

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch






class 							A_				:


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Dict=1_4    ,						UpperCAmelCase    :	str=7    ,						UpperCAmelCase    :	Tuple=True    ,						UpperCAmelCase    :	Dict=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Tuple=True    ,						UpperCAmelCase    :	Dict=9_9    ,						UpperCAmelCase    :	List[Any]=3_2    ,						UpperCAmelCase    :	Tuple=5    ,						UpperCAmelCase    :	int=4    ,						UpperCAmelCase    :	Optional[int]=3_7    ,						UpperCAmelCase    :	str="gelu"    ,						UpperCAmelCase    :	List[str]=0.1    ,						UpperCAmelCase    :	Any=0.1    ,						UpperCAmelCase    :	List[str]=5_1_2    ,						UpperCAmelCase    :	Any=0.02    ,						)							->			Tuple:
      __lowerCAmelCase:     Union[str, Any]     			=   parent
      __lowerCAmelCase:     Optional[int]     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     Optional[Any]     			=   use_input_mask
      __lowerCAmelCase:     Tuple     			=   use_token_type_ids
      __lowerCAmelCase:     Optional[int]     			=   use_labels
      __lowerCAmelCase:     Optional[Any]     			=   vocab_size
      __lowerCAmelCase:     Dict     			=   hidden_size
      __lowerCAmelCase:     str     			=   num_hidden_layers
      __lowerCAmelCase:     Union[str, Any]     			=   num_attention_heads
      __lowerCAmelCase:     List[Any]     			=   intermediate_size
      __lowerCAmelCase:     Optional[int]     			=   hidden_act
      __lowerCAmelCase:     Optional[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     Union[str, Any]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Any     			=   max_position_embeddings
      __lowerCAmelCase:     Dict     			=   initializer_range
      __lowerCAmelCase:     Tuple     			=   None
      __lowerCAmelCase:     Union[str, Any]     			=   vocab_size - 1
      __lowerCAmelCase:     List[Any]     			=   vocab_size - 1
      __lowerCAmelCase:     str     			=   vocab_size - 1


     def 						UpperCAmelCase						(							self    :	Dict      )							->			List[Any]:
      __lowerCAmelCase:     Union[str, Any]     			=   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size      )

      __lowerCAmelCase:     Optional[Any]     			=   None
      if self.use_input_mask:
       __lowerCAmelCase:     Tuple     			=   random_attention_mask([self.batch_size, self.seq_length]      )

      __lowerCAmelCase:     Optional[int]     			=   GPTaConfig(
          vocab_size=self.vocab_size    ,						n_embd=self.hidden_size    ,						n_layer=self.num_hidden_layers    ,						n_head=self.num_attention_heads    ,						n_positions=self.max_position_embeddings    ,						use_cache=UpperCAmelCase    ,						bos_token_id=self.bos_token_id    ,						eos_token_id=self.eos_token_id    ,						pad_token_id=self.pad_token_id    ,						)

      return (config, input_ids, input_mask)


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Tuple:
      __lowerCAmelCase:     str     			=   self.prepare_config_and_inputs()
      __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     List[Any]     			=   config_and_inputs
      __lowerCAmelCase:     int     			=   {'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			Optional[int]:
      __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     List[str]     			=   self.prepare_config_and_inputs()

      __lowerCAmelCase:     int     			=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
      __lowerCAmelCase:     str     			=   ids_tensor([self.batch_size, self.seq_length]    ,						vocab_size=2      )

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Optional[Any]      )							->			Any:
      __lowerCAmelCase:     Dict     			=   2_0
      __lowerCAmelCase:     Tuple     			=   model_class_name(UpperCAmelCase      )

      __lowerCAmelCase:     Dict     			=   model.init_cache(input_ids.shape[0]    ,						UpperCAmelCase      )
      __lowerCAmelCase:     Optional[Any]     			=   jnp.ones((input_ids.shape[0], max_decoder_length)    ,						dtype='i4'      )

      __lowerCAmelCase:     Union[str, Any]     			=   jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )
      __lowerCAmelCase:     int     			=   model(
          input_ids[:, :-1]    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase    ,						position_ids=UpperCAmelCase    ,						)

      __lowerCAmelCase:     Tuple     			=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,						dtype='i4'      )
      __lowerCAmelCase:     Any     			=   model(
          input_ids[:, -1:]    ,						attention_mask=UpperCAmelCase    ,						past_key_values=outputs_cache.past_key_values    ,						position_ids=UpperCAmelCase    ,						)

