



'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import tensorflow as tf

						from transformers import TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		int					=					LEDConfig
	__SCREAMING_SNAKE_CASE   :		int					=					{}
	__SCREAMING_SNAKE_CASE   :		List[str]					=					'gelu'


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=False       ,      lowercase=99       ,      lowercase=32       ,      lowercase=2       ,      lowercase=4       ,      lowercase=37       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=20       ,      lowercase=2       ,      lowercase=1       ,      lowercase=0       ,      lowercase=4       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						Optional[int]	=     batch_size
				A_	:						Tuple	=     seq_length
				A_	:						Union[str, Any]	=     is_training
				A_	:						str	=     use_labels
				A_	:						Any	=     vocab_size
				A_	:						Tuple	=     hidden_size
				A_	:						Optional[Any]	=     num_hidden_layers
				A_	:						Optional[Any]	=     num_attention_heads
				A_	:						Union[str, Any]	=     intermediate_size

				A_	:						Any	=     hidden_dropout_prob
				A_	:						Dict	=     attention_probs_dropout_prob
				A_	:						int	=     max_position_embeddings
				A_	:						str	=     eos_token_id
				A_	:						int	=     pad_token_id
				A_	:						Union[str, Any]	=     bos_token_id
				A_	:						Any	=     attention_window

				# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
				# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
				# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
				# because its local attention only attends to `self.attention_window` and one before and one after
				A_	:						Union[str, Any]	=     self.attention_window + 2

				# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
				# the `test_attention_outputs` and `test_hidden_states_output` tests
				A_	:						Any	=     (
				    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
				)


	def     _a	(self							):
				A_	:						int	=     ids_tensor([self.batch_size, self.seq_length - 1]       ,      self.vocab_size							)
				A_	:						int	=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)       ,      1							)
				A_	:						List[str]	=     tf.concat([input_ids, eos_tensor]       ,      axis=1							)

				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Optional[int]	=     self.config_cls(
				    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_ids=[2]       ,      bos_token_id=self.bos_token_id       ,      pad_token_id=self.pad_token_id       ,      decoder_start_token_id=self.pad_token_id       ,      attention_window=self.attention_window       ,      **self.config_updates       ,      )
				A_	:						Optional[Any]	=     prepare_led_inputs_dict(lowercase       ,      lowercase       ,      lowercase							)
				A_	:						Any	=     tf.concat(
				    [tf.zeros_like(lowercase							)[:, :-1], tf.ones_like(lowercase							)[:, -1:]]       ,      axis=-1       ,      )
				A_	:						Union[str, Any]	=     global_attention_mask
				return config, inputs_dict


	def     _a	(self       ,      lowercase       ,      lowercase							):
				A_	:						Tuple	=     TFLEDModel(config=lowercase							).get_decoder()
				A_	:						int	=     inputs_dict["""input_ids"""]

				A_	:						List[Any]	=     input_ids[:1, :]
				A_	:						int	=     inputs_dict["""attention_mask"""][:1, :]
				A_	:						List[str]	=     1

				# first forward pass
				A_	:						Any	=     model(lowercase       ,      attention_mask=lowercase       ,      use_cache=lowercase							)

				A_,    A_	:						Optional[Any]	=     outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				A_	:						List[str]	=     ids_tensor((self.batch_size, 3)       ,      config.vocab_size							)
				A_	:						Dict	=     tf.cast(ids_tensor((self.batch_size, 3)       ,      2							)       ,      tf.inta							)

				# append to next input_ids and
				A_	:						Optional[int]	=     tf.concat([input_ids, next_tokens]       ,      axis=-1							)
				A_	:						Dict	=     tf.concat([attention_mask, next_attn_mask]       ,      axis=-1							)

				A_	:						List[Any]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase       ,      past_key_values=lowercase							)[0]

				self.parent.assertEqual(next_tokens.shape[1]       ,      output_from_past.shape[1]							)

