


'''simple docstring'''




from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
       import numpy as np
       import tensorflow as tf

       from transformers import (
           TF_MODEL_FOR_CAUSAL_LM_MAPPING,
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           TF_MODEL_FOR_MASKED_LM_MAPPING,
           TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
           TF_MODEL_FOR_PRETRAINING_MAPPING,
           TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           TFSharedEmbeddings,
       )

       if _tf_gpu_memory_limit is not None:
              _a      :      str       				=      tf.config.list_physical_devices("GPU")
              for gpu in gpus:
                     # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                     try:
                            tf.config.set_logical_device_configuration(
                                gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                            )
                            _a      :      Optional[Any]       				=      tf.config.list_logical_devices("GPU")
                            print("Logical GPUs", logical_gpus)
                     except RuntimeError as e:
                            # Virtual devices must be set before GPUs have been initialized
                            print(e)




@require_tf
class  _lowercase   :
   _SCREAMING_SNAKE_CASE     :     List[str]									=  None
   _SCREAMING_SNAKE_CASE     :     Any									=  ()
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  ()
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  True
   _SCREAMING_SNAKE_CASE     :     List[str]									=  True
   _SCREAMING_SNAKE_CASE     :     str									=  True
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  False
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any]=False			)   ->       Dict:
        __snake_case    			=	copy.deepcopy(SCREAMING_SNAKE_CASE_			)

        if model_class in get_values(SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	{
                 k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,      1			)					,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)			)
                 if isinstance(SCREAMING_SNAKE_CASE_					,      tf.Tensor			) and v.ndim > 0
                 else v
                 for k, v in inputs_dict.items()
             }

        if return_labels:
             if model_class in get_values(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	tf.ones(self.model_tester.batch_size					,      dtype=tf.intaa			)
             elif model_class in get_values(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa			)
                  __snake_case    			=	tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa			)
             elif model_class in [
                 *get_values(SCREAMING_SNAKE_CASE_			),
                 *get_values(SCREAMING_SNAKE_CASE_			),
             ]:
                  __snake_case    			=	tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa			)
             elif model_class in get_values(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa			)
             elif model_class in [
                 *get_values(SCREAMING_SNAKE_CASE_			),
                 *get_values(SCREAMING_SNAKE_CASE_			),
                 *get_values(SCREAMING_SNAKE_CASE_			),
                 *get_values(SCREAMING_SNAKE_CASE_			),
                 *get_values(SCREAMING_SNAKE_CASE_			),
             ]:
                  __snake_case    			=	tf.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)					,      dtype=tf.intaa			)
        return inputs_dict
   @slow
   def a     (	self					:			Dict			)   ->       Any:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
             __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)

             @tf.function
             def run_in_graph_mode():
                  return model(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	run_in_graph_mode()
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
   @slow
   def a     (	self					:			Any			)   ->       int:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
             __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)

             @tf.function(experimental_compile=SCREAMING_SNAKE_CASE_			)
             def run_in_graph_mode():
                  return model(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	run_in_graph_mode()
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
   @slow
   def a     (	self					:			Optional[Any]			)   ->       List[str]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
             __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)
             if getattr(SCREAMING_SNAKE_CASE_					,      'hf_compute_loss'					,      SCREAMING_SNAKE_CASE_			):
                  # Test that model correctly compute the loss with kwargs
                  __snake_case    			=	self._prepare_for_class(inputs_dict.copy()					,      SCREAMING_SNAKE_CASE_					,      return_labels=SCREAMING_SNAKE_CASE_			)
                  # Is there a better way to remove these decoder inputs?
                  __snake_case    			=	{
                      key: val
                      for key, val in prepared_for_class.items()
                      if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                  }

                  __snake_case    			=	{
                      "labels",
                      "label",
                      "label_ids",
                      "start_positions",
                      "start_position",
                      "end_positions",
                      "end_position",
                      "next_sentence_label",
                  }
                  __snake_case    			=	possible_label_cols.intersection(set(SCREAMING_SNAKE_CASE_			)			)
                  self.assertGreater(len(SCREAMING_SNAKE_CASE_			)					,      0					,      msg='No matching label names found!'			)
                  __snake_case    			=	{key: val for key, val in prepared_for_class.items() if key in label_names}
                  __snake_case    			=	{key: val for key, val in prepared_for_class.items() if key not in label_names}
                  self.assertGreater(len(SCREAMING_SNAKE_CASE_			)					,      0			)