      __lowerCAmelCase:     int     			=   model(UpperCAmelCase      )

      __lowerCAmelCase:     Optional[Any]     			=   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    :	List[str]    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	Any      )							->			Union[str, Any]:
      __lowerCAmelCase:     str     			=   2_0
      __lowerCAmelCase:     Any     			=   model_class_name(UpperCAmelCase      )

      __lowerCAmelCase:     List[Any]     			=   jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]    ,						axis=-1    ,						)

      __lowerCAmelCase:     List[Any]     			=   model.init_cache(input_ids.shape[0]    ,						UpperCAmelCase      )
      __lowerCAmelCase:     str     			=   jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )

      __lowerCAmelCase:     Union[str, Any]     			=   model(
          input_ids[:, :-1]    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase    ,						position_ids=UpperCAmelCase    ,						)
      __lowerCAmelCase:     Tuple     			=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,						dtype='i4'      )
      __lowerCAmelCase:     List[Any]     			=   model(
          input_ids[:, -1:]    ,						past_key_values=outputs_cache.past_key_values    ,						attention_mask=UpperCAmelCase    ,						position_ids=UpperCAmelCase    ,						)

      __lowerCAmelCase:     List[Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )

      __lowerCAmelCase:     Dict     			=   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 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  str          =			(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     _lowercase    :  Optional[Any]          =			(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[Any]:
      __lowerCAmelCase:     Dict     			=   FlaxGPTaModelTester(self      )


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Dict:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     int     			=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Dict:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     Union[str, Any]     			=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )


     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			Dict:
      __lowerCAmelCase:     Dict     			=   GPTaTokenizer.from_pretrained('gpt2'    ,						pad_token='</s>'    ,						padding_side='left'      )
      __lowerCAmelCase:     Dict     			=   tokenizer(['Hello this is a long string', 'Hey']    ,						return_tensors='np'    ,						padding=UpperCAmelCase    ,						truncation=UpperCAmelCase      )

      __lowerCAmelCase:     Dict     			=   FlaxGPTaLMHeadModel.from_pretrained('gpt2'      )
      __lowerCAmelCase:     int     			=   False
      __lowerCAmelCase:     Dict     			=   model.config.eos_token_id

      __lowerCAmelCase:     Tuple     			=   jax.jit(model.generate      )

      __lowerCAmelCase:     Tuple     			=   jit_generate(inputs['input_ids']    ,						attention_mask=inputs['attention_mask']      ).sequences

      __lowerCAmelCase:     int     			=   tokenizer.batch_decode(UpperCAmelCase    ,						skip_special_tokens=UpperCAmelCase      )

      __lowerCAmelCase:     str     			=   [
          'Hello this is a long string of words. I\'m going to start with the first one.\n',
          'Hey, I\'m not sure if I\'m going to be able to do',
      ]

      self.assertListEqual(UpperCAmelCase    ,						UpperCAmelCase      )


     @is_pt_flax_cross_test
     def 						UpperCAmelCase						(							self    :	Dict      )							->			Union[str, Any]:
      __lowerCAmelCase			,							__lowerCAmelCase:     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__      ):
        # prepare inputs
        __lowerCAmelCase:     Any     			=   self._prepare_for_class(UpperCAmelCase    ,						UpperCAmelCase      )
        __lowerCAmelCase:     List[str]     			=   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCAmelCase:     Optional[Any]     			=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCAmelCase:     Union[str, Any]     			=   getattr(UpperCAmelCase    ,						UpperCAmelCase      )

        __lowerCAmelCase			,							__lowerCAmelCase:     Union[str, Any]     			=   pt_inputs['input_ids'].shape
        __lowerCAmelCase:     Optional[int]     			=   np.random.randint(0    ,						seq_length - 1    ,						size=(batch_size,)      )
        for batch_idx, start_index in enumerate(UpperCAmelCase      ):
         __lowerCAmelCase:     Dict     			=   0
         __lowerCAmelCase:     List[Any]     			=   1
         __lowerCAmelCase:     int     			=   0
         __lowerCAmelCase:     List[Any]     			=   1
        __lowerCAmelCase:     List[Any]     			=   pt_model_class(UpperCAmelCase      ).eval()
        __lowerCAmelCase:     int     			=   model_class(UpperCAmelCase    ,						dtype=jnp.floataa      )

        __lowerCAmelCase:     Union[str, Any]     			=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,						UpperCAmelCase      )
        __lowerCAmelCase:     Any     			=   fx_state

        with torch.no_grad():
         __lowerCAmelCase:     List[str]     			=   pt_model(**UpperCAmelCase      ).to_tuple()

