






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFEfficientFormerForImageClassification,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_tf_efficientformer import (
           TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor


class   __SCREAMING_SNAKE_CASE:
       def __init__(     self:      Optional[int]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      int = 13       ,	UpperCamelCase:      int = 64       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 3       ,	UpperCamelCase:      int = 3       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      int = 1_28       ,	UpperCamelCase:      Optional[Any]=[16, 32, 64, 1_28]       ,	UpperCamelCase:      int = 7       ,	UpperCamelCase:      int = 4       ,	UpperCamelCase:      int = 37       ,	UpperCamelCase:      str = "gelu"       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      int = 10       ,	UpperCamelCase:      float = 0.02       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 1       ,	UpperCamelCase:      int = 1_28       ,	UpperCamelCase:      List[int] = [2, 2, 2, 2]       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 2       ,	)		->		Dict:
           snake_case__									=     parent
           snake_case__									=     batch_size
           snake_case__									=     image_size
           snake_case__									=     patch_size
           snake_case__									=     num_channels
           snake_case__									=     is_training
           snake_case__									=     use_labels
           snake_case__									=     hidden_size
           snake_case__									=     num_hidden_layers
           snake_case__									=     num_attention_heads
           snake_case__									=     intermediate_size
           snake_case__									=     hidden_act
           snake_case__									=     hidden_dropout_prob
           snake_case__									=     attention_probs_dropout_prob
           snake_case__									=     type_sequence_label_size
           snake_case__									=     initializer_range
           snake_case__									=     encoder_stride
           snake_case__									=     num_attention_outputs
           snake_case__									=     embed_dim
           snake_case__									=     embed_dim + 1
           snake_case__									=     resolution
           snake_case__									=     depths
           snake_case__									=     hidden_sizes
           snake_case__									=     dim
           snake_case__									=     mlp_expansion_ratio
       def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
           snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           snake_case__									=     None
           if self.use_labels:
               snake_case__									=     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size						)

           snake_case__									=     self.get_config()

           return config, pixel_values, labels
       def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
           return EfficientFormerConfig(
               image_size=self.image_size       ,	patch_size=self.patch_size       ,	num_channels=self.num_channels       ,	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       ,	is_decoder=UpperCamelCase       ,	initializer_range=self.initializer_range       ,	encoder_stride=self.encoder_stride       ,	resolution=self.resolution       ,	depths=self.depths       ,	hidden_sizes=self.hidden_sizes       ,	dim=self.dim       ,	mlp_expansion_ratio=self.mlp_expansion_ratio       ,	)
       def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]						)		->		int:
           snake_case__									=     TFEfficientFormerModel(config=UpperCamelCase						)
           snake_case__									=     model(UpperCamelCase       ,	training=UpperCamelCase						)
           self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)						)
       def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Tuple						)		->		List[Any]:
           snake_case__									=     self.type_sequence_label_size
           snake_case__									=     TFEfficientFormerForImageClassification(UpperCamelCase						)
           snake_case__									=     model(UpperCamelCase       ,	labels=UpperCamelCase       ,	training=UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)

           # test greyscale images
           snake_case__									=     1
           snake_case__									=     TFEfficientFormerForImageClassification(UpperCamelCase						)

           snake_case__									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           snake_case__									=     model(UpperCamelCase       ,	labels=UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)


       def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[int]:
           snake_case__									=     self.prepare_config_and_inputs()
           snake_case__							,						snake_case__							,						snake_case__									=     config_and_inputs
           snake_case__									=     {'pixel_values': pixel_values}
           return config, inputs_dict


@require_tf
class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
       _UpperCAmelCase     =       (
           (
               TFEfficientFormerModel,
               TFEfficientFormerForImageClassificationWithTeacher,
               TFEfficientFormerForImageClassification,
           )
           if is_tf_available()
           else ()
       )
       _UpperCAmelCase     =       (
           {
               "feature-extraction": TFEfficientFormerModel,
               "image-classification": (
                   TFEfficientFormerForImageClassification,
                   TFEfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_tf_available()
           else {}
       )

       _UpperCAmelCase     =       False

       _UpperCAmelCase     =       False
       _UpperCAmelCase     =       False
       _UpperCAmelCase     =       False
       _UpperCAmelCase     =       False
       def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
           snake_case__									=     TFEfficientFormerModelTester(self						)
           snake_case__									=     ConfigTester(
               self       ,	config_class=UpperCamelCase       ,	has_text_modality=UpperCamelCase       ,	hidden_size=37						)
       def lowerCAmelCase_ (     self:      List[Any]						)		->		List[Any]:
           self.config_tester.run_common_tests()
       @unittest.skip(reason='EfficientFormer does not use inputs_embeds'						)
       def lowerCAmelCase_ (     self:      int						)		->		str:
           pass
       @unittest.skip(reason='EfficientFormer does not support input and output embeddings'						)
       def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[int]:
           pass
       def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
           snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               snake_case__									=     model_class(UpperCamelCase						)
               snake_case__									=     inspect.signature(model.call						)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               snake_case__									=     [*signature.parameters.keys()]

