'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   Dict							=						LEDConfig
    snake_case__							:   Dict							=						{}
    snake_case__							:   int							=						"gelu"






    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=2    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=20    ,							lowercase__=2    ,							lowercase__=1    ,							lowercase__=0    ,							lowercase__=4    ,							)							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			str    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			List[str]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Tuple    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       intermediate_size

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Dict    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Any    	=       eos_token_id
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pad_token_id
         SCREAMING_SNAKE_CASE					:			Tuple    	=       bos_token_id
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_window

         # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
         # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
         # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
         # because its local attention only attends to `self.attention_window` and one before and one after
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.attention_window + 2

         # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
         # the `test_attention_outputs` and `test_hidden_states_output` tests
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )    ,							1 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       tf.concat([input_ids, eos_tensor]    ,							axis=1 )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.config_cls(
             vocab_size=self.vocab_size    ,							d_model=self.hidden_size    ,							encoder_layers=self.num_hidden_layers    ,							decoder_layers=self.num_hidden_layers    ,							encoder_attention_heads=self.num_attention_heads    ,							decoder_attention_heads=self.num_attention_heads    ,							encoder_ffn_dim=self.intermediate_size    ,							decoder_ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_ids=[2]    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.pad_token_id    ,							attention_window=self.attention_window    ,							**self.config_updates    ,							)
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       prepare_led_inputs_dict(lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       tf.concat(
             [tf.zeros_like(lowercase__ )[:, :-1], tf.ones_like(lowercase__ )[:, -1:]]    ,							axis=-1    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       global_attention_mask
         return config, inputs_dict






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLEDModel(config=lowercase__ ).get_decoder()
         SCREAMING_SNAKE_CASE					:			int    	=       inputs_dict['input_ids']

         SCREAMING_SNAKE_CASE					:			Tuple    	=       input_ids[:1, :]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       inputs_dict['attention_mask'][:1, :]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1

         # first forward pass
         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							use_cache=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         SCREAMING_SNAKE_CASE					:			str    	=       ids_tensor((self.batch_size, 3)    ,							config.vocab_size )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.cast(ids_tensor((self.batch_size, 3)    ,							2 )    ,							tf.inta )

         # append to next input_ids and
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.concat([input_ids, next_tokens]    ,							axis=-1 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       tf.concat([attention_mask, next_attn_mask]    ,							axis=-1 )

         SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							attention_mask=lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							past_key_values=lowercase__ )[0]

         self.parent.assertEqual(next_tokens.shape[1]    ,							output_from_past.shape[1] )

         # select random slice
         SCREAMING_SNAKE_CASE					:			str    	=       int(ids_tensor((1,)    ,							output_from_past.shape[-1] ) )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       output_from_no_past[:, -3:, random_slice_idx]
         SCREAMING_SNAKE_CASE					:			Tuple    	=       output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(lowercase__    ,							lowercase__    ,							rtol=1E-3 )






def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_=None       ,      a_=None       ,      a_=None       ,      a_=None       ,      )      ->	Any:





     '''simple docstring'''





     if attention_mask is None:
          SCREAMING_SNAKE_CASE					:			int    	=       tf.cast(tf.math.not_equal(a_       ,      config.pad_token_id		)       ,      tf.inta		)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape       ,      dtype=tf.inta		),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,      config.pad_token_id		)       ,      tf.inta		),
              ]       ,      axis=-1       ,      )
     if head_mask is None:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
         "decoder_input_ids": decoder_input_ids,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
     }






@require_tf
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   str							=						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    snake_case__							:   List[str]							=						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
    snake_case__							:   Union[str, Any]							=						(
        {
            "conversational": TFLEDForConditionalGeneration,
            "feature-extraction": TFLEDModel,
            "summarization": TFLEDForConditionalGeneration,
            "text2text-generation": TFLEDForConditionalGeneration,
            "translation": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    snake_case__							:   List[Any]							=						True
    snake_case__							:   Tuple							=						False
    snake_case__							:   Dict							=						False
    snake_case__							:   int							=						False






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       TFLEDModelTester(self )
         SCREAMING_SNAKE_CASE					:			Dict    	=       ConfigTester(self    ,							config_class=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.zeros_like(inputs_dict['attention_mask'] )
         SCREAMING_SNAKE_CASE					:			str    	=       2
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.where(
             tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices    ,							1    ,							inputs_dict['global_attention_mask']    ,							)

         SCREAMING_SNAKE_CASE					:			Dict    	=       True
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.seq_length
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.decoder_attentions
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)

         def check_encoder_attentions_output(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [t.numpy() for t in outputs.encoder_attentions]
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertListEqual(
                  list(attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,							)

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True
              SCREAMING_SNAKE_CASE					:			Tuple    	=       False
              SCREAMING_SNAKE_CASE					:			str    	=       False
              SCREAMING_SNAKE_CASE					:			Any    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )
              self.assertEqual(config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )

              if self.is_encoder_decoder:
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model_class(lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
                   self.assertEqual(config.output_hidden_states    ,							lowercase__ )
                   check_decoder_attentions_output(lowercase__ )

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              SCREAMING_SNAKE_CASE					:			Dict    	=       True
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
              self.assertEqual(config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )

              # Check attention is always last and order is fine
              SCREAMING_SNAKE_CASE					:			str    	=       True
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,							len(lowercase__ ) )
              self.assertEqual(model.config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )






    @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
    def       _UpperCamelCase    (       self )							-> Tuple:
         pass






    def       _UpperCamelCase    (       self )							-> Tuple:
         # TODO: Head-masking not yet implement
         pass






def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     return tf.constant(a_       ,      dtype=tf.intaa		)


_lowerCAmelCase  :Union[str, Any]  				=    1E-4






@slow
@require_tf
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led

         # change to intended input here
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			Any    	=       prepare_led_inputs_dict(model.config    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(**lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			str    	=       (1, 1_024, 768)
         self.assertEqual(output.shape    ,							lowercase__ )
         # change to expected output here
         SCREAMING_SNAKE_CASE					:			str    	=       tf.convert_to_tensor(
             [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]    ,							)
         tf.debugging.assert_near(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-3 )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			str    	=       TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )

         # change to intended input here
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			str    	=       _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       prepare_led_inputs_dict(model.config    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       model(**lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (1, 1_024, model.config.vocab_size)
         self.assertEqual(output.shape    ,							lowercase__ )
         # change to expected output here
         SCREAMING_SNAKE_CASE					:			str    	=       tf.convert_to_tensor(
             [[33.6_507, 6.4_5_7_2, 16.8_089], [5.8_7_3_9, -2.4_2_3_8, 11.2_902], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]    ,							)
         tf.debugging.assert_near(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-3    ,							rtol=1E-3 )


'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

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






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

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






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

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



