

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)



class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Tuple     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  str=13     ,		lowerCAmelCase__						:  Optional[Any]=7     ,		lowerCAmelCase__						:  int=True     ,		lowerCAmelCase__						:  Tuple=True     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  Union[str, Any]=True     ,		lowerCAmelCase__						:  Dict=99     ,		lowerCAmelCase__						:  Tuple=32     ,		lowerCAmelCase__						:  Tuple=2     ,		lowerCAmelCase__						:  List[Any]=4     ,		lowerCAmelCase__						:  int=37     ,		lowerCAmelCase__						:  int="gelu"     ,		lowerCAmelCase__						:  List[str]=0.1     ,		lowerCAmelCase__						:  List[Any]=0.1     ,		lowerCAmelCase__						:  int=512     ,		lowerCAmelCase__						:  str=16     ,		lowerCAmelCase__						:  List[Any]=2     ,		lowerCAmelCase__						:  Optional[int]=0.02     ,		lowerCAmelCase__						:  Any=3     ,		lowerCAmelCase__						:  Optional[Any]=4     ,		lowerCAmelCase__						:  str=None     ,		):
									SCREAMING_SNAKE_CASE_:				List[str]							= parent
									SCREAMING_SNAKE_CASE_:				Any							= 13
									SCREAMING_SNAKE_CASE_:				str							= 7
									SCREAMING_SNAKE_CASE_:				Any							= True
									SCREAMING_SNAKE_CASE_:				Any							= True
									SCREAMING_SNAKE_CASE_:				List[Any]							= True
									SCREAMING_SNAKE_CASE_:				List[Any]							= True
									SCREAMING_SNAKE_CASE_:				Dict							= 99
									SCREAMING_SNAKE_CASE_:				int							= 32
									SCREAMING_SNAKE_CASE_:				str							= 2
									SCREAMING_SNAKE_CASE_:				Tuple							= 4
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 37
									SCREAMING_SNAKE_CASE_:				str							= "gelu"
									SCREAMING_SNAKE_CASE_:				str							= 0.1
									SCREAMING_SNAKE_CASE_:				List[Any]							= 0.1
									SCREAMING_SNAKE_CASE_:				Dict							= 512
									SCREAMING_SNAKE_CASE_:				str							= 16
									SCREAMING_SNAKE_CASE_:				List[str]							= False
									SCREAMING_SNAKE_CASE_:				Any							= -1
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= True
									SCREAMING_SNAKE_CASE_:				str							= 2
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 0.02
									SCREAMING_SNAKE_CASE_:				int							= 3
									SCREAMING_SNAKE_CASE_:				Dict							= 4
									SCREAMING_SNAKE_CASE_:				List[Any]							= None



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Any							= ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size)

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:				int							= random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:				List[str]							= None
									if self.use_token_type_ids:
															SCREAMING_SNAKE_CASE_:				int							= ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size)

									SCREAMING_SNAKE_CASE_:				Dict							= None
									SCREAMING_SNAKE_CASE_:				List[Any]							= None
									SCREAMING_SNAKE_CASE_:				Dict							= None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_:				Dict							= ids_tensor([self.batch_size]     ,		self.type_sequence_label_size)
															SCREAMING_SNAKE_CASE_:				Any							= ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels)

									SCREAMING_SNAKE_CASE_:				List[str]							= 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     ,		relative_attention=self.relative_attention     ,		max_relative_positions=self.max_relative_positions     ,		position_biased_input=self.position_biased_input     ,		initializer_range=self.initializer_range     ,		return_dict=lowerCAmelCase__     ,		)

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				List[str]							= TFDebertaModel(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

									SCREAMING_SNAKE_CASE_:				List[str]							= [input_ids, input_mask]
									SCREAMING_SNAKE_CASE_:				List[str]							= model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Any							= model(lowerCAmelCase__)

									self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= TFDebertaForMaskedLM(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size))



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  List[str]):
									SCREAMING_SNAKE_CASE_:				int							= self.num_labels
									SCREAMING_SNAKE_CASE_:				Optional[int]							= TFDebertaForSequenceClassification(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[Any]							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									SCREAMING_SNAKE_CASE_:				List[Any]							= model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels))



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				int							= self.num_labels
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= TFDebertaForTokenClassification(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[Any]							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels))



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				int							= TFDebertaForQuestionAnswering(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Any							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									SCREAMING_SNAKE_CASE_:				Tuple							= model(lowerCAmelCase__)
									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  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					
									):				List[Any]							= config_and_inputs
									SCREAMING_SNAKE_CASE_:				Tuple							= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict







@require_tf
class 						__lowercase       (				UpperCAmelCase_	, UpperCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							Tuple    							=  (
			    (
			        TFDebertaModel,
			        TFDebertaForMaskedLM,
			        TFDebertaForQuestionAnswering,
			        TFDebertaForSequenceClassification,
			        TFDebertaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_UpperCAmelCase     :							List[Any]    							=  (
			    {
			        '''feature-extraction''': TFDebertaModel,
			        '''fill-mask''': TFDebertaForMaskedLM,
			        '''question-answering''': TFDebertaForQuestionAnswering,
			        '''text-classification''': TFDebertaForSequenceClassification,
			        '''token-classification''': TFDebertaForTokenClassification,
			        '''zero-shot''': TFDebertaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			_UpperCAmelCase     :							List[Any]    							=  False
			_UpperCAmelCase     :							List[Any]    							=  False



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= TFDebertaModelTester(self)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= ConfigTester(self     ,		config_class=lowerCAmelCase__     ,		hidden_size=37)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									self.config_tester.run_common_tests()



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				str							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__)




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				int							= TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
									self.assertIsNotNone(lowerCAmelCase__)







@require_tf
class 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







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




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
									SCREAMING_SNAKE_CASE_:				Tuple							= tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
									SCREAMING_SNAKE_CASE_:				List[Any]							= tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__)[0]

									SCREAMING_SNAKE_CASE_:				List[Any]							= tf.constant(
									    [
									        [
									            [-0.5985_5896, -0.8055_2566, -0.846_2135],
									            [1.448_4025, -0.9348_3794, -0.8059_3085],
									            [0.312_2741, 0.0031_6059, -1.413_1377],
									        ]
									    ])
									tf.debugging.assert_near(output[:, 1:4, 1:4]     ,		lowerCAmelCase__     ,		atol=1E-4)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

									self.register_modules(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