                  # Make sure it works with XLA!
                  model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)					,      jit_compile=SCREAMING_SNAKE_CASE_			)
                  # Make sure the model fits without crashing regardless of where we pass the labels
                  __snake_case    			=	model.fit(
                      SCREAMING_SNAKE_CASE_					,      validation_data=SCREAMING_SNAKE_CASE_					,      steps_per_epoch=1					,      validation_steps=1					,      shuffle=SCREAMING_SNAKE_CASE_					,      verbose=0					,      )
                  __snake_case    			=	history.history["loss"][0]
                  self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)
                  __snake_case    			=	history.history["val_loss"][0]
                  self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)

                  # Now test it with separate labels, to make sure that path works in XLA too.
                  __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)
                  model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)					,      jit_compile=SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	model.fit(
                      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      validation_data=(inputs_minus_labels, labels)					,      steps_per_epoch=1					,      validation_steps=1					,      shuffle=SCREAMING_SNAKE_CASE_					,      verbose=0					,      )

                  __snake_case    			=	history.history["loss"][0]
                  self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)
                  __snake_case    			=	history.history["val_loss"][0]
                  self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)
   @slow
   def a     (	self					:			List[str]			)   ->       Optional[int]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
        __snake_case    			=	True
        __snake_case    			=	True

        if hasattr(SCREAMING_SNAKE_CASE_					,      'use_cache'			):
             __snake_case    			=	True

        __snake_case    			=	getattr(self.model_tester					,      'encoder_seq_length'					,      self.model_tester.seq_length			)
        __snake_case    			=	getattr(self.model_tester					,      'key_length'					,      SCREAMING_SNAKE_CASE_			)

        for model_class in self.all_model_classes[:2]:
             __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)
             model.build()
             __snake_case    			=	len(model(SCREAMING_SNAKE_CASE_			)			)

             for key in list(class_inputs_dict.keys()			):
                  # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                  if key not in model.input_signature:
                       del class_inputs_dict[key]
                  # Check it's a tensor, in case the inputs dict has some bools in it too
                  elif isinstance(class_inputs_dict[key]					,      tf.Tensor			) and class_inputs_dict[key].dtype.is_integer:
                       __snake_case    			=	tf.cast(class_inputs_dict[key]					,      tf.intaa			)

             if set(class_inputs_dict.keys()			) != set(model.input_signature.keys()			):
                  continue  # Some models have inputs that the preparation functions don't create, we skip those

             with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(SCREAMING_SNAKE_CASE_					,      saved_model=SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      'saved_model'					,      '1'			)
                  __snake_case    			=	tf.keras.models.load_model(SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

                  if self.is_encoder_decoder:
                       __snake_case    			=	outputs["encoder_hidden_states"]
                       __snake_case    			=	outputs["encoder_attentions"]
                  else:
                       __snake_case    			=	outputs["hidden_states"]
                       __snake_case    			=	outputs["attentions"]

                  self.assertEqual(len(SCREAMING_SNAKE_CASE_			)					,      SCREAMING_SNAKE_CASE_			)

                  __snake_case    			=	getattr(
                      self.model_tester					,      'expected_num_hidden_layers'					,      self.model_tester.num_hidden_layers + 1			)

                  self.assertEqual(len(SCREAMING_SNAKE_CASE_			)					,      SCREAMING_SNAKE_CASE_			)
                  self.assertListEqual(
                      list(output_hidden_states[0].shape[-2:]			)					,      [self.model_tester.seq_length, self.model_tester.hidden_size]					,      )

                  self.assertEqual(len(SCREAMING_SNAKE_CASE_			)					,      self.model_tester.num_hidden_layers			)
                  self.assertListEqual(
                      list(output_attentions[0].shape[-3:]			)					,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					,      )
   @slow
   def a     (	self					:			Optional[int]			)   ->       Union[str, Any]:
        tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

        # try/finally block to ensure subsequent tests run in float32
        try:
             __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes[:2]:
                  __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

                  self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        finally:
             tf.keras.mixed_precision.set_global_policy('float32'			)
   @slow
   def a     (	self					:			Any			)   ->       str:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()
        # head_mask and decoder_head_mask has different shapes than other input args
        if "head_mask" in inputs_dict:
             del inputs_dict["head_mask"]
        if "decoder_head_mask" in inputs_dict:
             del inputs_dict["decoder_head_mask"]
        if "cross_attn_head_mask" in inputs_dict:
             del inputs_dict["cross_attn_head_mask"]
        __snake_case    			=	{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__			),)
            for module_member_name in dir(SCREAMING_SNAKE_CASE_			)
            if module_member_name.endswith('MainLayer'			)
            for module_member in (getattr(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			),)
            if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(SCREAMING_SNAKE_CASE_					,      '_keras_serializable'					,      SCREAMING_SNAKE_CASE_			)
        }