				# select random slice
				A_	:						int	=     int(ids_tensor((1,)       ,      output_from_past.shape[-1]							)							)
				A_	:						Any	=     output_from_no_past[:, -3:, random_slice_idx]
				A_	:						str	=     output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(lowercase       ,      lowercase       ,      rtol=1E-3							)






def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      ):
			'''simple docstring'''





			if attention_mask is None:
						A_	:						int	=     tf.cast(tf.math.not_equal(lowerCamelCase__						,      config.pad_token_id    )						,      tf.inta    )
			if decoder_attention_mask is None:
						A_	:						Dict	=     tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape						,      dtype=tf.inta    ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,      config.pad_token_id    )						,      tf.inta    ),
						    ]						,      axis=-1						,      )
			if head_mask is None:
						A_	:						Any	=     tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
			if decoder_head_mask is None:
						A_	:						Any	=     tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			    "decoder_input_ids": decoder_input_ids,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			}







@require_tf
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		int					=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE   :		Any					=					(
	    {
	        'conversational': TFLEDForConditionalGeneration,
	        'feature-extraction': TFLEDModel,
	        'summarization': TFLEDForConditionalGeneration,
	        'text2text-generation': TFLEDForConditionalGeneration,
	        'translation': TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE   :		List[str]					=					True
	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		str					=					False


	def     _a	(self							):
				A_	:						List[Any]	=     TFLEDModelTester(self							)
				A_	:						str	=     ConfigTester(self       ,      config_class=lowercase							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowercase							)


	def     _a	(self							):
				A_,    A_	:						List[str]	=     self.model_tester.prepare_config_and_inputs_for_common()
				A_	:						Optional[int]	=     tf.zeros_like(inputs_dict["""attention_mask"""]							)
				A_	:						Any	=     2
				A_	:						Optional[Any]	=     tf.where(
				    tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices       ,      1       ,      inputs_dict["""global_attention_mask"""]       ,      )

				A_	:						List[str]	=     True
				A_	:						Optional[Any]	=     self.model_tester.seq_length
				A_	:						Tuple	=     self.model_tester.encoder_seq_length

				def check_decoder_attentions_output(lowercase							):
							A_	:						List[str]	=     outputs.decoder_attentions
							self.assertEqual(len(lowercase							)       ,      self.model_tester.num_hidden_layers							)
							self.assertListEqual(
							    list(decoder_attentions[0].shape[-3:]							)       ,      [self.model_tester.num_attention_heads, seq_length, seq_length]       ,      )

				def check_encoder_attentions_output(lowercase							):
							A_	:						List[Any]	=     [t.numpy() for t in outputs.encoder_attentions]
							A_	:						Optional[int]	=     [t.numpy() for t in outputs.encoder_global_attentions]
							self.assertEqual(len(lowercase							)       ,      self.model_tester.num_hidden_layers							)
							self.assertEqual(len(lowercase							)       ,      self.model_tester.num_hidden_layers							)
							self.assertListEqual(
							    list(attentions[0].shape[-3:]							)       ,      [self.model_tester.num_attention_heads, seq_length, seq_length]       ,      )
							self.assertListEqual(
							    list(global_attentions[0].shape[-3:]							)       ,      [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]       ,      )

				for model_class in self.all_model_classes:
							A_	:						Optional[int]	=     True
							A_	:						Dict	=     False
							A_	:						Optional[int]	=     False
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						int	=     model(self._prepare_for_class(lowercase       ,      lowercase							)							)
							A_	:						Tuple	=     len(lowercase							)
							self.assertEqual(config.output_hidden_states       ,      lowercase							)
							check_encoder_attentions_output(lowercase							)

							if self.is_encoder_decoder:
										A_	:						int	=     model_class(lowercase							)
										A_	:						int	=     model(self._prepare_for_class(lowercase       ,      lowercase							)							)
										self.assertEqual(config.output_hidden_states       ,      lowercase							)
										check_decoder_attentions_output(lowercase							)