               snake_case__									=     ['pixel_values']
               self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
       def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
           def check_hidden_states_output(UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[int]						):
               snake_case__									=     model_class(UpperCamelCase						)

               snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)       ,	training=UpperCamelCase						)
               snake_case__									=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               snake_case__									=     getattr(
                   self.model_tester       ,	'expected_num_hidden_layers'       ,	self.model_tester.num_hidden_layers + 1						)
               self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

               if hasattr(self.model_tester       ,	'encoder_seq_length'						):
                   snake_case__									=     self.model_tester.encoder_seq_length
                   if hasattr(self.model_tester       ,	'chunk_length'						) and self.model_tester.chunk_length > 1:
                       snake_case__									=     seq_length * self.model_tester.chunk_length
               else:
                   snake_case__									=     self.model_tester.seq_length

               self.assertListEqual(
                   list(hidden_states[-1].shape[-2:]						)       ,	[seq_length, self.model_tester.hidden_size]       ,	)

               if config.is_encoder_decoder:
                   snake_case__									=     outputs.decoder_hidden_states

                   self.asseretIsInstance(UpperCamelCase       ,	(list, tuple)						)
                   self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)
                   snake_case__									=     getattr(self.model_tester       ,	'seq_length'       ,	UpperCamelCase						)
                   snake_case__									=     getattr(self.model_tester       ,	'decoder_seq_length'       ,	UpperCamelCase						)

                   self.assertListEqual(
                       list(hidden_states[-1].shape[-2:]						)       ,	[decoder_seq_length, self.model_tester.hidden_size]       ,	)

           snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               snake_case__									=     True
               check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               snake_case__									=     True

               check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
       def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      List[Any]=False						)		->		List[str]:
           snake_case__									=     super()._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)

           if return_labels:
               if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

           return inputs_dict
       def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Union[str, Any]:
           snake_case__									=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase						)
       @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'						)
       def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[Any]:
           snake_case__									=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase						)
       def lowerCAmelCase_ (     self:      Tuple						)		->		Dict:
           snake_case__									=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase						)
       @slow
       def lowerCAmelCase_ (     self:      Dict						)		->		Dict:
           for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               snake_case__									=     TFEfficientFormerModel.from_pretrained(UpperCamelCase						)
               self.assertIsNotNone(UpperCamelCase						)
       def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
           snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
           snake_case__									=     True

           snake_case__									=     getattr(self.model_tester       ,	'seq_length'       ,	UpperCamelCase						)
           snake_case__									=     getattr(self.model_tester       ,	'encoder_seq_length'       ,	UpperCamelCase						)
           snake_case__									=     getattr(self.model_tester       ,	'key_length'       ,	UpperCamelCase						)
           snake_case__									=     getattr(self.model_tester       ,	'chunk_length'       ,	UpperCamelCase						)

           if chunk_length is not None and hasattr(self.model_tester       ,	'num_hashes'						):
               snake_case__									=     encoder_seq_length * self.model_tester.num_hashes

           for model_class in self.all_model_classes:
               snake_case__									=     True
               snake_case__									=     False
               snake_case__									=     True
               snake_case__									=     model_class(UpperCamelCase						)

               snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)       ,	training=UpperCamelCase						)
               snake_case__									=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
               self.assertEqual(len(UpperCamelCase						)       ,	self.model_tester.num_attention_outputs						)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               snake_case__									=     True
               snake_case__									=     model_class(UpperCamelCase						)
               snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)       ,	training=UpperCamelCase						)

               snake_case__									=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
               self.assertEqual(len(UpperCamelCase						)       ,	self.model_tester.num_attention_outputs						)

               if chunk_length is not None:
                   self.assertListEqual(
                       list(attentions[0].shape[-4:]						)       ,	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,	)
               else:
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]						)       ,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,	)


       def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[Any]:
           # We use a simplified version of this test for EfficientFormer because it requires training=False
           # and Keras refuses to let us force that during functional construction
           snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               # Prepare our model
               snake_case__									=     model_class(UpperCamelCase						)
               # These are maximally general inputs for the model, with multiple None dimensions
               # Hopefully this will catch any conditionals that fail for flexible shapes
               snake_case__									=     {
                   key: tf.keras.Input(shape=val.shape[1:]       ,	dtype=val.dtype       ,	name=UpperCamelCase						)
                   for key, val in model.input_signature.items()
                   if key in model.dummy_inputs
               }
               snake_case__									=     model(UpperCamelCase						)
               self.assertTrue(outputs_dict is not None						)


def      a_ (      )			->			Dict:







    """simple docstring"""
    snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
    return image


@require_tf
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
       @cached_property
       def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
           return (
               EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'						)
               if is_vision_available()
               else None
           )
       @slow
       def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
           snake_case__									=     TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'						)
           snake_case__									=     self.default_image_processor
           snake_case__									=     prepare_img()
           snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='tf'						)
           # forward pass
           snake_case__									=     model(**UpperCamelCase       ,	training=UpperCamelCase						)
           # verify the logits
           snake_case__									=     tf.TensorShape((1, 10_00)						)
           self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)
           snake_case__									=     tf.constant([-0.0_555, 0.4_825, -0.0_852]						)
           self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)