        for main_layer_class in tf_main_layer_classes:
             # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
             if "T5" in main_layer_class.__name__:
                  # Take the same values than in TFT5ModelTester for this shared layer
                  __snake_case    			=	TFSharedEmbeddings(self.model_tester.vocab_size					,      self.model_tester.hidden_size					,      name='shared'			)
                  __snake_case    			=	False
                  __snake_case    			=	main_layer_class(SCREAMING_SNAKE_CASE_					,      embed_tokens=SCREAMING_SNAKE_CASE_			)
             else:
                  __snake_case    			=	main_layer_class(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	{
                 name: tf.keras.Input(tensor.shape[1:]					,      dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
             }

             if hasattr(self.model_tester					,      'num_labels'			):
                  __snake_case    			=	self.model_tester.num_labels
             else:
                  __snake_case    			=	2

             __snake_case    			=	tf.data.Dataset.from_tensor_slices(
                 (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)			))			).batch(1			)

             __snake_case    			=	main_layer(SCREAMING_SNAKE_CASE_			)[0]
             __snake_case    			=	tf.keras.layers.Dense(SCREAMING_SNAKE_CASE_					,      activation='softmax'					,      name='outputs'			)(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	tf.keras.models.Model(inputs=SCREAMING_SNAKE_CASE_					,      outputs=[outputs]			)

             model.compile(loss='binary_crossentropy'					,      optimizer='adam'					,      metrics=['binary_accuracy']			)
             model.fit(SCREAMING_SNAKE_CASE_					,      epochs=1			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                  __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      'keras_model.h5'			)
                  model.save(SCREAMING_SNAKE_CASE_			)
                  if "T5" in main_layer_class.__name__:
                       __snake_case    			=	tf.keras.models.load_model(
                           SCREAMING_SNAKE_CASE_					,      custom_objects={
                               main_layer_class.__name__: main_layer_class,
                               'TFSharedEmbeddings': TFSharedEmbeddings,
                           }					,      )
                  else:
                       __snake_case    			=	tf.keras.models.load_model(
                           SCREAMING_SNAKE_CASE_					,      custom_objects={main_layer_class.__name__: main_layer_class}			)
                  assert isinstance(SCREAMING_SNAKE_CASE_					,      tf.keras.Model			)
                  model(SCREAMING_SNAKE_CASE_			)
   @slow
   def a     (	self					:			str			)   ->       str:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
             __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	copy.deepcopy(SCREAMING_SNAKE_CASE_			)

             if not self.is_encoder_decoder:
                  __snake_case    			=	inputs["input_ids"]
                  del inputs["input_ids"]
             else:
                  __snake_case    			=	inputs["input_ids"]
                  __snake_case    			=	inputs.get('decoder_input_ids'					,      SCREAMING_SNAKE_CASE_			)
                  del inputs["input_ids"]
                  inputs.pop('decoder_input_ids'					,      SCREAMING_SNAKE_CASE_			)

             if not self.is_encoder_decoder:
                  __snake_case    			=	model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)
             else:
                  __snake_case    			=	model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             @tf.function
             def run_in_graph_mode():
                  return model(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	run_in_graph_mode()
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]			)   ->       Any:
        __snake_case    			=	[]
        if model.config.bos_token_id is not None:
             special_tokens.append(model.config.bos_token_id			)
        if model.config.pad_token_id is not None:
             special_tokens.append(model.config.pad_token_id			)
        if model.config.eos_token_id is not None:
             special_tokens.append(model.config.eos_token_id			)

        # create random bad tokens that are not special tokens
        __snake_case    			=	[]
        while len(SCREAMING_SNAKE_CASE_			) < num_bad_tokens:
             __snake_case    			=	tf.squeeze(ids_tensor((1, 1)					,      self.model_tester.vocab_size			)					,      0			).numpy()[0]
             if token not in special_tokens:
                  bad_tokens.append(SCREAMING_SNAKE_CASE_			)
        return bad_tokens
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       List[str]:







        for token_id in output_ids[0].numpy().tolist():
             self.assertGreaterEqual(SCREAMING_SNAKE_CASE_					,      0			)
             self.assertLess(SCREAMING_SNAKE_CASE_					,      self.model_tester.vocab_size			)





   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       Optional[int]:







        for bad_word_ids in bad_words_ids:
             # for all slices in batch
             for generated_ids_slice in generated_ids:
                  # for all word idx
                  for i in range(len(SCREAMING_SNAKE_CASE_			)					,      len(SCREAMING_SNAKE_CASE_			)			):
                       # if tokens match
                       if generated_ids_slice[i - len(SCREAMING_SNAKE_CASE_			) : i] == bad_word_ids:
                            return True
        return False








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




