


'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 lowerCamelCase							:   Union[str, Any]  						=	'platform'

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )

def _SCREAMING_SNAKE_CASE      (A  , A  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , )       ->       Union[str, Any]:






       """simple docstring"""







       if attention_mask is None:
              lowercase__      			= np.where(input_ids != config.pad_token_id  , 1  , 0       )
       if decoder_attention_mask is None:
              lowercase__      			= np.where(decoder_input_ids != config.pad_token_id  , 1  , 0       )
       if head_mask is None:
              lowercase__      			= np.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              lowercase__      			= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              lowercase__      			= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    List[str]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Any=13   ,						UpperCamelCase    :    Optional[int]=7   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    Union[str, Any]=99   ,						UpperCamelCase    :    List[Any]=16   ,						UpperCamelCase    :    List[Any]=2   ,						UpperCamelCase    :    List[str]=4   ,						UpperCamelCase    :    Tuple=4   ,						UpperCamelCase    :    Dict="gelu"   ,						UpperCamelCase    :    Tuple=0.1   ,						UpperCamelCase    :    List[Any]=0.1   ,						UpperCamelCase    :    Any=32   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=1   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    List[str]=0.02   ,						):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







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

             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						1   ,						2 )

             lowercase__      			= BlenderbotConfig(
                 vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						encoder_layers=self.num_hidden_layers   ,						decoder_layers=self.num_hidden_layers   ,						encoder_attention_heads=self.num_attention_heads   ,						decoder_attention_heads=self.num_attention_heads   ,						encoder_ffn_dim=self.intermediate_size   ,						decoder_ffn_dim=self.intermediate_size   ,						dropout=self.hidden_dropout_prob   ,						attention_dropout=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						pad_token_id=self.pad_token_id   ,						initializer_range=self.initializer_range   ,						use_cache=UpperCamelCase   ,						)
             lowercase__      			= prepare_blenderbot_inputs_dict(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
             return config, inputs_dict



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= 20
             lowercase__      			= model_class_name(UpperCamelCase )

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

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

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

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

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

             lowercase__      			= model.decode(UpperCamelCase   ,						UpperCamelCase )

             lowercase__      			= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
             self.parent.assertTrue(diff < 1E-3   ,						msg=f"Max diff is {diff}" )






      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= 20
             lowercase__      			= model_class_name(UpperCamelCase )

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

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

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

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

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

             lowercase__      			= model.decode(UpperCamelCase   ,						UpperCamelCase   ,						decoder_attention_mask=UpperCamelCase )

             lowercase__      			= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
             self.parent.assertTrue(diff < 1E-3   ,						msg=f"Max diff is {diff}" )






@require_flax
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							99



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= np.array(
                 [
                     [71, 82, 18, 33, 46, 91, 2],
                     [68, 34, 26, 58, 30, 82, 2],
                     [5, 97, 17, 39, 94, 40, 2],
                     [76, 83, 94, 25, 70, 78, 2],
                     [87, 59, 41, 35, 48, 66, 2],
                     [55, 13, 16, 58, 5, 2, 1],  # note padding
                     [64, 27, 31, 51, 12, 75, 2],
                     [52, 64, 86, 17, 83, 39, 2],
                     [48, 61, 9, 24, 71, 82, 2],
                     [26, 1, 60, 48, 22, 13, 2],
                     [21, 5, 62, 28, 14, 76, 2],
                     [45, 98, 37, 86, 59, 48, 2],
                     [70, 70, 50, 9, 28, 0, 2],
                 ]   ,						dtype=np.intaa   ,						)

             lowercase__      			= input_ids.shape[0]
             lowercase__      			= BlenderbotConfig(
                 vocab_size=self.vocab_size   ,						d_model=24   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=32   ,						decoder_ffn_dim=32   ,						max_position_embeddings=48   ,						eos_token_id=2   ,						pad_token_id=1   ,						bos_token_id=0   ,						)
             return config, input_ids, batch_size



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__			,lowercase__      			= self._get_config_and_data()
             lowercase__      			= FlaxBlenderbotForConditionalGeneration(UpperCamelCase )
             lowercase__      			= lm_model(input_ids=UpperCamelCase )
             lowercase__      			= (batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs['''logits'''].shape   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= BlenderbotConfig(
                 vocab_size=self.vocab_size   ,						d_model=14   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=8   ,						decoder_ffn_dim=8   ,						max_position_embeddings=48   ,						)
             lowercase__      			= FlaxBlenderbotForConditionalGeneration(UpperCamelCase )
             lowercase__      			= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,						dtype=np.intaa )
             lowercase__      			= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,						dtype=np.intaa )
             lowercase__      			= lm_model(input_ids=UpperCamelCase   ,						decoder_input_ids=UpperCamelCase )
             lowercase__      			= (*summary.shape, config.vocab_size)
             self.assertEqual(outputs['''logits'''].shape   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







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






@require_flax
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase  ,     lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							True
      lowerCAmelCase__					:     str  					=							(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      lowerCAmelCase__					:     List[Any]  					=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= FlaxBlenderbotModelTester(self )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__ ):
                           lowercase__      			= self._prepare_for_class(UpperCamelCase   ,						UpperCamelCase )
                           lowercase__      			= model_class(UpperCamelCase )

                           @jax.jit
                           def encode_jitted(UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    List[str]=None   ,						**UpperCamelCase    :    Optional[Any] ):
                                  return model.encode(input_ids=UpperCamelCase   ,						attention_mask=UpperCamelCase )

                           with self.subTest('''JIT Enabled''' ):
                                  lowercase__      			= encode_jitted(**UpperCamelCase ).to_tuple()

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

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



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

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

                           lowercase__      			= {
                               '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                               '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                               '''encoder_outputs''': encoder_outputs,
                           }

                           @jax.jit
                           def decode_jitted(UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Dict ):
                                  return model.decode(
                                      decoder_input_ids=UpperCamelCase   ,						decoder_attention_mask=UpperCamelCase   ,						encoder_outputs=UpperCamelCase   ,						)

                           with self.subTest('''JIT Enabled''' ):
                                  lowercase__      			= decode_jitted(**UpperCamelCase ).to_tuple()

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

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



      @slow
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             for model_class_name in self.all_model_classes:
                    lowercase__      			= model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
                    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                    lowercase__      			= np.ones((1, 1) ) * model.config.eos_token_id
                    lowercase__      			= model(UpperCamelCase )
                    self.assertIsNotNone(UpperCamelCase )






      @unittest.skipUnless(jax_device != '''cpu'''   ,						'''3B test too slow on CPU.''' )
      @slow
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
             lowercase__      			= {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

             lowercase__      			= FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''   ,						from_pt=UpperCamelCase )
             lowercase__      			= BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

             lowercase__      			= ['''Sam''']
             lowercase__      			= tokenizer(UpperCamelCase   ,						return_tensors='''jax''' )

             lowercase__      			= model.generate(**UpperCamelCase   ,						**UpperCamelCase )
             lowercase__      			= '''Sam is a great name. It means "sun" in Gaelic.'''

             lowercase__      			= tokenizer.batch_decode(UpperCamelCase   ,						**UpperCamelCase )
             assert generated_txt[0].strip() == tgt_text









"""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        _UpperCAmelCase       (			lowerCAmelCase__):

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

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     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		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

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

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

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

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

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # 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+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

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

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

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

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