        __lowerCAmelCase:     Dict     			=   fx_model(**UpperCAmelCase      ).to_tuple()
        self.assertEqual(len(UpperCAmelCase      )    ,						len(UpperCAmelCase      )    ,						'Output lengths differ between Flax and PyTorch'      )
        for fx_output, pt_output in zip(UpperCAmelCase    ,						UpperCAmelCase      ):
         self.assert_almost_equals(fx_output[:, -1]    ,						pt_output[:, -1].numpy()    ,						4E-2      )

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(UpperCAmelCase      )
         __lowerCAmelCase:     List[Any]     			=   model_class.from_pretrained(UpperCAmelCase    ,						from_pt=UpperCAmelCase      )

        __lowerCAmelCase:     Optional[Any]     			=   fx_model_loaded(**UpperCAmelCase      ).to_tuple()
        self.assertEqual(
            len(UpperCAmelCase      )    ,						len(UpperCAmelCase      )    ,						'Output lengths differ between Flax and PyTorch'      )
        for fx_output_loaded, pt_output in zip(UpperCAmelCase    ,						UpperCAmelCase      ):
         self.assert_almost_equals(fx_output_loaded[:, -1]    ,						pt_output[:, -1].numpy()    ,						4E-2      )


     @is_pt_flax_cross_test
     def 						UpperCAmelCase						(							self    :	Dict      )							->			str:
      __lowerCAmelCase			,							__lowerCAmelCase:     Any     			=   self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__      ):
        # prepare inputs
        __lowerCAmelCase:     int     			=   self._prepare_for_class(UpperCAmelCase    ,						UpperCAmelCase      )
        __lowerCAmelCase:     Any     			=   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCAmelCase:     Any     			=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCAmelCase:     Dict     			=   getattr(UpperCAmelCase    ,						UpperCAmelCase      )

        __lowerCAmelCase:     Dict     			=   pt_model_class(UpperCAmelCase      ).eval()
        __lowerCAmelCase:     str     			=   model_class(UpperCAmelCase    ,						dtype=jnp.floataa      )

        __lowerCAmelCase:     Union[str, Any]     			=   load_flax_weights_in_pytorch_model(UpperCAmelCase    ,						fx_model.params      )
        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pt_inputs['input_ids'].shape
        __lowerCAmelCase:     Dict     			=   np.random.randint(0    ,						seq_length - 1    ,						size=(batch_size,)      )
        for batch_idx, start_index in enumerate(UpperCAmelCase      ):
         __lowerCAmelCase:     List[str]     			=   0
         __lowerCAmelCase:     Union[str, Any]     			=   1
         __lowerCAmelCase:     Tuple     			=   0
         __lowerCAmelCase:     Optional[int]     			=   1

        # make sure weights are tied in PyTorch
        pt_model.tie_weights()

        with torch.no_grad():
         __lowerCAmelCase:     Dict     			=   pt_model(**UpperCAmelCase      ).to_tuple()

        __lowerCAmelCase:     Union[str, Any]     			=   fx_model(**UpperCAmelCase      ).to_tuple()
        self.assertEqual(len(UpperCAmelCase      )    ,						len(UpperCAmelCase      )    ,						'Output lengths differ between Flax and PyTorch'      )
        for fx_output, pt_output in zip(UpperCAmelCase    ,						UpperCAmelCase      ):
         self.assert_almost_equals(fx_output[:, -1]    ,						pt_output[:, -1].numpy()    ,						4E-2      )

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(UpperCAmelCase      )
         __lowerCAmelCase:     Tuple     			=   pt_model_class.from_pretrained(UpperCAmelCase    ,						from_flax=UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     List[str]     			=   pt_model_loaded(**UpperCAmelCase      ).to_tuple()

        self.assertEqual(
            len(UpperCAmelCase      )    ,						len(UpperCAmelCase      )    ,						'Output lengths differ between Flax and PyTorch'      )
        for fx_output, pt_output in zip(UpperCAmelCase    ,						UpperCAmelCase      ):
         self.assert_almost_equals(fx_output[:, -1]    ,						pt_output[:, -1].numpy()    ,						4E-2      )


     @slow
     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Optional[int]:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase:     int     			=   model_class_name.from_pretrained('gpt2'    ,						from_pt=UpperCAmelCase      )
       __lowerCAmelCase:     Optional[Any]     			=   model(np.ones((1, 1)      )      )
       self.assertIsNotNone(UpperCAmelCase      )








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 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

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


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

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

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

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

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

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

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

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

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