							# Check that output attentions can also be changed via the config
							del inputs_dict["output_attentions"]
							A_	:						Dict	=     True
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						List[str]	=     model(self._prepare_for_class(lowercase       ,      lowercase							)							)
							self.assertEqual(config.output_hidden_states       ,      lowercase							)
							check_encoder_attentions_output(lowercase							)

							# Check attention is always last and order is fine
							A_	:						Any	=     True
							A_	:						Union[str, Any]	=     True
							A_	:						Dict	=     model_class(lowercase							)
							A_	:						Dict	=     model(self._prepare_for_class(lowercase       ,      lowercase							)							)

							self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)       ,      len(lowercase							)							)
							self.assertEqual(model.config.output_hidden_states       ,      lowercase							)
							check_encoder_attentions_output(lowercase							)


	@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				# TODO: Head-masking not yet implement
				pass






def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return tf.constant(lowerCamelCase__						,      dtype=tf.intaa    )


lowerCamelCase							:Optional[Any]				    =					1E-4







@slow
@require_tf
class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						Optional[Any]	=     TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

				# change to intended input here
				A_	:						Tuple	=     _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_	:						str	=     _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_	:						str	=     prepare_led_inputs_dict(model.config       ,      lowercase       ,      lowercase							)
				A_	:						int	=     model(**lowercase							)[0]
				A_	:						Dict	=     (1, 1024, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				# change to expected output here
				A_	:						Union[str, Any]	=     tf.convert_to_tensor(
				    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]       ,      )
				tf.debugging.assert_near(output[:, :3, :3]       ,      lowercase       ,      atol=1E-3							)


	def     _a	(self							):
				A_	:						List[str]	=     TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

				# change to intended input here
				A_	:						Optional[Any]	=     _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_	:						Dict	=     _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_	:						Any	=     prepare_led_inputs_dict(model.config       ,      lowercase       ,      lowercase							)
				A_	:						Optional[int]	=     model(**lowercase							)[0]
				A_	:						Optional[int]	=     (1, 1024, model.config.vocab_size)
				self.assertEqual(output.shape       ,      lowercase							)
				# change to expected output here
				A_	:						List[Any]	=     tf.convert_to_tensor(
				    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]       ,      )
				tf.debugging.assert_near(output[:, :3, :3]       ,      lowercase       ,      atol=1E-3       ,      rtol=1E-3							)




'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCamelCase							:Any				    =					logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				super().__init__()
				self.register_modules(
				    vae=lowercase       ,      text_encoder=lowercase       ,      tokenizer=lowercase       ,      unet=lowercase       ,      scheduler=lowercase       ,      safety_checker=lowercase       ,      feature_extractor=lowercase       ,      )


	def     _a	(self       ,      lowercase = "auto"							):
				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							A_	:						int	=     self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase							)


	def     _a	(self							):
				self.enable_attention_slicing(lowercase							)


	@torch.no_grad()
	def __call__(self       ,      lowercase       ,      lowercase = 512       ,      lowercase = 512       ,      lowercase = 50       ,      lowercase = 7.5       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = 0.0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = "pil"       ,      lowercase = True       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = None       ,      **lowercase       ,      ):

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     1
				elif isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(lowercase       ,      lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

				# get prompt text embeddings
				A_	:						Optional[Any]	=     self.tokenizer(
				    lowercase       ,      padding="""max_length"""       ,      max_length=self.tokenizer.model_max_length       ,      return_tensors="""pt"""       ,      )
				A_	:						Dict	=     text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_	:						Union[str, Any]	=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
							A_	:						Dict	=     text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
							A_	:						Any	=     self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				A_,    A_,    A_	:						Tuple	=     text_embeddings.shape
				A_	:						Optional[Any]	=     text_embeddings.repeat(1       ,      lowercase       ,      1							)
				A_	:						Any	=     text_embeddings.view(bs_embed * num_images_per_prompt       ,      lowercase       ,      -1							)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				A_	:						List[str]	=     guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							A_	:						List[str]
							if negative_prompt is None:
										A_	:						Optional[int]	=     [""""""]
							elif type(lowercase							) is not type(lowercase							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase							)} !='
										    F' {type(lowercase							)}.'							)
							elif isinstance(lowercase       ,      lowercase							):
										A_	:						Dict	=     [negative_prompt]
							elif batch_size != len(lowercase							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
							else:
										A_	:						Dict	=     negative_prompt

