






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       int,    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Union[str, Any]=99,    lowerCamelCase		:       List[str]=13,    lowerCamelCase		:       Dict=7,    lowerCamelCase		:       int=9,    lowerCamelCase		:       List[str]=True,    lowerCamelCase		:       Optional[int]=True,    lowerCamelCase		:       Tuple=False,    lowerCamelCase		:       List[str]=32,    lowerCamelCase		:       Any=5,    lowerCamelCase		:       List[Any]=4,    lowerCamelCase		:       Tuple=37,    lowerCamelCase		:       str=8,    lowerCamelCase		:       Union[str, Any]=0.1,    lowerCamelCase		:       Union[str, Any]=0.002,    lowerCamelCase		:       Optional[int]=1,    lowerCamelCase		:       Any=0,    lowerCamelCase		:       int=0,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       int=None,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     encoder_seq_length
								lowercase__  					=     decoder_seq_length
								# For common tests
								lowercase__  					=     self.decoder_seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_attention_mask
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     d_ff
								lowercase__  					=     relative_attention_num_buckets
								lowercase__  					=     dropout_rate
								lowercase__  					=     initializer_factor
								lowercase__  					=     eos_token_id
								lowercase__  					=     pad_token_id
								lowercase__  					=     decoder_start_token_id
								lowercase__  					=     None
								lowercase__  					=     decoder_layers




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								return TaConfig.from_pretrained('''google/umt5-base'''    )




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       str,    lowerCamelCase		:       Tuple,    lowerCamelCase		:       str=None,    lowerCamelCase		:       Any=None,    lowerCamelCase		:       str=None,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Tuple=None,    ):

								'''simple docstring'''
								if attention_mask is None:
												lowercase__  					=     input_ids.ne(config.pad_token_id    )
								if decoder_attention_mask is None:
												lowercase__  					=     decoder_input_ids.ne(config.pad_token_id    )
								if head_mask is None:
												lowercase__  					=     torch.ones(config.num_hidden_layers,    config.num_attention_heads,    device=lowerCamelCase    )
								if decoder_head_mask is None:
												lowercase__  					=     torch.ones(config.num_decoder_layers,    config.num_attention_heads,    device=lowerCamelCase    )
								if cross_attn_head_mask is None:
												lowercase__  					=     torch.ones(
												    config.num_decoder_layers,    config.num_attention_heads,    device=lowerCamelCase    )
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.encoder_seq_length],    self.vocab_size    )
								lowercase__  					=     ids_tensor([self.batch_size, self.decoder_seq_length],    self.vocab_size    )

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								lowercase__  					=     input_ids.clamp(self.pad_token_id + 1    )
								lowercase__  					=     decoder_input_ids.clamp(self.pad_token_id + 1    )

								lowercase__  					=     self.get_config()
								lowercase__  					=     config.num_attention_heads
								lowercase__  					=     self.prepare_inputs_dict(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								return config, input_dict




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.prepare_config_and_inputs()
								return config, inputs_dict




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								return TaConfig(
								    vocab_size=166,    d_model=self.hidden_size,    d_ff=self.d_ff,    d_kv=self.hidden_size // self.num_attention_heads,    num_layers=self.num_hidden_layers,    num_decoder_layers=self.decoder_layers,    num_heads=self.num_attention_heads,    relative_attention_num_buckets=self.relative_attention_num_buckets,    dropout_rate=self.dropout_rate,    initializer_factor=self.initializer_factor,    eos_token_id=self.eos_token_id,    bos_token_id=self.pad_token_id,    pad_token_id=self.pad_token_id,    decoder_start_token_id=self.decoder_start_token_id,    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								return TaConfig(
								    vocab_size=self.vocab_size,    d_model=self.hidden_size,    d_ff=self.d_ff,    d_kv=self.hidden_size // self.num_attention_heads,    num_layers=self.num_hidden_layers,    num_decoder_layers=self.decoder_layers,    num_heads=self.num_attention_heads,    relative_attention_num_buckets=self.relative_attention_num_buckets,    dropout_rate=self.dropout_rate,    initializer_factor=self.initializer_factor,    eos_token_id=self.eos_token_id,    bos_token_id=self.pad_token_id,    pad_token_id=self.pad_token_id,    decoder_start_token_id=self.decoder_start_token_id,    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[Any],    lowerCamelCase		:       Optional[int],    ):

								'''simple docstring'''
								lowercase__  					=     UMTaModel(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  					=     model(
								    input_ids=lowerCamelCase,    decoder_input_ids=lowerCamelCase,    attention_mask=lowerCamelCase,    decoder_attention_mask=lowerCamelCase,    )
								lowercase__  					=     model(input_ids=lowerCamelCase,    decoder_input_ids=lowerCamelCase    )
								lowercase__  					=     result.last_hidden_state
								lowercase__  					=     result.past_key_values
								lowercase__  					=     result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size(),    (self.batch_size, self.encoder_seq_length, self.hidden_size)    )
								self.parent.assertEqual(decoder_output.size(),    (self.batch_size, self.decoder_seq_length, self.hidden_size)    )
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(lowerCamelCase    ),    config.num_layers    )
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]    ),    4    )




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Any,    ):

