"""simple docstring"""





from __future__ import annotations

import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertModel,
				)
@require_tf
class  _snake_case     (					a__				, a__				, unittest.TestCase		):
						snake_case__			  =			(
						    (
						        TFMobileBertModel,
						        TFMobileBertForMaskedLM,
						        TFMobileBertForNextSentencePrediction,
						        TFMobileBertForPreTraining,
						        TFMobileBertForQuestionAnswering,
						        TFMobileBertForSequenceClassification,
						        TFMobileBertForTokenClassification,
						        TFMobileBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						snake_case__			  =			(
						    {
						        "feature-extraction": TFMobileBertModel,
						        "fill-mask": TFMobileBertForMaskedLM,
						        "question-answering": TFMobileBertForQuestionAnswering,
						        "text-classification": TFMobileBertForSequenceClassification,
						        "token-classification": TFMobileBertForTokenClassification,
						        "zero-shot": TFMobileBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						snake_case__			  =			False
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Union[str, Any]=False    ):
													__lowerCamelCase					:		Optional[Any]		   =     super()._prepare_for_class(UpperCAmelCase							,					UpperCAmelCase							,					return_labels=UpperCAmelCase    )

													if return_labels:
																				if model_class in get_values(UpperCAmelCase    ):
																											__lowerCamelCase					:		Union[str, Any]		   =     tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa    )

													return inputs_dict
						class  _snake_case     (					a__		):






												def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]=13							,					UpperCAmelCase      :			Union[str, Any]=7							,					UpperCAmelCase      :			Optional[int]=True							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Optional[int]=99							,					UpperCAmelCase      :			int=32							,					UpperCAmelCase      :			int=32							,					UpperCAmelCase      :			Union[str, Any]=2							,					UpperCAmelCase      :			Dict=4							,					UpperCAmelCase      :			str=37							,					UpperCAmelCase      :			Dict="gelu"							,					UpperCAmelCase      :			Any=0.1							,					UpperCAmelCase      :			Optional[Any]=0.1							,					UpperCAmelCase      :			Dict=512							,					UpperCAmelCase      :			Tuple=16							,					UpperCAmelCase      :			Optional[int]=2							,					UpperCAmelCase      :			int=0.0_2							,					UpperCAmelCase      :			int=3							,					UpperCAmelCase      :			Tuple=4							,					UpperCAmelCase      :			Tuple=None							,					):
																			__lowerCamelCase					:		Optional[int]		   =     parent
																			__lowerCamelCase					:		Tuple		   =     batch_size
																			__lowerCamelCase					:		Optional[Any]		   =     seq_length
																			__lowerCamelCase					:		str		   =     is_training
																			__lowerCamelCase					:		str		   =     use_input_mask
																			__lowerCamelCase					:		Optional[Any]		   =     use_token_type_ids
																			__lowerCamelCase					:		str		   =     use_labels
																			__lowerCamelCase					:		int		   =     vocab_size
																			__lowerCamelCase					:		int		   =     hidden_size
																			__lowerCamelCase					:		str		   =     num_hidden_layers
																			__lowerCamelCase					:		Any		   =     num_attention_heads
																			__lowerCamelCase					:		Dict		   =     intermediate_size
																			__lowerCamelCase					:		Dict		   =     hidden_act
																			__lowerCamelCase					:		Tuple		   =     hidden_dropout_prob
																			__lowerCamelCase					:		Dict		   =     attention_probs_dropout_prob
																			__lowerCamelCase					:		Tuple		   =     max_position_embeddings
																			__lowerCamelCase					:		List[Any]		   =     type_vocab_size
																			__lowerCamelCase					:		Optional[Any]		   =     type_sequence_label_size
																			__lowerCamelCase					:		Any		   =     initializer_range
																			__lowerCamelCase					:		Optional[int]		   =     num_labels
																			__lowerCamelCase					:		Optional[int]		   =     num_choices
																			__lowerCamelCase					:		Tuple		   =     scope
																			__lowerCamelCase					:		Tuple		   =     embedding_size






												def       lowerCamelCase__     (       self      :			int    ):
																			__lowerCamelCase					:		Any		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

																			__lowerCamelCase					:		Dict		   =     None
																			if self.use_input_mask:
																										__lowerCamelCase					:		Optional[Any]		   =     random_attention_mask([self.batch_size, self.seq_length]    )

																			__lowerCamelCase					:		Union[str, Any]		   =     None
																			if self.use_token_type_ids:
																										__lowerCamelCase					:		str		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.type_vocab_size    )

																			__lowerCamelCase					:		Dict		   =     None
																			__lowerCamelCase					:		str		   =     None
																			__lowerCamelCase					:		Any		   =     None
																			if self.use_labels:
																										__lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size]							,					self.type_sequence_label_size    )
																										__lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels    )
																										__lowerCamelCase					:		Union[str, Any]		   =     ids_tensor([self.batch_size]							,					self.num_choices    )

																			__lowerCamelCase					:		Tuple		   =     MobileBertConfig(
																			    vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					type_vocab_size=self.type_vocab_size							,					initializer_range=self.initializer_range							,					embedding_size=self.embedding_size							,					)

																			return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






												def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]    ):
																			__lowerCamelCase					:		Optional[Any]		   =     TFMobileBertModel(config=UpperCAmelCase    )
																			__lowerCamelCase					:		List[str]		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		Optional[Any]		   =     model(UpperCAmelCase    )