       @slow
       def lowerCAmelCase_ (     self:      Tuple						)		->		List[Any]:
           snake_case__									=     TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
               'snap-research/efficientformer-l1-300'						)
           snake_case__									=     self.default_image_processor
           snake_case__									=     prepare_img()
           snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='tf'						)
           # forward pass
           snake_case__									=     model(**UpperCamelCase       ,	training=UpperCamelCase						)
           # verify the logits
           snake_case__									=     tf.TensorShape((1, 10_00)						)
           self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)
           snake_case__									=     tf.constant([-0.1_312, 0.4_353, -1.0_499]						)
           self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)












from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__UpperCamelCase					:		Optional[int]	  =						8
def      a_ (      _A      ,   _A=BITS  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x * 255).int().clamp(0      ,   255  )

				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A  )
				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b c h w -> b c 1 h w'  )

				snake_case__									=     ((x & mask) != 0).float()
				snake_case__									=     rearrange(_A      ,   'b c d h w -> b (c d) h w'  )
				snake_case__									=     bits * 2 - 1
				return bits
def      a_ (      _A      ,   _A=BITS  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x > 0).int()
				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A      ,   dtype=torch.intaa  )

				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b (c d) h w -> b c d h w'      ,   d=8  )
				snake_case__									=     reduce(x * mask      ,   'b c d h w -> b c h w'      ,   'sum'  )
				return (dec / 255).clamp(0.0      ,   1.0  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A = 0.0      ,   _A = True      ,   _A=None      ,   _A = True      ,   )			->			Union[DDIMSchedulerOutput, Tuple]:







				"""simple docstring"""
				if self.num_inference_steps is None:
								raise ValueError(
								    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				snake_case__									=     timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[timestep]
				snake_case__									=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				snake_case__									=     1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				snake_case__									=     self._get_variance(_A      ,   _A  )
				snake_case__									=     eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								snake_case__									=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								snake_case__									=     model_output.device if torch.is_tensor(_A  ) else 'cpu'
								snake_case__									=     torch.randn(model_output.shape      ,   dtype=model_output.dtype      ,   generator=_A  ).to(_A  )
								snake_case__									=     self._get_variance(_A      ,   _A  ) ** 0.5 * eta * noise

								snake_case__									=     prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A="epsilon"      ,   _A=None      ,   _A = True      ,   )			->			Union[DDPMSchedulerOutput, Tuple]:







				"""simple docstring"""
				snake_case__									=     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								snake_case__							,						snake_case__									=     torch.split(_A      ,   sample.shape[1]      ,   dim=1  )
				else:
								snake_case__									=     None

				# 1. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[t]
				snake_case__									=     self.alphas_cumprod[t - 1] if t > 0 else self.one
				snake_case__									=     1 - alpha_prod_t
				snake_case__									=     1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								snake_case__									=     model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''  )

				# 3. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				snake_case__									=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				snake_case__									=     self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				snake_case__									=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				snake_case__									=     0
				if t > 0:
								snake_case__									=     torch.randn(
								    model_output.size()      ,   dtype=model_output.dtype      ,   layout=model_output.layout      ,   generator=_A  ).to(model_output.device  )
								snake_case__									=     (self._get_variance(_A      ,   predicted_variance=_A  ) ** 0.5) * noise

				snake_case__									=     pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDConditionModel       ,	UpperCamelCase:      Union[DDIMScheduler, DDPMScheduler]       ,	UpperCamelCase:      Optional[float] = 1.0       ,	)		->		Union[str, Any]:
											super().__init__()
											snake_case__									=     bit_scale
											snake_case__									=     (
											    ddim_bit_scheduler_step if isinstance(UpperCamelCase       ,	UpperCamelCase						) else ddpm_bit_scheduler_step
											)

											self.register_modules(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)


							@torch.no_grad()
							def __call__(     self:      Dict       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 50       ,	UpperCamelCase:      Optional[torch.Generator] = None       ,	UpperCamelCase:      Optional[int] = 1       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[Tuple, ImagePipelineOutput]:
											snake_case__									=     torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)       ,	generator=UpperCamelCase       ,	)
											snake_case__									=     decimal_to_bits(UpperCamelCase						) * self.bit_scale
											snake_case__									=     latents.to(self.device						)

											self.scheduler.set_timesteps(UpperCamelCase						)

											for t in self.progress_bar(self.scheduler.timesteps						):
															# predict the noise residual
															snake_case__									=     self.unet(UpperCamelCase       ,	UpperCamelCase						).sample

															# compute the previous noisy sample x_t -> x_t-1
															snake_case__									=     self.scheduler.step(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						).prev_sample

											snake_case__									=     bits_to_decimal(UpperCamelCase						)

											if output_type == "pil":
															snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=UpperCamelCase						)




