




'''simple docstring'''





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		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=7		,     __lowerCAmelCase=True		,     __lowerCAmelCase=True		,     __lowerCAmelCase=True		,     __lowerCAmelCase=True		,     __lowerCAmelCase=99		,     __lowerCAmelCase=32		,     __lowerCAmelCase=2		,     __lowerCAmelCase=4		,     __lowerCAmelCase=37		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=512		,     __lowerCAmelCase=16		,     __lowerCAmelCase=2		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=3		,     __lowerCAmelCase=4		,     __lowerCAmelCase=None		,     ):







      '''simple docstring'''


      _a				:							List[str]							       =      parent
      _a				:							str							       =      13
      _a				:							str							       =      7
      _a				:							Any							       =      True
      _a				:							Union[str, Any]							       =      True
      _a				:							List[Any]							       =      True
      _a				:							List[Any]							       =      True
      _a				:							Union[str, Any]							       =      99
      _a				:							str							       =      32
      _a				:							Tuple							       =      2
      _a				:							List[Any]							       =      4
      _a				:							Any							       =      37
      _a				:							Any							       =      """gelu"""
      _a				:							str							       =      0.1
      _a				:							Tuple							       =      0.1
      _a				:							Tuple							       =      512
      _a				:							Optional[int]							       =      16
      _a				:							Optional[int]							       =      False
      _a				:							Tuple							       =      -1
      _a				:							Optional[int]							       =      True
      _a				:							Tuple							       =      2
      _a				:							int							       =      0.02
      _a				:							List[str]							       =      3
      _a				:							Any							       =      4
      _a				:							List[Any]							       =      None



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size )

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

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

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

      _a				:							int							       =      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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      TFDebertaModel(config=__lowerCAmelCase )
      _a				:							Optional[int]							       =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      _a				:							Optional[int]							       =      [input_ids, input_mask]
      _a				:							Optional[Any]							       =      model(__lowerCAmelCase )

      _a				:							Any							       =      model(__lowerCAmelCase )

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      TFDebertaForMaskedLM(config=__lowerCAmelCase )
      _a				:							Dict							       =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      _a				:							Optional[int]							       =      model(__lowerCAmelCase )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.num_labels
      _a				:							str							       =      TFDebertaForSequenceClassification(config=__lowerCAmelCase )
      _a				:							Dict							       =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Any							       =      self.num_labels
      _a				:							Optional[int]							       =      TFDebertaForTokenClassification(config=__lowerCAmelCase )
      _a				:							int							       =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      _a				:							Union[str, Any]							       =      model(__lowerCAmelCase )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Any							       =      TFDebertaForQuestionAnswering(config=__lowerCAmelCase )
      _a				:							Union[str, Any]							       =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      _a				:							Optional[Any]							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.prepare_config_and_inputs()
      (
          (
          _a
      )       , (
          _a
      )       , (
          _a
      )       , (
          _a
      )       , (
          _a
      )       , (
          _a
      )       , (
          _a
      )       , 
      )				:							Optional[Any]							       =      config_and_inputs
      _a				:							str							       =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict






@require_tf
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :int          =					(
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  a     :List[str]          =					(
      {
          'feature-extraction': TFDebertaModel,
          'fill-mask': TFDebertaForMaskedLM,
          'question-answering': TFDebertaForQuestionAnswering,
          'text-classification': TFDebertaForSequenceClassification,
          'token-classification': TFDebertaForTokenClassification,
          'zero-shot': TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  a     :str          =					False
  a     :Dict          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      TFDebertaModelTester(self )
      _a				:							List[Any]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     hidden_size=37 )



  def     a_							(			self ):







      '''simple docstring'''


      self.config_tester.run_common_tests()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
      self.assertIsNotNone(__lowerCAmelCase )






@require_tf
class      UpperCamelCase       (	unittest.TestCase       ):



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







      '''simple docstring'''


      pass



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
      _a				:							List[Any]							       =      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
      _a				:							Optional[Any]							       =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
      _a				:							List[str]							       =      model(__lowerCAmelCase		,     attention_mask=__lowerCAmelCase )[0]

      _a				:							Tuple							       =      tf.constant(
          [
              [
                  [-0.59855896, -0.80552566, -0.8462135],
                  [1.4484025, -0.93483794, -0.80593085],
                  [0.3122741, 0.00316059, -1.4131377],
              ]
          ] )
      tf.debugging.assert_near(output[:, 1:4, 1:4]		,     __lowerCAmelCase		,     atol=1e-4 )




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







      def lowercase__		( self):
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=_UpperCAmelCase)







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