								'''simple docstring'''
								lowercase__  					=     UMTaModel(config=lowerCamelCase    ).get_decoder().to(lowerCamelCase    ).eval()
								# first forward pass
								lowercase__  					=     model(lowerCamelCase,    use_cache=lowerCamelCase    )
								lowercase__  					=     model(lowerCamelCase    )
								lowercase__  					=     model(lowerCamelCase,    use_cache=lowerCamelCase    )

								self.parent.assertTrue(len(lowerCamelCase    ) == len(lowerCamelCase    )    )
								self.parent.assertTrue(len(lowerCamelCase    ) == len(lowerCamelCase    ) + 1    )

								lowercase__					,		lowercase__  					=     outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowercase__  					=     ids_tensor((self.batch_size, 1),    config.vocab_size    )

								# append to next input_ids and
								lowercase__  					=     torch.cat([input_ids, next_tokens],    dim=-1    )

								lowercase__  					=     model(lowerCamelCase    )['''last_hidden_state''']
								lowercase__  					=     model(lowerCamelCase,    past_key_values=lowerCamelCase    )['''last_hidden_state''']

								# select random slice
								lowercase__  					=     ids_tensor((1,),    output_from_past.shape[-1]    ).item()
								lowercase__  					=     output_from_no_past[:, -1, random_slice_idx].detach()
								lowercase__  					=     output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(lowerCamelCase,    lowerCamelCase,    atol=1E-3    )    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       str,    lowerCamelCase		:       Union[str, Any],    ):

								'''simple docstring'''
								lowercase__  					=     UMTaModel(config=lowerCamelCase    ).to(lowerCamelCase    ).half().eval()
								lowercase__  					=     model(**lowerCamelCase    )['''last_hidden_state''']
								self.parent.assertFalse(torch.isnan(lowerCamelCase    ).any().item()    )



@require_torch
class  _UpperCAmelCase   (			A__						,A__						,A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			(
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				lowercase__											=			(UMTaForConditionalGeneration,) if is_torch_available() else ()
				lowercase__											=			(
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=			True
				lowercase__											=			False
				lowercase__											=			False
				lowercase__											=			True
				lowercase__											=			True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				lowercase__											=			[0.8, 0.9]




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     UMTaModelTester(self    )




				@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''    )
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								lowercase__  					=     UMTaModel(config_and_inputs[0]    ).to(lowerCamelCase    )
								with tempfile.TemporaryDirectory() as tmpdirname:
												torch.onnx.export(
												    lowerCamelCase,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),    F"""{tmpdirname}/t5_test.onnx""",    export_params=lowerCamelCase,    opset_version=9,    input_names=['''input_ids''', '''decoder_input_ids'''],    )




				@unittest.skipIf(torch_device == '''cpu''',    '''Cant do half precision'''    )
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								lowercase__  					=     config_and_inputs[0]
								lowercase__  					=     UMTaForConditionalGeneration(lowerCamelCase    ).eval()
								model.to(lowerCamelCase    )

								lowercase__  					=     {
								    '''head_mask''': torch.zeros(config.num_layers,    config.num_heads,    device=lowerCamelCase    ),
								    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers,    config.num_heads,    device=lowerCamelCase    ),
								    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers,    config.num_heads,    device=lowerCamelCase    ),
								}

								for attn_name, (name, mask) in zip(lowerCamelCase,    head_masking.items()    ):
												lowercase__  					=     {name: mask}
												# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
												if name == "head_mask":
																lowercase__  					=     torch.ones(
																    config.num_decoder_layers,    config.num_heads,    device=lowerCamelCase    )

												lowercase__  					=     model.generate(
												    config_and_inputs[1]['''input_ids'''],    num_beams=1,    max_length=3,    output_attentions=lowerCamelCase,    return_dict_in_generate=lowerCamelCase,    **lowerCamelCase,    )
												# We check the state of decoder_attentions and cross_attentions just from the last step
												lowercase__  					=     out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
												self.assertEqual(sum([w.sum().item() for w in attn_weights]    ),    0.0    )




				@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''    )
				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								pass



@require_torch
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				@unittest.skip(
				    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''    )
				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''',    return_dict=lowerCamelCase    ).to(lowerCamelCase    )
								lowercase__  					=     AutoTokenizer.from_pretrained('''google/umt5-small''',    use_fast=lowerCamelCase,    legacy=lowerCamelCase    )
								lowercase__  					=     [
								    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
								    '''No se como puedo <extra_id_0>.''',
								    '''This is the reason why we <extra_id_0> them.''',
								    '''The <extra_id_0> walks in <extra_id_1>, seats''',
								    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
								]
								lowercase__  					=     tokenizer(lowerCamelCase,    return_tensors='''pt''',    padding=lowerCamelCase    ).input_ids
								# fmt: off
								lowercase__  					=     torch.tensor(
								    [
								        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
								    ]    )
								# fmt: on
								torch.testing.assert_allclose(lowerCamelCase,    lowerCamelCase    )

								lowercase__  					=     model.generate(input_ids.to(lowerCamelCase    )    )
								lowercase__  					=     [
								    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
								    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								]
								lowercase__  					=     tokenizer.batch_decode(lowerCamelCase    )
								self.assertEqual(lowerCamelCase,    lowerCamelCase    )












import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

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

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

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																scheduler.set_timesteps(lowerCamelCase    )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