							A_	:						int	=     text_input_ids.shape[-1]
							A_	:						List[Any]	=     self.tokenizer(
							    lowercase       ,      padding="""max_length"""       ,      max_length=lowercase       ,      truncation=lowercase       ,      return_tensors="""pt"""       ,      )
							A_	:						str	=     self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_	:						Optional[Any]	=     uncond_embeddings.shape[1]
							A_	:						str	=     uncond_embeddings.repeat(lowercase       ,      lowercase       ,      1							)
							A_	:						List[str]	=     uncond_embeddings.view(batch_size * num_images_per_prompt       ,      lowercase       ,      -1							)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A_	:						Dict	=     torch.cat([uncond_embeddings, text_embeddings]							)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				A_	:						int	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				A_	:						Dict	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				A_	:						Dict	=     text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not exist on mps
										A_	:						Tuple	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(self.device							)
										A_	:						int	=     torch.randn(lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(
										    self.device							)
							else:
										A_	:						int	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
										A_	:						str	=     torch.randn(lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
				else:
							if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							A_	:						str	=     latents_reference.to(self.device							)
							A_	:						Tuple	=     latents.to(self.device							)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				A_	:						Optional[int]	=     (latents_shape[3] - latents_shape_reference[3]) // 2
				A_	:						Optional[int]	=     (latents_shape[2] - latents_shape_reference[2]) // 2
				A_	:						Optional[int]	=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				A_	:						int	=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				A_	:						Optional[Any]	=     0 if dx < 0 else dx
				A_	:						Optional[Any]	=     0 if dy < 0 else dy
				A_	:						Optional[int]	=     max(-dx       ,      0							)
				A_	:						List[str]	=     max(-dy       ,      0							)
				# import pdb
				# pdb.set_trace()
				A_	:						str	=     latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(lowercase							)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				A_	:						Any	=     self.scheduler.timesteps.to(self.device							)

				# scale the initial noise by the standard deviation required by the scheduler
				A_	:						Tuple	=     latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				A_	:						Dict	=     """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
				A_	:						Any	=     {}
				if accepts_eta:
							A_	:						Optional[int]	=     eta

				for i, t in enumerate(self.progress_bar(lowercase							)							):
							# expand the latents if we are doing classifier free guidance
							A_	:						Tuple	=     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
							A_	:						Tuple	=     self.scheduler.scale_model_input(lowercase       ,      lowercase							)

							# predict the noise residual
							A_	:						List[str]	=     self.unet(lowercase       ,      lowercase       ,      encoder_hidden_states=lowercase							).sample

							# perform guidance
							if do_classifier_free_guidance:
										A_,    A_	:						str	=     noise_pred.chunk(2							)
										A_	:						Optional[Any]	=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A_	:						List[str]	=     self.scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
										callback(lowercase       ,      lowercase       ,      lowercase							)

				A_	:						List[str]	=     1 / 0.1_82_15 * latents
				A_	:						List[str]	=     self.vae.decode(lowercase							).sample

				A_	:						Optional[int]	=     (image / 2 + 0.5).clamp(0       ,      1							)

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

				if self.safety_checker is not None:
							A_	:						Union[str, Any]	=     self.feature_extractor(self.numpy_to_pil(lowercase							)       ,      return_tensors="""pt"""							).to(
							    self.device							)
							A_,    A_	:						Optional[int]	=     self.safety_checker(
							    images=lowercase       ,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
				else:
							A_	:						Tuple	=     None

				if output_type == "pil":
							A_	:						Tuple	=     self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowercase       ,      nsfw_content_detected=lowercase							)