


"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
     lowerCAmelCase__				    =       '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Dict=None    ,				SCREAMING_SNAKE_CASE   :   Dict=None    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]=None    ,				SCREAMING_SNAKE_CASE   :   int=None    ,				SCREAMING_SNAKE_CASE   :   int=None    ,				SCREAMING_SNAKE_CASE   :   Tuple=None    ,				):


  '''simple docstring'''







  if attention_mask is None:
    lowerCAmelCase					:					List[str]								=							np.where(input_ids != config.pad_token_id    ,				1    ,				0    )
  if decoder_attention_mask is None:
    lowerCAmelCase					:					Optional[Any]								=							np.where(decoder_input_ids != config.pad_token_id    ,				1    ,				0    )
  if head_mask is None:
    lowerCAmelCase					:					str								=							np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    lowerCAmelCase					:					Tuple								=							np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    lowerCAmelCase					:					int								=							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__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__=13		,					snake_case__=7		,					snake_case__=True		,					snake_case__=False		,					snake_case__=99		,					snake_case__=16		,					snake_case__=2		,					snake_case__=4		,					snake_case__=4		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=32		,					snake_case__=2		,					snake_case__=1		,					snake_case__=0		,					snake_case__=0.02		,					):






         """simple docstring"""





         lowerCAmelCase					:					str								=							parent
         lowerCAmelCase					:					str								=							batch_size
         lowerCAmelCase					:					Optional[int]								=							seq_length
         lowerCAmelCase					:					int								=							is_training
         lowerCAmelCase					:					str								=							use_labels
         lowerCAmelCase					:					Dict								=							vocab_size
         lowerCAmelCase					:					Union[str, Any]								=							hidden_size
         lowerCAmelCase					:					Any								=							num_hidden_layers
         lowerCAmelCase					:					int								=							num_attention_heads
         lowerCAmelCase					:					List[str]								=							intermediate_size
         lowerCAmelCase					:					Any								=							hidden_act
         lowerCAmelCase					:					Optional[int]								=							hidden_dropout_prob
         lowerCAmelCase					:					Optional[int]								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Union[str, Any]								=							max_position_embeddings
         lowerCAmelCase					:					Tuple								=							eos_token_id
         lowerCAmelCase					:					Any								=							pad_token_id
         lowerCAmelCase					:					Dict								=							bos_token_id
         lowerCAmelCase					:					int								=							initializer_range

       def 	lowercase__							(						self						):






         """simple docstring"""





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

         lowerCAmelCase					:					Optional[Any]								=							shift_tokens_right(snake_case__		,					1		,					2						)

         lowerCAmelCase					:					Optional[int]								=							BlenderbotSmallConfig(
             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=snake_case__		,					)
         lowerCAmelCase					:					Union[str, Any]								=							prepare_blenderbot_inputs_dict(snake_case__		,					snake_case__		,					snake_case__						)
         return config, inputs_dict

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					int								=							self.prepare_config_and_inputs()
         return config, inputs_dict

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							20
         lowerCAmelCase					:					List[Any]								=							model_class_name(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							model.encode(inputs_dict["input_ids"]						)

         lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         lowerCAmelCase					:					List[str]								=							model.init_cache(decoder_input_ids.shape[0]		,					snake_case__		,					snake_case__						)
         lowerCAmelCase					:					int								=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,					dtype="i4"						)

         lowerCAmelCase					:					Tuple								=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,					)
         lowerCAmelCase					:					Optional[Any]								=							model.decode(
             decoder_input_ids[:, :-1]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=snake_case__		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					Tuple								=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,					dtype="i4"						)
         lowerCAmelCase					:					Tuple								=							model.decode(
             decoder_input_ids[:, -1:]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=outputs_cache.past_key_values		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					Optional[int]								=							model.decode(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							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 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							20
         lowerCAmelCase					:					Optional[Any]								=							model_class_name(snake_case__						)

         lowerCAmelCase					:					Tuple								=							model.encode(inputs_dict["input_ids"]						)

         lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

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

         lowerCAmelCase					:					Optional[Any]								=							model.init_cache(decoder_input_ids.shape[0]		,					snake_case__		,					snake_case__						)
         lowerCAmelCase					:					Tuple								=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,					)

