




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
     a_   :	Tuple							   = """platform"""

     import jax
     import jax.numpy as jnp

     from transformers import AutoTokenizer
     from transformers.models.mbart.modeling_flax_mbart import (
         FlaxMBartForConditionalGeneration,
         FlaxMBartForQuestionAnswering,
         FlaxMBartForSequenceClassification,
         FlaxMBartModel,
         shift_tokens_right,
     )





def   a_							(   __snake_case							:    Dict     , __snake_case							:    Optional[Any]     , __snake_case							:    List[Any]=None     , __snake_case							:    Tuple=None     , __snake_case							:    Any=None     , __snake_case							:    Optional[Any]=None     , __snake_case							:    List[str]=None     , __snake_case							:    Any=None     , )							->		Tuple:



       """simple docstring"""
       if attention_mask is None:
              lowerCamelCase_							=np.where(input_ids != config.pad_token_id     , 1     , 0     )
       if decoder_attention_mask is None:
              lowerCamelCase_							=np.where(decoder_input_ids != config.pad_token_id     , 1     , 0     )
       if head_mask is None:
              lowerCamelCase_							=np.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              lowerCamelCase_							=np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              lowerCamelCase_							=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": decoder_attention_mask,
       }


class 						__UpperCamelCase (				unittest.TestCase       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase=13,	lowerCAmelCase=7,	lowerCAmelCase=True,	lowerCAmelCase=False,	lowerCAmelCase=99,	lowerCAmelCase=16,	lowerCAmelCase=2,	lowerCAmelCase=4,	lowerCAmelCase=4,	lowerCAmelCase="gelu",	lowerCAmelCase=0.1,	lowerCAmelCase=0.1,	lowerCAmelCase=32,	lowerCAmelCase=2,	lowerCAmelCase=1,	lowerCAmelCase=0,	lowerCAmelCase=2,	lowerCAmelCase=0.0_2,	):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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

              lowerCamelCase_							=shift_tokens_right(lowerCAmelCase,	1						)

              lowerCamelCase_							=MBartConfig(
                  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,	decoder_start_token_id=self.decoder_start_token_id,	initializer_range=self.initializer_range,	use_cache=lowerCAmelCase,	)
              lowerCamelCase_							=prepare_mbart_inputs_dict(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
              return config, inputs_dict






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=20
              lowerCamelCase_							=model_class_name(lowerCAmelCase						)

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

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

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

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

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

              lowerCamelCase_							=model.decode(lowerCAmelCase,	lowerCAmelCase						)

              lowerCamelCase_							=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,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=20
              lowerCamelCase_							=model_class_name(lowerCAmelCase						)

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

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

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

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

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

              lowerCamelCase_							=model.decode(lowerCAmelCase,	lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase						)

              lowerCamelCase_							=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 						__UpperCamelCase (				unittest.TestCase       ):
       lowercase			:					Dict   		=99






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=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_							=input_ids.shape[0]
              lowerCamelCase_							=MBartConfig(
                  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_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForSequenceClassification(lowerCAmelCase						)
              lowerCamelCase_							=model(input_ids=lowerCAmelCase,	decoder_input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(batch_size, config.num_labels)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForQuestionAnswering(lowerCAmelCase						)
              lowerCamelCase_							=model(input_ids=lowerCAmelCase						)

              self.assertEqual(outputs['''start_logits'''].shape,	input_ids.shape						)
              self.assertEqual(outputs['''end_logits'''].shape,	input_ids.shape						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForConditionalGeneration(lowerCAmelCase						)
              lowerCamelCase_							=lm_model(input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(batch_size, input_ids.shape[1], config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=MBartConfig(
                  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_							=FlaxMBartForConditionalGeneration(lowerCAmelCase						)
              lowerCamelCase_							=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],	dtype=np.intaa						)
              lowerCamelCase_							=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],	dtype=np.intaa						)
              lowerCamelCase_							=lm_model(input_ids=lowerCAmelCase,	decoder_input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(*summary.shape, config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







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





@require_flax
class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase    ,      lowerCamelCase__       ):
       lowercase			:					Any   		=True
       lowercase			:					Union[str, Any]   		=(
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       lowercase			:					Union[str, Any]   		=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=FlaxMBartModelTester(self						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=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(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

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

                            with self.subTest('''JIT Enabled'''						):
                                   lowerCamelCase_							=encode_jitted(**lowerCAmelCase						).to_tuple()

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

                            lowerCamelCase_							={
                                '''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(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):
                                   return model.decode(
                                       decoder_input_ids=lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase,	encoder_outputs=lowerCAmelCase,	)

                            with self.subTest('''JIT Enabled'''						):
                                   lowerCamelCase_							=decode_jitted(**lowerCAmelCase						).to_tuple()

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

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






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              for model_class_name in self.all_model_classes:
                     lowerCamelCase_							=model_class_name.from_pretrained('''facebook/mbart-large-cc25''',	from_pt=lowerCAmelCase						)
                     # FlaxMBartForSequenceClassification expects eos token in input_ids
                     lowerCamelCase_							=np.ones((1, 1)						) * model.config.eos_token_id
                     lowerCamelCase_							=model(lowerCAmelCase						)
                     self.assertIsNotNone(lowerCAmelCase						)





@require_flax
@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase (				unittest.TestCase       ):
       lowercase			:					Tuple   		=[
           ' UN Chief Says There Is No Military Solution in Syria',
       ]
       lowercase			:					str   		=[
           'Şeful ONU declară că nu există o soluţie militară în Siria',
       ]
       lowercase			:					Optional[int]   		='facebook/mbart-large-en-ro'






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              return AutoTokenizer.from_pretrained(self.model_name						)






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,	from_pt=lowerCAmelCase						)
              return model






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.translate_src_text(**lowerCAmelCase						)
              self.assertListEqual(self.expected_text,	lowerCAmelCase						)






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.tokenizer(self.src_text,	**lowerCAmelCase,	return_tensors='''np'''						)
              lowerCamelCase_							=self.model.generate(
                  model_inputs.input_ids,	attention_mask=model_inputs.attention_mask,	decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO'''],	early_stopping=lowerCAmelCase,	num_beams=2,	).sequences
              lowerCamelCase_							=self.tokenizer.batch_decode(lowerCAmelCase,	skip_special_tokens=lowerCAmelCase						)
              return generated_words






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              self._assert_generated_batch_equal_expected()










'''simple docstring'''




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=(HeunDiscreteScheduler,)
       lowercase			:					int   		=10






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_0_0_1,
                  '''beta_end''': 0.0_2,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**lowerCAmelCase						)
              return config






       def   lowercase__  (							self						):



              """simple docstring"""







              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              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=lowerCAmelCase,	beta_end=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for schedule in ["linear", "scaled_linear", "exp"]:
                     self.check_over_configs(beta_schedule=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for clip_sample_range in [1.0, 2.0, 3.0]:
                     self.check_over_configs(clip_sample_range=lowerCAmelCase,	clip_sample=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

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

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







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

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

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

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 4.6_934e-07						) < 1e-2
                     assert abs(result_mean.item() - 6.1_112e-10						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 4.693_428_650_170_972e-07						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







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

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

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

              if str(lowerCAmelCase						).startswith('''cpu'''						):
                     # The following sum varies between 148 and 156 on mps. Why?
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              elif str(lowerCAmelCase						).startswith('''mps'''						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase,	use_karras_sigmas=lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

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

              assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
              assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07						) < 1e-2




