


'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
      _UpperCamelCase          =     """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				):




     '''simple docstring'''





     if attention_mask is None:
          __A  :					List[str]						     =       np.where(input_ids != config.pad_token_id					,				1					,				0       )
     if decoder_attention_mask is None:
          __A  :					List[str]						     =       np.where(decoder_input_ids != config.pad_token_id					,				1					,				0       )
     if head_mask is None:
          __A  :					Union[str, Any]						     =       np.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          __A  :					Optional[int]						     =       np.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          __A  :					int						     =       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        __magic_name__  :

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase=13 ,    lowerCamelCase=7 ,    lowerCamelCase=True ,    lowerCamelCase=False ,    lowerCamelCase=99 ,    lowerCamelCase=16 ,    lowerCamelCase=2 ,    lowerCamelCase=4 ,    lowerCamelCase=4 ,    lowerCamelCase="gelu" ,    lowerCamelCase=0.1 ,    lowerCamelCase=0.1 ,    lowerCamelCase=32 ,    lowerCamelCase=2 ,    lowerCamelCase=1 ,    lowerCamelCase=0 ,    lowerCamelCase=0.02 ,    ):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       parent
          __A  :					int						     =       batch_size
          __A  :					Optional[Any]						     =       seq_length
          __A  :					Union[str, Any]						     =       is_training
          __A  :					Tuple						     =       use_labels
          __A  :					List[Any]						     =       vocab_size
          __A  :					Optional[Any]						     =       hidden_size
          __A  :					List[Any]						     =       num_hidden_layers
          __A  :					Optional[int]						     =       num_attention_heads
          __A  :					Optional[Any]						     =       intermediate_size
          __A  :					List[Any]						     =       hidden_act
          __A  :					List[Any]						     =       hidden_dropout_prob
          __A  :					Optional[int]						     =       attention_probs_dropout_prob
          __A  :					Dict						     =       max_position_embeddings
          __A  :					Dict						     =       eos_token_id
          __A  :					Optional[Any]						     =       pad_token_id
          __A  :					List[Any]						     =       bos_token_id
          __A  :					List[Any]						     =       initializer_range


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

          __A  :					Any						     =       shift_tokens_right(lowerCamelCase ,    1 ,    2		)

          __A  :					Optional[Any]						     =       MarianConfig(
              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=lowerCamelCase ,    )
          __A  :					Dict						     =       prepare_marian_inputs_dict(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          return config, inputs_dict


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A     ,__A  :					List[Any]						     =       self.prepare_config_and_inputs()
          return config, inputs_dict


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       20
          __A  :					List[str]						     =       model_class_name(lowerCamelCase		)

          __A  :					str						     =       model.encode(inputs_dict["input_ids"]		)

          __A     ,__A  :					Dict						     =       (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          __A  :					Dict						     =       model.init_cache(decoder_input_ids.shape[0] ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,    dtype="i4"		)

          __A  :					Tuple						     =       jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,    )
          __A  :					int						     =       model.decode(
              decoder_input_ids[:, :-1] ,    lowerCamelCase ,    decoder_attention_mask=lowerCamelCase ,    past_key_values=lowerCamelCase ,    decoder_position_ids=lowerCamelCase ,    )

          __A  :					int						     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,    dtype="i4"		)
          __A  :					Any						     =       model.decode(
              decoder_input_ids[:, -1:] ,    lowerCamelCase ,    decoder_attention_mask=lowerCamelCase ,    past_key_values=outputs_cache.past_key_values ,    decoder_position_ids=lowerCamelCase ,    )

          __A  :					str						     =       model.decode(lowerCamelCase ,    lowerCamelCase		)

          __A  :					List[str]						     =       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__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       20
          __A  :					List[Any]						     =       model_class_name(lowerCamelCase		)

          __A  :					int						     =       model.encode(inputs_dict["input_ids"]		)

          __A     ,__A  :					Optional[int]						     =       (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          __A  :					Optional[int]						     =       jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
              ] ,    axis=-1 ,    )

          __A  :					Any						     =       model.init_cache(decoder_input_ids.shape[0] ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					str						     =       jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,    )

          __A  :					int						     =       model.decode(
              decoder_input_ids[:, :-1] ,    lowerCamelCase ,    decoder_attention_mask=lowerCamelCase ,    past_key_values=lowerCamelCase ,    decoder_position_ids=lowerCamelCase ,    )
          __A  :					Optional[int]						     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,    dtype="i4"		)
          __A  :					List[str]						     =       model.decode(
              decoder_input_ids[:, -1:] ,    lowerCamelCase ,    past_key_values=outputs_cache.past_key_values ,    decoder_attention_mask=lowerCamelCase ,    decoder_position_ids=lowerCamelCase ,    )

          __A  :					Optional[int]						     =       model.decode(lowerCamelCase ,    lowerCamelCase ,    decoder_attention_mask=lowerCamelCase		)

          __A  :					str						     =       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        __magic_name__  (					lowerCAmelCase						,		unittest.TestCase						,		lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							True
     lowerCamelCase__						=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     lowerCamelCase__						=							(FlaxMarianMTModel,) if is_flax_available() else ()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       FlaxMarianModelTester(self		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A     ,__A  :					str						     =       self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A     ,__A  :					List[str]						     =       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(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A     ,__A  :					List[str]						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                    __A  :					Optional[int]						     =       self._prepare_for_class(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Union[str, Any]						     =       model_class(lowerCamelCase		)

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

                    with self.subTest("JIT Enabled"		):
                         __A  :					List[str]						     =       encode_jitted(**lowerCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              __A  :					int						     =       encode_jitted(**lowerCamelCase		).to_tuple()