         lowerCAmelCase					:					Dict								=							model.decode(
             decoder_input_ids[:, :-1]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=snake_case__		,					decoder_position_ids=snake_case__		,					)
         lowerCAmelCase					:					List[str]								=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,					dtype="i4"						)
         lowerCAmelCase					:					Union[str, Any]								=							model.decode(
             decoder_input_ids[:, -1:]		,					snake_case__		,					past_key_values=outputs_cache.past_key_values		,					decoder_attention_mask=snake_case__		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					Dict								=							model.decode(snake_case__		,					snake_case__		,					decoder_attention_mask=snake_case__						)

         lowerCAmelCase					:					Tuple								=							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    ):
       """simple docstring"""


       a  :    Tuple       =99

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							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		,					)

         lowerCAmelCase					:					int								=							input_ids.shape[0]
         lowerCAmelCase					:					Any								=							BlenderbotSmallConfig(
             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 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							self._get_config_and_data()
         lowerCAmelCase					:					Dict								=							FlaxBlenderbotSmallForConditionalGeneration(snake_case__						)
         lowerCAmelCase					:					Tuple								=							lm_model(input_ids=snake_case__						)
         lowerCAmelCase					:					Optional[int]								=							(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["logits"].shape		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							BlenderbotSmallConfig(
             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		,					)
         lowerCAmelCase					:					List[Any]								=							FlaxBlenderbotSmallForConditionalGeneration(snake_case__						)
         lowerCAmelCase					:					Tuple								=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					Dict								=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					Any								=							lm_model(input_ids=snake_case__		,					decoder_input_ids=snake_case__						)
         lowerCAmelCase					:					List[Any]								=							(*summary.shape, config.vocab_size)
         self.assertEqual(outputs["logits"].shape		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					Optional[int]								=							shift_tokens_right(snake_case__		,					1		,					2						)
         lowerCAmelCase					:					Tuple								=							np.equal(snake_case__		,					1						).astype(np.floataa						).sum()
         lowerCAmelCase					:					Any								=							np.equal(snake_case__		,					1						).astype(np.floataa						).sum()
         self.assertEqual(shifted.shape		,					input_ids.shape						)
         self.assertEqual(snake_case__		,					n_pad_before - 1						)
         self.assertTrue(np.equal(shifted[:, 0]		,					2						).all()						)


@require_flax
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase       ,   lowercase    ):
       """simple docstring"""


       a  :    List[str]       =True
       a  :    Any       =(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       a  :    Dict       =(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							FlaxBlenderbotSmallModelTester(self						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case__		,					snake_case__		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					str								=							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(snake_case__		,					snake_case__		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					str								=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
             lowerCAmelCase					:					Tuple								=							self._prepare_for_class(snake_case__		,					snake_case__						)
             lowerCAmelCase					:					str								=							model_class(snake_case__						)

             @jax.jit
             def encode_jitted(snake_case__		,					snake_case__=None		,					**snake_case__						):
               return model.encode(input_ids=snake_case__		,					attention_mask=snake_case__						)

             with self.subTest("JIT Enabled"						):
               lowerCAmelCase					:					Dict								=							encode_jitted(**snake_case__						).to_tuple()

             with self.subTest("JIT Disabled"						):
               with jax.disable_jit():
                 lowerCAmelCase					:					Union[str, Any]								=							encode_jitted(**snake_case__						).to_tuple()

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
             lowerCAmelCase					:					str								=							model_class(snake_case__						)
             lowerCAmelCase					:					List[str]								=							model.encode(inputs_dict["input_ids"]		,					inputs_dict["attention_mask"]						)

             lowerCAmelCase					:					Tuple								=							{
                 "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(snake_case__		,					snake_case__		,					snake_case__						):
               return model.decode(
                   decoder_input_ids=snake_case__		,					decoder_attention_mask=snake_case__		,					encoder_outputs=snake_case__		,					)

             with self.subTest("JIT Enabled"						):
               lowerCAmelCase					:					int								=							decode_jitted(**snake_case__						).to_tuple()

             with self.subTest("JIT Disabled"						):
               with jax.disable_jit():
                 lowerCAmelCase					:					Optional[Any]								=							decode_jitted(**snake_case__						).to_tuple()

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







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for model_class_name in self.all_model_classes:
           lowerCAmelCase					:					Tuple								=							model_class_name.from_pretrained("facebook/blenderbot_small-90M"						)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           lowerCAmelCase					:					Dict								=							np.ones((1, 1)						) * model.config.eos_token_id
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__						)
           self.assertIsNotNone(snake_case__						)









"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =(DEISMultistepScheduler,)
       a  :    str       =(("num_inference_steps", 25),)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
         }

         config.update(**snake_case__						)
         return config

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					int								=							0.1 * sample
         lowerCAmelCase					:					Tuple								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Any								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)
           # copy over dummy past residuals
           lowerCAmelCase					:					str								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					List[str]								=							scheduler_class.from_pretrained(snake_case__						)
             new_scheduler.set_timesteps(snake_case__						)
             # copy over dummy past residuals
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sample, sample
           for t in range(snake_case__		,					time_step + scheduler.config.solver_order + 1						):
             lowerCAmelCase					:					Any								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
             lowerCAmelCase					:					str								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					Optional[int]								=							0.1 * sample
         lowerCAmelCase					:					List[Any]								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					int								=							self.get_scheduler_config()
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowerCAmelCase					:					Union[str, Any]								=							dummy_past_residuals[: scheduler.config.solver_order]

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

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

           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					int								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         if scheduler is None:
           lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
           lowerCAmelCase					:					Optional[int]								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
         lowerCAmelCase					:					Tuple								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					int								=							10
         lowerCAmelCase					:					List[str]								=							self.dummy_model()
         lowerCAmelCase					:					List[Any]								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Any								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					List[Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         return sample

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase					:					Optional[Any]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Dict								=							self.get_scheduler_config()
           lowerCAmelCase					:					List[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase					:					int								=							self.dummy_sample
           lowerCAmelCase					:					int								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,					"set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,					"set_timesteps"						):
             lowerCAmelCase					:					List[str]								=							num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowerCAmelCase					:					int								=							[residual + 0.2, residual + 0.15, residual + 0.10]
           lowerCAmelCase					:					Tuple								=							dummy_past_residuals[: scheduler.config.solver_order]

           lowerCAmelCase					:					int								=							scheduler.timesteps[5]
           lowerCAmelCase					:					str								=							scheduler.timesteps[6]

           lowerCAmelCase					:					str								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

           self.assertEqual(output_a.shape		,					sample.shape						)
           self.assertEqual(output_a.shape		,					output_a.shape						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							DEISMultistepScheduler(**self.get_scheduler_config()						)
         lowerCAmelCase					:					Dict								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

         lowerCAmelCase					:					List[str]								=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Any								=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					str								=							UniPCMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Union[str, Any]								=							DEISMultistepScheduler.from_config(scheduler.config						)

         lowerCAmelCase					:					Union[str, Any]								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					str								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(thresholding=snake_case__						)
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=snake_case__		,					prediction_type=snake_case__		,					sample_max_value=snake_case__		,					algorithm_type="deis"		,					solver_order=snake_case__		,					solver_type=snake_case__		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 lowerCAmelCase					:					Any								=							self.full_loop(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 assert not torch.isnan(snake_case__						).any(), "Samples have nan numbers"

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(lower_order_final=snake_case__						)
         self.check_over_configs(lower_order_final=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=snake_case__		,					time_step=0						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.full_loop()
         lowerCAmelCase					:					Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.full_loop(prediction_type="v_prediction"						)
         lowerCAmelCase					:					List[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.091						) < 1e-3







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.scheduler_classes[0]
         lowerCAmelCase					:					str								=							self.get_scheduler_config(thresholding=snake_case__		,					dynamic_thresholding_ratio=0						)
         lowerCAmelCase					:					Optional[Any]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							10
         lowerCAmelCase					:					Tuple								=							self.dummy_model()
         lowerCAmelCase					:					List[str]								=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					Tuple								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         assert sample.dtype == torch.floataa





