





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    snake_case_           =      'platform'

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )






def lowerCamelCase__   (							snake_case_     :  Dict	,				snake_case_     :  Dict	,				snake_case_     :  Dict=None	,				snake_case_     :  Any=None	,				snake_case_     :  Optional[Any]=None	,				snake_case_     :  Dict=None	,				snake_case_     :  Optional[int]=None	,				snake_case_     :  Dict=None	,				)				->    Union[str, Any]:
   if attention_mask is None:
      __snake_case								=       np.where(input_ids != config.pad_token_id	,				1	,				0	)
   if decoder_attention_mask is None:
      __snake_case								=       np.where(decoder_input_ids != config.pad_token_id	,				1	,				0	)
   if head_mask is None:
      __snake_case								=       np.ones((config.encoder_layers, config.encoder_attention_heads)	)
   if decoder_head_mask is None:
      __snake_case								=       np.ones((config.decoder_layers, config.decoder_attention_heads)	)
   if cross_attn_head_mask is None:
      __snake_case								=       np.ones((config.decoder_layers, config.decoder_attention_heads)	)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }

class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Dict    ,      a__	:				Union[str, Any]    ,      a__	:				Tuple=13    ,      a__	:				Optional[Any]=7    ,      a__	:				Union[str, Any]=True    ,      a__	:				Any=False    ,      a__	:				List[str]=99    ,      a__	:				Dict=16    ,      a__	:				Optional[int]=2    ,      a__	:				List[str]=4    ,      a__	:				Dict=4    ,      a__	:				List[Any]="gelu"    ,      a__	:				Dict=0.1    ,      a__	:				List[Any]=0.1    ,      a__	:				List[Any]=32    ,      a__	:				List[Any]=2    ,      a__	:				str=1    ,      a__	:				Union[str, Any]=0    ,      a__	:				List[str]=0.0_2    ,      ):





         """simple docstring"""



         __snake_case								=       parent
         __snake_case								=       batch_size
         __snake_case								=       seq_length
         __snake_case								=       is_training
         __snake_case								=       use_labels
         __snake_case								=       vocab_size
         __snake_case								=       hidden_size
         __snake_case								=       num_hidden_layers
         __snake_case								=       num_attention_heads
         __snake_case								=       intermediate_size
         __snake_case								=       hidden_act
         __snake_case								=       hidden_dropout_prob
         __snake_case								=       attention_probs_dropout_prob
         __snake_case								=       max_position_embeddings
         __snake_case								=       eos_token_id
         __snake_case								=       pad_token_id
         __snake_case								=       bos_token_id
         __snake_case								=       initializer_range


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size				)    ,      3    ,      self.vocab_size				)
         __snake_case								=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,      dtype=np.intaa				))    ,      -1				)

         __snake_case								=       shift_tokens_right(a__    ,      1    ,      2				)

         __snake_case								=       BlenderbotConfig(
             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_id=self.eos_token_id    ,      bos_token_id=self.bos_token_id    ,      pad_token_id=self.pad_token_id    ,      initializer_range=self.initializer_range    ,      use_cache=a__    ,      )
         __snake_case								=       prepare_blenderbot_inputs_dict(a__    ,      a__    ,      a__				)
         return config, inputs_dict


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.prepare_config_and_inputs()
         return config, inputs_dict


      def        a			(self	:				int    ,      a__	:				str    ,      a__	:				int    ,      a__	:				int				):





         """simple docstring"""



         __snake_case								=       20
         __snake_case								=       model_class_name(a__				)

         __snake_case								=       model.encode(inputs_dict['''input_ids''']				)

         __snake_case	,     __snake_case								=       (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __snake_case								=       model.init_cache(decoder_input_ids.shape[0]    ,      a__    ,      a__				)
         __snake_case								=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,      dtype='''i4'''				)

         __snake_case								=       jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,      )
         __snake_case								=       model.decode(
             decoder_input_ids[:, :-1]    ,      a__    ,      decoder_attention_mask=a__    ,      past_key_values=a__    ,      decoder_position_ids=a__    ,      )

         __snake_case								=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,      dtype='''i4'''				)
         __snake_case								=       model.decode(
             decoder_input_ids[:, -1:]    ,      a__    ,      decoder_attention_mask=a__    ,      past_key_values=outputs_cache.past_key_values    ,      decoder_position_ids=a__    ,      )

         __snake_case								=       model.decode(a__    ,      a__				)

         __snake_case								=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
         self.parent.assertTrue(diff < 1E-3    ,      msg=f"""Max diff is {diff}"""				)


      def        a			(self	:				Dict    ,      a__	:				Optional[int]    ,      a__	:				List[Any]    ,      a__	:				Dict				):





         """simple docstring"""



         __snake_case								=       20
         __snake_case								=       model_class_name(a__				)

         __snake_case								=       model.encode(inputs_dict['''input_ids''']				)

         __snake_case	,     __snake_case								=       (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __snake_case								=       jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
             ]    ,      axis=-1    ,      )

         __snake_case								=       model.init_cache(decoder_input_ids.shape[0]    ,      a__    ,      a__				)
         __snake_case								=       jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,      )

         __snake_case								=       model.decode(
             decoder_input_ids[:, :-1]    ,      a__    ,      decoder_attention_mask=a__    ,      past_key_values=a__    ,      decoder_position_ids=a__    ,      )
         __snake_case								=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,      dtype='''i4'''				)
         __snake_case								=       model.decode(
             decoder_input_ids[:, -1:]    ,      a__    ,      past_key_values=outputs_cache.past_key_values    ,      decoder_attention_mask=a__    ,      decoder_position_ids=a__    ,      )

         __snake_case								=       model.decode(a__    ,      a__    ,      decoder_attention_mask=a__				)

         __snake_case								=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
         self.parent.assertTrue(diff < 1E-3    ,      msg=f"""Max diff is {diff}"""				)






@require_flax
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):
      A_						:    Tuple							=   99


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]    ,      dtype=np.intaa    ,      )

         __snake_case								=       input_ids.shape[0]
         __snake_case								=       BlenderbotConfig(
             vocab_size=self.vocab_size    ,      d_model=24    ,      encoder_layers=2    ,      decoder_layers=2    ,      encoder_attention_heads=2    ,      decoder_attention_heads=2    ,      encoder_ffn_dim=32    ,      decoder_ffn_dim=32    ,      max_position_embeddings=48    ,      eos_token_id=2    ,      pad_token_id=1    ,      bos_token_id=0    ,      )
         return config, input_ids, batch_size


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case	,     __snake_case	,     __snake_case								=       self._get_config_and_data()
         __snake_case								=       FlaxBlenderbotForConditionalGeneration(a__				)
         __snake_case								=       lm_model(input_ids=a__				)
         __snake_case								=       (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,      a__				)


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       BlenderbotConfig(
             vocab_size=self.vocab_size    ,      d_model=14    ,      encoder_layers=2    ,      decoder_layers=2    ,      encoder_attention_heads=2    ,      decoder_attention_heads=2    ,      encoder_ffn_dim=8    ,      decoder_ffn_dim=8    ,      max_position_embeddings=48    ,      )
         __snake_case								=       FlaxBlenderbotForConditionalGeneration(a__				)
         __snake_case								=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,      dtype=np.intaa				)
         __snake_case								=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,      dtype=np.intaa				)
         __snake_case								=       lm_model(input_ids=a__    ,      decoder_input_ids=a__				)
         __snake_case								=       (*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,      a__				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,      dtype=np.intaa				)
         __snake_case								=       shift_tokens_right(a__    ,      1    ,      2				)
         __snake_case								=       np.equal(a__    ,      1				).astype(np.floataa				).sum()
         __snake_case								=       np.equal(a__    ,      1				).astype(np.floataa				).sum()
         self.assertEqual(shifted.shape    ,      input_ids.shape				)
         self.assertEqual(a__    ,      n_pad_before - 1				)
         self.assertTrue(np.equal(shifted[:, 0]    ,      2				).all()				)






@require_flax
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase						,				_UpperCAmelCase  ):
      A_						:    Dict							=   True
      A_						:    Tuple							=   (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      A_						:    Any							=   (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       FlaxBlenderbotModelTester(self				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(a__    ,      a__    ,      a__				)


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(a__    ,      a__    ,      a__				)


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
               __snake_case								=       self._prepare_for_class(a__    ,      a__				)
               __snake_case								=       model_class(a__				)

               @jax.jit
               def encode_jitted(a__	:				Dict    ,      a__	:				int=None    ,      **a__	:				Optional[int]				):
                  return model.encode(input_ids=a__    ,      attention_mask=a__				)

               with self.subTest('''JIT Enabled'''				):
                  __snake_case								=       encode_jitted(**a__				).to_tuple()

               with self.subTest('''JIT Disabled'''				):
                  with jax.disable_jit():
                     __snake_case								=       encode_jitted(**a__				).to_tuple()

               self.assertEqual(len(a__				)    ,      len(a__				)				)
               for jitted_output, output in zip(a__    ,      a__				):
                  self.assertEqual(jitted_output.shape    ,      output.shape				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
               __snake_case								=       model_class(a__				)
               __snake_case								=       model.encode(inputs_dict['''input_ids''']    ,      inputs_dict['''attention_mask''']				)

               __snake_case								=       {
                   '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                   '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                   '''encoder_outputs''': encoder_outputs,
               }

               @jax.jit
               def decode_jitted(a__	:				int    ,      a__	:				Union[str, Any]    ,      a__	:				List[Any]				):
                  return model.decode(
                      decoder_input_ids=a__    ,      decoder_attention_mask=a__    ,      encoder_outputs=a__    ,      )

               with self.subTest('''JIT Enabled'''				):
                  __snake_case								=       decode_jitted(**a__				).to_tuple()

               with self.subTest('''JIT Disabled'''				):
                  with jax.disable_jit():
                     __snake_case								=       decode_jitted(**a__				).to_tuple()

               self.assertEqual(len(a__				)    ,      len(a__				)				)
               for jitted_output, output in zip(a__    ,      a__				):
                  self.assertEqual(jitted_output.shape    ,      output.shape				)


      @slow
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         for model_class_name in self.all_model_classes:
            __snake_case								=       model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            __snake_case								=       np.ones((1, 1)				) * model.config.eos_token_id
            __snake_case								=       model(a__				)
            self.assertIsNotNone(a__				)


      @unittest.skipUnless(jax_device != '''cpu'''    ,      '''3B test too slow on CPU.'''				)
      @slow
      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __snake_case								=       {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __snake_case								=       FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,      from_pt=a__				)
         __snake_case								=       BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

         __snake_case								=       ['''Sam''']
         __snake_case								=       tokenizer(a__    ,      return_tensors='''jax'''				)

         __snake_case								=       model.generate(**a__    ,      **a__				)
         __snake_case								=       '''Sam is a great name. It means "sun" in Gaelic.'''

         __snake_case								=       tokenizer.batch_decode(a__    ,      **a__				)
         assert generated_txt[0].strip() == tgt_text








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (DPMSolverSinglestepScheduler,)
      A_						:    Union[str, Any]							=   (('num_inference_steps', 25),)


      def        a			(self	:				Dict    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float('''inf'''				),
             '''variance_type''': None,
         }

         config.update(**a__				)
         return config


      def        a			(self	:				str    ,      a__	:				Any=0    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)
            # copy over dummy past residuals
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               new_scheduler.set_timesteps(a__				)
               # copy over dummy past residuals
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         pass


      def        a			(self	:				List[Any]    ,      a__	:				Dict=0    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

            # copy over dummy past residuals (must be after setting timesteps)
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__				)

               # copy over dummy past residual (must be after setting timesteps)
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


      def        a			(self	:				int    ,      a__	:				Tuple=None    ,      **a__	:				List[str]				):





         """simple docstring"""



         if scheduler is None:
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       50
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_5_7_4				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3

         __snake_case								=       DEISMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       UniPCMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=a__    ,      prediction_type=a__    ,      sample_max_value=a__    ,      algorithm_type='''dpmsolver++'''    ,      solver_order=a__    ,      solver_type=a__    ,      )


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     __snake_case								=       self.full_loop(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         self.check_over_configs(lambda_min_clipped=-float('''inf'''				)				)
         self.check_over_configs(lambda_min_clipped=-5.1				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.check_over_configs(variance_type=a__				)
         self.check_over_configs(variance_type='''learned_range'''				)


      def        a			(self	:				int				):





         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.full_loop(use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_2_4_8				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_4_5_3				) < 1E-3


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''    ,      use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.0_6_4_9				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         assert sample.dtype == torch.floataa