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A     ,__A  :					Union[str, Any]						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                    __A  :					Tuple						     =       model_class(lowerCamelCase		)
                    __A  :					List[Any]						     =       model.encode(inputs_dict["input_ids"] ,    inputs_dict["attention_mask"]		)

                    __A  :					Dict						     =       {
                        "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(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):
                         return model.decode(
                             decoder_input_ids=lowerCamelCase ,    decoder_attention_mask=lowerCamelCase ,    encoder_outputs=lowerCamelCase ,    )

                    with self.subTest("JIT Enabled"		):
                         __A  :					Union[str, Any]						     =       decode_jitted(**lowerCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              __A  :					Any						     =       decode_jitted(**lowerCamelCase		).to_tuple()

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


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for model_class_name in self.all_model_classes:
               __A  :					str						     =       model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"		)
               # FlaxMarianForSequenceClassification expects eos token in input_ids
               __A  :					Tuple						     =       np.ones((1, 1)		) * model.config.eos_token_id
               __A  :					Union[str, Any]						     =       model(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          __A  :					int						     =       f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls


     @cached_property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return MarianTokenizer.from_pretrained(self.model_name		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self.tokenizer.eos_token_id


     @cached_property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					FlaxMarianMTModel						     =       FlaxMarianMTModel.from_pretrained(self.model_name		)
          self.assertEqual(model.config.decoder_start_token_id ,    model.config.pad_token_id		)
          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.translate_src_text(**lowerCamelCase		)
          self.assertListEqual(self.expected_text ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Dict						     =       self.tokenizer(self.src_text ,    padding=lowerCamelCase ,    return_tensors="np" ,    **lowerCamelCase		)
          __A  :					int						     =       self.model.generate(
              model_inputs.input_ids ,    attention_mask=model_inputs.attention_mask ,    num_beams=2 ,    max_length=128 ,    ).sequences
          __A  :					Optional[int]						     =       self.tokenizer.batch_decode(lowerCamelCase ,    skip_special_tokens=lowerCamelCase		)
          return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							'fr'
     lowerCamelCase__						=							[
         'I am a small frog.',
         'Now I can forget the 100 words of german that I know.',
     ]
     lowerCamelCase__						=							[
         'Je suis une petite grenouille.',
         'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
     ]


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'fr'
     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							[
         'Donnez moi le micro.',
         'Tom et Mary étaient assis à une table.',  # Accents
     ]
     lowerCamelCase__						=							[
         'Give me the microphone.',
         'Tom and Mary were sitting at a table.',
     ]


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'mt'
     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     lowerCamelCase__						=							['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							'de'
     lowerCamelCase__						=							[
         'I am a small frog.',
         'Now I can forget the 100 words of german that I know.',
         'Tom asked his teacher for advice.',
         'That\'s how I would do it.',
         'Tom really admired Mary\'s courage.',
         'Turn around and close your eyes.',
     ]
     lowerCamelCase__						=							[
         'Ich bin ein kleiner Frosch.',
         'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
         'Tom bat seinen Lehrer um Rat.',
         'So würde ich das machen.',
         'Tom bewunderte Marias Mut wirklich.',
         'Drehen Sie sich um und schließen Sie die Augen.',
     ]


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							'zh'
     lowerCamelCase__						=							['My name is Wolfgang and I live in Berlin']
     lowerCamelCase__						=							['我叫沃尔夫冈 我住在柏林']


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'ru'
     lowerCamelCase__						=							'fr'
     lowerCamelCase__						=							['Он показал мне рукопись своей новой пьесы.']
     lowerCamelCase__						=							['Il m\'a montré le manuscrit de sa nouvelle pièce.']


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'en'
     lowerCamelCase__						=							'ROMANCE'
     lowerCamelCase__						=							[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     lowerCamelCase__						=							[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self._assert_generated_batch_equal_expected()





'''simple docstring'''


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

          self.register_modules(vae=lowerCamelCase ,    text_encoder=lowerCamelCase ,    tokenizer=lowerCamelCase ,    unet=lowerCamelCase ,    scheduler=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       (1 / 0.1_8215) * latent
          with torch.no_grad():
               __A  :					List[Any]						     =       self.vae.decode(lowerCamelCase		).sample
          __A  :					int						     =       (img / 2 + 0.5).clamp(0 ,    1		)
          __A  :					List[str]						     =       img.detach().cpu().permute(0 ,    2 ,    3 ,    1		).numpy()
          __A  :					List[str]						     =       (img * 255).round().astype("uint8"		)
          return Image.fromarray(img[0]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





          __A  :					Optional[int]						     =       steps - int(kmin * steps		)
          __A  :					Union[str, Any]						     =       steps - int(kmax * steps		)

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

          torch.manual_seed(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8) ,    ).to(self.device		)

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

          with torch.no_grad():
               __A  :					Union[str, Any]						     =       self.unet(
                   lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    encoder_hidden_states=lowerCamelCase ,    ).sample

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
               if i > tmax:
                    if i < tmin:  # layout generation phase
                         __A  :					List[Any]						     =       self.scheduler.add_noise(
                             lowerCamelCase ,    lowerCamelCase ,    timesteps=lowerCamelCase ,    )

                         __A  :					Tuple						     =       (mix_factor * latents) + (
                             1 - mix_factor
                         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                         __A  :					Any						     =       torch.cat([input] * 2		)

                    else:  # content generation phase
                         __A  :					Any						     =       torch.cat([latents] * 2		)

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

                    __A     ,__A  :					str						     =       pred.chunk(2		)
                    __A  :					Optional[Any]						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