																			__lowerCamelCase					:		str		   =     [input_ids, input_mask]
																			__lowerCamelCase					:		Union[str, Any]		   =     model(UpperCAmelCase    )

																			__lowerCamelCase					:		Union[str, Any]		   =     model(UpperCAmelCase    )

																			self.parent.assertEqual(
																			    result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )
																			self.parent.assertEqual(result.pooler_output.shape							,					(self.batch_size, self.hidden_size)    )






												def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple    ):
																			__lowerCamelCase					:		str		   =     TFMobileBertForMaskedLM(config=UpperCAmelCase    )
																			__lowerCamelCase					:		int		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )






												def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
																			__lowerCamelCase					:		Optional[int]		   =     TFMobileBertForNextSentencePrediction(config=UpperCAmelCase    )
																			__lowerCamelCase					:		Optional[int]		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		Any		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.logits.shape							,					(self.batch_size, 2)    )






												def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]    ):
																			__lowerCamelCase					:		Union[str, Any]		   =     TFMobileBertForPreTraining(config=UpperCAmelCase    )
																			__lowerCamelCase					:		Optional[int]		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		str		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(
																			    result.prediction_logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )
																			self.parent.assertEqual(result.seq_relationship_logits.shape							,					(self.batch_size, 2)    )






												def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Any    ):
																			__lowerCamelCase					:		int		   =     self.num_labels
																			__lowerCamelCase					:		List[Any]		   =     TFMobileBertForSequenceClassification(config=UpperCAmelCase    )
																			__lowerCamelCase					:		int		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )






												def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			List[str]    ):
																			__lowerCamelCase					:		List[str]		   =     self.num_choices
																			__lowerCamelCase					:		Optional[int]		   =     TFMobileBertForMultipleChoice(config=UpperCAmelCase    )
																			__lowerCamelCase					:		int		   =     tf.tile(tf.expand_dims(UpperCAmelCase							,					1    )							,					(1, self.num_choices, 1)    )
																			__lowerCamelCase					:		Optional[Any]		   =     tf.tile(tf.expand_dims(UpperCAmelCase							,					1    )							,					(1, self.num_choices, 1)    )
																			__lowerCamelCase					:		Tuple		   =     tf.tile(tf.expand_dims(UpperCAmelCase							,					1    )							,					(1, self.num_choices, 1)    )
																			__lowerCamelCase					:		List[Any]		   =     {
																			    "input_ids": multiple_choice_inputs_ids,
																			    "attention_mask": multiple_choice_input_mask,
																			    "token_type_ids": multiple_choice_token_type_ids,
																			}
																			__lowerCamelCase					:		Union[str, Any]		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)    )






												def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Optional[Any]    ):
																			__lowerCamelCase					:		str		   =     self.num_labels
																			__lowerCamelCase					:		Optional[int]		   =     TFMobileBertForTokenClassification(config=UpperCAmelCase    )
																			__lowerCamelCase					:		Dict		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)    )






												def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict    ):
																			__lowerCamelCase					:		Union[str, Any]		   =     TFMobileBertForQuestionAnswering(config=UpperCAmelCase    )
																			__lowerCamelCase					:		Union[str, Any]		   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
																			__lowerCamelCase					:		int		   =     model(UpperCAmelCase    )
																			self.parent.assertEqual(result.start_logits.shape							,					(self.batch_size, self.seq_length)    )
																			self.parent.assertEqual(result.end_logits.shape							,					(self.batch_size, self.seq_length)    )







												def       lowerCamelCase__     (       self      :			Any    ):
																			__lowerCamelCase					:		Tuple		   =     self.prepare_config_and_inputs()
																			(
																			    (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  (
																			    __lowerCamelCase
																			)   ,  
																			)					:		List[Any]		   =     config_and_inputs
																			__lowerCamelCase					:		str		   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
																			return config, inputs_dict






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		List[Any]		   =     TFMobileBertModelTest.TFMobileBertModelTester(self    )
													__lowerCamelCase					:		Any		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					hidden_size=37    )






						def       lowerCamelCase__     (       self      :			Any    ):
													self.config_tester.run_common_tests()






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		int		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCAmelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			Dict    ):
													# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													for model_name in ["google/mobilebert-uncased"]:
																				__lowerCamelCase					:		Tuple		   =     TFMobileBertModel.from_pretrained(UpperCAmelCase    )
																				self.assertIsNotNone(UpperCAmelCase    )

@require_tf
class  _snake_case     (					unittest.TestCase		):






						@slow
						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Optional[int]		   =     TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased"    )
													__lowerCamelCase					:		List[Any]		   =     tf.constant([[0, 1, 2, 3, 4, 5]]    )
													__lowerCamelCase					:		Optional[Any]		   =     model(UpperCAmelCase    )[0]

													__lowerCamelCase					:		Optional[int]		   =     [1, 6, 30522]
													self.assertEqual(output.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     tf.constant(
													    [
													        [
													            [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
													            [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
													            [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
													        ]
													    ]    )
													tf.debugging.assert_near(output[:, :3, :3]							,					UpperCAmelCase							,					atol=1E-4    )
"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													for l in label:
																				if l not in self.labels:
																											raise ValueError(
																											    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""    )

													return [self.labels[l] for l in label]







						@torch.no_grad()
						def __call__(       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

													# set step values
													self.scheduler.set_timesteps(UpperCAmelCase    )

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																											# This would be a good case for the `match` statement (Python 3.10+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (samples / 2 + 0.5).clamp(0							,					1    )

													# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )