



'''simple docstring'''





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
  lowercase_									=    "platform"
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			PegasusConfig
 __lowerCamelCase       :			Optional[int]		=			{}
 __lowerCamelCase       :			Any		=			'gelu'



 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=False					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A=0.1					,  A=0.1					,  A=20					,  A=2					,  A=1					,  A=0					,  )       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size

      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    eos_token_id
      _a           =    pad_token_id
      _a           =    bos_token_id



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size			).clip(3					,  self.vocab_size			)
      _a           =    np.expand_dims(np.array([self.eos_token_id] * self.batch_size			)					,  1			)
      _a           =    np.concatenate([input_ids, eos_tensor]					,  axis=1			)

      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    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					,  **self.config_updates					,  )
      _a           =    prepare_pegasus_inputs_dict(A					,  A					,  A			)
      return config, inputs_dict



 def    a__    (self					,  A					,  A					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    20
      _a           =    model_class_name(A			)

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

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

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

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

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

      _a           =    model.decode(A					,  A			)

      _a           =    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    a__    (self					,  A					,  A					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    20
      _a           =    model_class_name(A			)

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

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

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

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

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

      _a           =    model.decode(A					,  A					,  decoder_attention_mask=A			)

      _a           =    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 lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=None    ,      __A=None    ,      ):



     """simple docstring"""



     if attention_mask is None:
          _a           =    np.not_equal(__A    ,      config.pad_token_id).astype(np.inta)
     if decoder_attention_mask is None:
          _a           =    np.concatenate(
              [
                  np.ones(decoder_input_ids[:, :1].shape    ,      dtype=np.inta),
                  np.not_equal(decoder_input_ids[:, 1:]    ,      config.pad_token_id).astype(np.inta),
              ]    ,      axis=-1    ,      )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }






@require_flax
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 __lowerCamelCase       :			Optional[int]		=			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 __lowerCamelCase       :			Tuple		=			True
 __lowerCamelCase       :			Optional[Any]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			str		=			False



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    FlaxPegasusModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(A					,  A					,  A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(A					,  A					,  A			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__			):
                _a           =    self._prepare_for_class(A					,  A			)
                _a           =    model_class(A			)

                @jax.jit
                def encode_jitted(A					,  A=None					,  **A			):
                     return model.encode(input_ids=A					,  attention_mask=A			)

                with self.subTest('''JIT Enabled'''			):
                     _a           =    encode_jitted(**A			).to_tuple()

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

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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

                _a           =    {
                    '''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(A					,  A					,  A			):
                     return model.decode(
                         decoder_input_ids=A					,  decoder_attention_mask=A					,  encoder_outputs=A					,  )

                with self.subTest('''JIT Enabled'''			):
                     _a           =    decode_jitted(**A			).to_tuple()

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

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



 @slow
 def    a__    (self			)       ->	str:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a           =    model_class_name.from_pretrained('''google/pegasus-large'''					,  from_pt=A			)
           _a           =    np.ones((1, 1)			)
           _a           =    model(A			)
           self.assertIsNotNone(A			)



 @slow
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''			)
      _a           =    PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''			)

      _a           =    [
          ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
          ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
      ]

      _a           =    [
          '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
          '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
      ]

      _a           =    tokenizer(A					,  return_tensors='''np'''					,  truncation=A					,  max_length=512					,  padding=A			)
      _a           =    model.generate(**A					,  num_beams=2			).sequences
      _a           =    tokenizer.batch_decode(A					,  skip_special_tokens=A			)
      assert tgt_text == decoded








'''simple docstring'''





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     if isinstance(__A    ,      torch.Tensor):
          return image
     elif isinstance(__A    ,      PIL.Image.Image):
          _a           =    [image]

     if isinstance(image[0]    ,      PIL.Image.Image):
          _a           =    [np.array(i.resize((w, h)    ,      resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
          _a           =    np.concatenate(__A    ,      axis=0)
          _a           =    np.array(__A).astype(np.floataa) / 2_55.0
          _a           =    image.transpose(0    ,      3    ,      1    ,      2)
          _a           =    2.0 * image - 1.0
          _a           =    torch.from_numpy(__A)
     elif isinstance(image[0]    ,      torch.Tensor):
          _a           =    torch.cat(__A    ,      dim=0)
     return image




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=0.99_95):



     """simple docstring"""



     if not isinstance(__A    ,      np.ndarray):
          _a           =    True
          _a           =    va.device
          _a           =    va.cpu().numpy()
          _a           =    va.cpu().numpy()

     _a           =    np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
     if np.abs(__A) > DOT_THRESHOLD:
          _a           =    (1 - t) * va + t * va
     else:
          _a           =    np.arccos(__A)
          _a           =    np.sin(__A)
          _a           =    theta_a * t
          _a           =    np.sin(__A)
          _a           =    np.sin(theta_a - theta_t) / sin_theta_a
          _a           =    sin_theta_t / sin_theta_a
          _a           =    sa * va + sa * va

     if inputs_are_torch:
          _a           =    torch.from_numpy(__A).to(__A)

     return va




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    F.normalize(__A    ,      dim=-1)
     _a           =    F.normalize(__A    ,      dim=-1)
     return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     for param in model.parameters():
          _a           =    value






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A=None					,  A=None					,  A=None					,  )       ->	str:


      """simple docstring"""



      super().__init__()
      self.register_modules(
          vae=A					,  text_encoder=A					,  clip_model=A					,  tokenizer=A					,  unet=A					,  scheduler=A					,  feature_extractor=A					,  coca_model=A					,  coca_tokenizer=A					,  coca_transform=A					,  )
      _a           =    (
          feature_extractor.size
          if isinstance(feature_extractor.size					,  A			)
          else feature_extractor.size['''shortest_edge''']
      )
      _a           =    transforms.Normalize(mean=feature_extractor.image_mean					,  std=feature_extractor.image_std			)
      set_requires_grad(self.text_encoder					,  A			)
      set_requires_grad(self.clip_model					,  A			)



 def    a__    (self					,  A = "auto"			)       ->	Union[str, Any]:


      """simple docstring"""



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



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      self.enable_attention_slicing(A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self					,  A					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    min(int(num_inference_steps * strength			)					,  A			)

      _a           =    max(num_inference_steps - init_timestep					,  0			)
      _a           =    self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A=None			)       ->	List[str]:


      """simple docstring"""



      if not isinstance(A					,  torch.Tensor			):
           raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A			)}'''			)

      _a           =    image.to(device=A					,  dtype=A			)

      if isinstance(A					,  A			):
           _a           =    [
               self.vae.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(A			)
           ]
           _a           =    torch.cat(A					,  dim=0			)
      else:
           _a           =    self.vae.encode(A			).latent_dist.sample(A			)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    0.18215 * init_latents
      _a           =    init_latents.repeat_interleave(A					,  dim=0			)

      _a           =    randn_tensor(init_latents.shape					,  generator=A					,  device=A					,  dtype=A			)

      # get latents
      _a           =    self.scheduler.add_noise(A					,  A					,  A			)
      _a           =    init_latents

      return latents



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.coca_transform(A			).unsqueeze(0			)
      with torch.no_grad(), torch.cuda.amp.autocast():
           _a           =    self.coca_model.generate(transformed_image.to(device=self.device					,  dtype=self.coca_model.dtype			)			)
      _a           =    self.coca_tokenizer.decode(generated[0].cpu().numpy()			)
      return generated.split('''<end_of_text>'''			)[0].replace('''<start_of_text>'''					,  ''''''			).rstrip(''' .,'''			)



 def    a__    (self					,  A					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.feature_extractor.preprocess(A			)
      _a           =    torch.from_numpy(clip_image_input['''pixel_values'''][0]			).unsqueeze(0			).to(self.device			).half()
      _a           =    self.clip_model.get_image_features(A			)
      _a           =    image_embeddings_clip / image_embeddings_clip.norm(p=2					,  dim=-1					,  keepdim=A			)
      _a           =    image_embeddings_clip.repeat_interleave(A					,  dim=0			)
      return image_embeddings_clip



 @torch.enable_grad()
 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    latents.detach().requires_grad_()

      _a           =    self.scheduler.scale_model_input(A					,  A			)

      # predict the noise residual
      _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

      if isinstance(self.scheduler					,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)			):
           _a           =    self.scheduler.alphas_cumprod[timestep]
           _a           =    1 - alpha_prod_t
           # compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _a           =    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

           _a           =    torch.sqrt(A			)
           _a           =    pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler					,  A			):
           _a           =    self.scheduler.sigmas[index]
           _a           =    latents - sigma * noise_pred
      else:
           raise ValueError(f'''scheduler type {type(self.scheduler			)} not supported'''			)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * sample
      _a           =    self.vae.decode(A			).sample
      _a           =    (image / 2 + 0.5).clamp(0					,  1			)

      _a           =    transforms.Resize(self.feature_extractor_size			)(A			)
      _a           =    self.normalize(A			).to(latents.dtype			)

      _a           =    self.clip_model.get_image_features(A			)
      _a           =    image_embeddings_clip / image_embeddings_clip.norm(p=2					,  dim=-1					,  keepdim=A			)

      _a           =    spherical_dist_loss(A					,  A			).mean() * clip_guidance_scale

      _a           =    -torch.autograd.grad(A					,  A			)[0]

      if isinstance(self.scheduler					,  A			):
           _a           =    latents.detach() + grads * (sigma**2)
           _a           =    noise_pred_original
      else:
           _a           =    noise_pred_original - torch.sqrt(A			) * grads
      return noise_pred, latents



 @torch.no_grad()
 def __call__(self					,  A					,  A					,  A = None					,  A = None					,  A = 512					,  A = 512					,  A = 0.6					,  A = 50					,  A = 7.5					,  A = 1					,  A = 0.0					,  A = 100					,  A = None					,  A = "pil"					,  A = True					,  A = 0.8					,  A = 0.1					,  A = 0.1					,  )       ->	str:


      """simple docstring"""



      if isinstance(A					,  A			) and len(A			) != batch_size:
           raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A			)} generators.'''			)

      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 isinstance(A					,  torch.Generator			) and batch_size > 1:
           _a           =    [generator] + [None] * (batch_size - 1)

      _a           =    [
          ('''model''', self.coca_model is None),
          ('''tokenizer''', self.coca_tokenizer is None),
          ('''transform''', self.coca_transform is None),
      ]
      _a           =    [x[0] for x in coca_is_none if x[1]]
      _a           =    ''', '''.join(A			)
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)
      if style_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)

      # get prompt text embeddings for content and style
      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(content_text_input.input_ids.to(self.device			)			)[0]

      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(style_text_input.input_ids.to(self.device			)			)[0]

      _a           =    slerp(A					,  A					,  A			)

      # duplicate text embeddings for each generation per prompt
      _a           =    text_embeddings.repeat_interleave(A					,  dim=0			)

      # set timesteps
      _a           =    '''offset''' in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
      _a           =    {}
      if accepts_offset:
           _a           =    1

      self.scheduler.set_timesteps(A					,  **A			)
      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      self.scheduler.timesteps.to(self.device			)

      _a    ,       _a           =    self.get_timesteps(A					,  A					,  self.device			)
      _a           =    timesteps[:1].repeat(A			)

      # Preprocess image
      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    slerp(A					,  A					,  A			)

      if clip_guidance_scale > 0:
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    slerp(
               A					,  A					,  A			)

      # 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           =    guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _a           =    content_text_input.input_ids.shape[-1]
           _a           =    self.tokenizer(['''''']					,  padding='''max_length'''					,  max_length=A					,  return_tensors='''pt'''			)
           _a           =    self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
           # duplicate unconditional embeddings for each generation per prompt
           _a           =    uncond_embeddings.repeat_interleave(A					,  dim=0			)

           # 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           =    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           =    (batch_size, self.unet.config.in_channels, height // 8, width // 8)
      _a           =    text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _a           =    torch.randn(A					,  generator=A					,  device='''cpu'''					,  dtype=A			).to(
                    self.device			)
           else:
                _a           =    torch.randn(A					,  generator=A					,  device=self.device					,  dtype=A			)
      else:
           if latents.shape != latents_shape:
                raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
           _a           =    latents.to(self.device			)

      # scale the initial noise by the standard deviation required by the scheduler
      _a           =    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           =    '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      _a           =    {}
      if accepts_eta:
           _a           =    eta

      # check if the scheduler accepts generator
      _a           =    '''generator''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      if accepts_generator:
           _a           =    generator

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

                # predict the noise residual
                _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

                # perform classifier free guidance
                if do_classifier_free_guidance:
                     _a    ,       _a           =    noise_pred.chunk(2			)
                     _a           =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # perform clip guidance
                if clip_guidance_scale > 0:
                     _a           =    (
                         text_embeddings.chunk(2			)[1] if do_classifier_free_guidance else text_embeddings
                     )
                     _a    ,       _a           =    self.cond_fn(
                         A					,  A					,  A					,  A					,  A					,  A					,  A					,  )

                # compute the previous noisy sample x_t -> x_t-1
                _a           =    self.scheduler.step(A					,  A					,  A					,  **A			).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * latents
      _a           =    self.vae.decode(A			).sample

      _a           =    (image / 2 + 0.5).clamp(0					,  1			)
      _a           =    image.cpu().permute(0					,  2					,  3					,  1			).numpy()

      if output_type == "pil":
           _a           =    self.numpy_to_pil(A			)

      if not return_dict:
           return (image, None)

      return StableDiffusionPipelineOutput(images=A					,  nsfw_content_detected=A			)



