75							,						guidance_scale=7.5							,						num_inference_steps=15							,						generator=snake_case_							,						output_type="np"							,						)

												__snake_case     			=       output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image    ).max() < 1e-2
