
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 							UpperCAmelCase	:

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

					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    Union[str, Any]											=  ids_tensor([self.batch_size, self.seq_length] ,      self.vocab_size					)

											lowercase       :    Any											=  None
											if self.use_input_mask:
																	lowercase       :    List[Any]											=  random_attention_mask([self.batch_size, self.seq_length]					)

											lowercase       :    Optional[int]											=  None
											if self.use_token_type_ids:
																	lowercase       :    Tuple											=  ids_tensor([self.batch_size, self.seq_length] ,      self.type_vocab_size					)

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

											lowercase       :    Union[str, Any]											=  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        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					List[Any] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					List[Any]					):
											lowercase       :    int											=  TFDebertaModel(config=lowerCAmelCase					)
											lowercase       :    Union[str, Any]											=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

											lowercase       :    Tuple											=  [input_ids, input_mask]
											lowercase       :    str											=  model(lowerCAmelCase					)

											lowercase       :    int											=  model(lowerCAmelCase					)

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

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

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Tuple					):
											lowercase       :    Optional[int]											=  self.num_labels
											lowercase       :    List[str]											=  TFDebertaForSequenceClassification(config=lowerCAmelCase					)
											lowercase       :    str											=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}

											lowercase       :    int											=  model(lowerCAmelCase					)
											self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.num_labels)					)

					def        _lowerCAmelCase	(			self      :					List[Any] ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					str ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Dict					):
											lowercase       :    Optional[Any]											=  self.num_labels
											lowercase       :    str											=  TFDebertaForTokenClassification(config=lowerCAmelCase					)
											lowercase       :    List[str]											=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}
											lowercase       :    str											=  model(lowerCAmelCase					)
											self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length, self.num_labels)					)

					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Any ,      lowerCAmelCase      :					str ,      lowerCAmelCase      :					int					):
											lowercase       :    int											=  TFDebertaForQuestionAnswering(config=lowerCAmelCase					)
											lowercase       :    Any											=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}

											lowercase       :    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        _lowerCAmelCase	(			self      :					int					):
											lowercase       :    int											=  self.prepare_config_and_inputs()
											(
											    lowercase				
											)       :    int											=  config_and_inputs
											lowercase       :    Dict											=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
											return config, inputs_dict







@require_tf
class 							UpperCAmelCase	(   __lowerCamelCase						, __lowerCamelCase						, unittest.TestCase					):
					a__: Optional[Any]			  =				(
					    (
					        TFDebertaModel,
					        TFDebertaForMaskedLM,
					        TFDebertaForQuestionAnswering,
					        TFDebertaForSequenceClassification,
					        TFDebertaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					a__: Any			  =				(
					    {
					        """feature-extraction""": TFDebertaModel,
					        """fill-mask""": TFDebertaForMaskedLM,
					        """question-answering""": TFDebertaForQuestionAnswering,
					        """text-classification""": TFDebertaForSequenceClassification,
					        """token-classification""": TFDebertaForTokenClassification,
					        """zero-shot""": TFDebertaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					a__: Any			  =				False
					a__: Any			  =				False

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    Union[str, Any]											=  TFDebertaModelTester(self					)
											lowercase       :    int											=  ConfigTester(self ,      config_class=lowerCAmelCase ,      hidden_size=37					)

					def        _lowerCAmelCase	(			self      :					Dict					):
											self.config_tester.run_common_tests()

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											lowercase       :    Union[str, Any]											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    List[Any]											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Optional[int]					):
											lowercase       :    int											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Tuple					):
											lowercase       :    int											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Dict											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase					)




					@slow
					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    int											=  TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''					)
											self.assertIsNotNone(lowerCAmelCase					)







@require_tf
class 							UpperCAmelCase	(   unittest.TestCase					):

					@unittest.skip(reason='''Model not available yet'''					)
					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											pass




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

											lowercase       :    Optional[Any]											=  tf.constant(
											    [
											        [
											            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
											            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
											            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
											        ]
											    ]					)
											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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  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					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


