

'''simple docstring'''



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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.models.layoutlm.modeling_tf_layoutlm import (
         TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForQuestionAnswering,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForTokenClassification,
         TFLayoutLMModel,
     )



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=13      , _SCREAMING_SNAKE_CASE=7      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=99      , _SCREAMING_SNAKE_CASE=32      , _SCREAMING_SNAKE_CASE=2      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=37      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=5_12      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=2      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=10_00      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							parent
      lowerCAmelCase 						=							batch_size
      lowerCAmelCase 						=							seq_length
      lowerCAmelCase 						=							is_training
      lowerCAmelCase 						=							use_input_mask
      lowerCAmelCase 						=							use_token_type_ids
      lowerCAmelCase 						=							use_labels
      lowerCAmelCase 						=							vocab_size
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							type_sequence_label_size
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							num_labels
      lowerCAmelCase 						=							num_choices
      lowerCAmelCase 						=							scope
      lowerCAmelCase 						=							range_bbox

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      # convert bbox to numpy since TF does not support item assignment
      lowerCAmelCase 						=							ids_tensor([self.batch_size, self.seq_length, 4]      , self.range_bbox	).numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]	):
          for j in range(bbox.shape[1]	):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  lowerCAmelCase 						=							bbox[i, j, 3]
                  lowerCAmelCase 						=							bbox[i, j, 1]
                  lowerCAmelCase 						=							t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  lowerCAmelCase 						=							bbox[i, j, 2]
                  lowerCAmelCase 						=							bbox[i, j, 0]
                  lowerCAmelCase 						=							t
      lowerCAmelCase 						=							tf.convert_to_tensor(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							None
      if self.use_input_mask:
          lowerCAmelCase 						=							random_attention_mask([self.batch_size, self.seq_length]	)

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

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

      lowerCAmelCase 						=							LayoutLMConfig(
          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      , )

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMModel(config=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape      , (self.batch_size, self.hidden_size)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE      , labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.num_labels
      lowerCAmelCase 						=							TFLayoutLMForSequenceClassification(config=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.num_labels
      lowerCAmelCase 						=							TFLayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE      , labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   
      ) 						=							config_and_inputs
      lowerCAmelCase 						=							{
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict



@require_tf
class 				_snake_case					(   a_     , a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     (
      (
          TFLayoutLMModel,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForTokenClassification,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForQuestionAnswering,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     (
      {
          '''feature-extraction''': TFLayoutLMModel,
          '''fill-mask''': TFLayoutLMForMaskedLM,
          '''text-classification''': TFLayoutLMForSequenceClassification,
          '''token-classification''': TFLayoutLMForTokenClassification,
          '''zero-shot''': TFLayoutLMForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE			:       int				=     False
  SCREAMING_SNAKE_CASE			:       List[str]				=     True
  SCREAMING_SNAKE_CASE			:       str				=     10

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMModelTester(self	)
      lowerCAmelCase 						=							ConfigTester(self      , config_class=_SCREAMING_SNAKE_CASE      , hidden_size=37	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.config_tester.run_common_tests()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase 						=							TFLayoutLMModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
          self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)


  @unittest.skip('Onnx compliancy broke with TF 2.10'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass



def       snake_case   (	) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]			)  # noqa: E231
    lowerCAmelCase 						=							tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]			)  # noqa: E231
    lowerCAmelCase 						=							tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]			)  # noqa: E231
    lowerCAmelCase 						=							tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]			)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    lowerCAmelCase 						=							tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]			)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class 				_snake_case					(   unittest.TestCase			):

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	)

      lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							prepare_layoutlm_batch_inputs()

      # forward pass
      lowerCAmelCase 						=							model(input_ids=_SCREAMING_SNAKE_CASE      , bbox=_SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the sequence output on [0, :3, :3]
      lowerCAmelCase 						=							tf.convert_to_tensor(
          [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]      , )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      , _SCREAMING_SNAKE_CASE      , atol=1e-3	)	)

      # test the pooled output on [1, :3]
      lowerCAmelCase 						=							tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]	)

      self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      , _SCREAMING_SNAKE_CASE      , atol=1e-3	)	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'      , num_labels=2	)

      lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							prepare_layoutlm_batch_inputs()

      # forward pass
      lowerCAmelCase 						=							model(
          input_ids=_SCREAMING_SNAKE_CASE      , bbox=_SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE      , labels=tf.convert_to_tensor([1, 1]	)      , )

      # test whether we get a loss as a scalar
      lowerCAmelCase 						=							outputs.loss
      lowerCAmelCase 						=							(2,)
      self.assertEqual(loss.shape      , _SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      lowerCAmelCase 						=							outputs.logits
      lowerCAmelCase 						=							(2, 2)
      self.assertEqual(logits.shape      , _SCREAMING_SNAKE_CASE	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'      , num_labels=13	)

      lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							prepare_layoutlm_batch_inputs()

      # forward pass
      lowerCAmelCase 						=							model(
          input_ids=_SCREAMING_SNAKE_CASE      , bbox=_SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE      , labels=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      lowerCAmelCase 						=							outputs.logits
      lowerCAmelCase 						=							tf.convert_to_tensor((2, 25, 13)	)
      self.assertEqual(logits.shape      , _SCREAMING_SNAKE_CASE	)


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	)

      lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							prepare_layoutlm_batch_inputs()

      # forward pass
      lowerCAmelCase 						=							model(input_ids=_SCREAMING_SNAKE_CASE      , bbox=_SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      lowerCAmelCase 						=							tf.convert_to_tensor((2, 25)	)
      self.assertEqual(outputs.start_logits.shape      , _SCREAMING_SNAKE_CASE	)
      self.assertEqual(outputs.end_logits.shape      , _SCREAMING_SNAKE_CASE	)








'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (DDPMParallelScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE	)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							self.dummy_sample_deter + 0.1
      lowerCAmelCase 						=							self.dummy_sample_deter - 0.1

      lowerCAmelCase 						=							samplea.shape[0]
      lowerCAmelCase 						=							torch.stack([samplea, samplea, samplea]      , dim=0	)
      lowerCAmelCase 						=							torch.arange(_SCREAMING_SNAKE_CASE	)[0:3, None].repeat(1      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(samples.flatten(0      , 1	)      , timesteps.flatten(0      , 1	)	)
      lowerCAmelCase 						=							scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE      , timesteps.flatten(0      , 1	)      , samples.flatten(0      , 1	)	)

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
      assert abs(result_mean.item() - 0.5_005	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='`custom_timesteps` must be in descending order.'	):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
          scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE      , timesteps=_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _SCREAMING_SNAKE_CASE      , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      , ):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)





