





from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TFSharedEmbeddings,
     )

     if _tf_gpu_memory_limit is not None:
          _UpperCamelCase       = tf.config.list_physical_devices('''GPU''')
          for gpu in gpus:
               # Restrict TensorFlow to only allocate x GB of memory on the GPUs
               try:
                    tf.config.set_logical_device_configuration(
                        gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                    )
                    _UpperCamelCase       = tf.config.list_logical_devices('''GPU''')
                    print('''Logical GPUs''', logical_gpus)
               except RuntimeError as e:
                    # Virtual devices must be set before GPUs have been initialized
                    print(e)




@require_tf
class 				_lowerCamelCase :



      """simple docstring"""






      UpperCAmelCase_						:      int       =None
      UpperCAmelCase_						:      Union[str, Any]       =()
      UpperCAmelCase_						:      List[str]       =()
      UpperCAmelCase_						:      Optional[Any]       =True
      UpperCAmelCase_						:      Optional[Any]       =True
      UpperCAmelCase_						:      Any       =True
      UpperCAmelCase_						:      int       =False







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=False   )       ->   dict:






        '''simple docstring'''






        __snake_case   :				Any        =       copy.deepcopy(UpperCAmelCase   )

        if model_class in get_values(UpperCAmelCase   ):
          __snake_case   :				Optional[int]        =       {
              k: tf.tile(tf.expand_dims(UpperCAmelCase				,						1   )				,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)   )
              if isinstance(UpperCAmelCase				,						tf.Tensor   ) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }

        if return_labels:
          if model_class in get_values(UpperCAmelCase   ):
            __snake_case   :				List[str]        =       tf.ones(self.model_tester.batch_size				,						dtype=tf.intaa   )
          elif model_class in get_values(UpperCAmelCase   ):
            __snake_case   :				List[Any]        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
            __snake_case   :				Union[str, Any]        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
          elif model_class in [
              *get_values(UpperCAmelCase   ),
              *get_values(UpperCAmelCase   ),
          ]:
            __snake_case   :				int        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
          elif model_class in get_values(UpperCAmelCase   ):
            __snake_case   :				Any        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
          elif model_class in [
              *get_values(UpperCAmelCase   ),
              *get_values(UpperCAmelCase   ),
              *get_values(UpperCAmelCase   ),
              *get_values(UpperCAmelCase   ),
              *get_values(UpperCAmelCase   ),
          ]:
            __snake_case   :				Optional[Any]        =       tf.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=tf.intaa   )
        return inputs_dict







      @slow
      def 						UpperCAmelCase		( self   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				List[str]        =       self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case   :				str        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				List[str]        =       model_class(UpperCAmelCase   )

          @tf.function
          def run_in_graph_mode():
            return model(UpperCAmelCase   )

          __snake_case   :				int        =       run_in_graph_mode()
          self.assertIsNotNone(UpperCAmelCase   )







      @slow
      def 						UpperCAmelCase		( self   )       ->   int:






        '''simple docstring'''






        __snake_case   :				List[str]        =       self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case   :				str        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				Union[str, Any]        =       model_class(UpperCAmelCase   )

          @tf.function(experimental_compile=UpperCAmelCase   )
          def run_in_graph_mode():
            return model(UpperCAmelCase   )

          __snake_case   :				int        =       run_in_graph_mode()
          self.assertIsNotNone(UpperCAmelCase   )







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






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case   :				List[Any]        =       model_class(UpperCAmelCase   )
          if getattr(UpperCAmelCase				,						"hf_compute_loss"				,						UpperCAmelCase   ):
            # Test that model correctly compute the loss with kwargs
            __snake_case   :				List[Any]        =       self._prepare_for_class(inputs_dict.copy()				,						UpperCAmelCase				,						return_labels=UpperCAmelCase   )
            # Is there a better way to remove these decoder inputs?
            __snake_case   :				int        =       {
                key: val
                for key, val in prepared_for_class.items()
                if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
            }

            __snake_case   :				Dict        =       {
                "labels",
                "label",
                "label_ids",
                "start_positions",
                "start_position",
                "end_positions",
                "end_position",
                "next_sentence_label",
            }
            __snake_case   :				str        =       possible_label_cols.intersection(set(UpperCAmelCase   )   )
            self.assertGreater(len(UpperCAmelCase   )				,						0				,						msg="No matching label names found!"   )
            __snake_case   :				Optional[Any]        =       {key: val for key, val in prepared_for_class.items() if key in label_names}
            __snake_case   :				int        =       {key: val for key, val in prepared_for_class.items() if key not in label_names}
            self.assertGreater(len(UpperCAmelCase   )				,						0   )

            # Make sure it works with XLA!
            model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )				,						jit_compile=UpperCAmelCase   )
            # Make sure the model fits without crashing regardless of where we pass the labels
            __snake_case   :				str        =       model.fit(
                UpperCAmelCase				,						validation_data=UpperCAmelCase				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=UpperCAmelCase				,						verbose=0				,						)
            __snake_case   :				List[str]        =       history.history["loss"][0]
            self.assertTrue(not isnan(UpperCAmelCase   )   )
            __snake_case   :				str        =       history.history["val_loss"][0]
            self.assertTrue(not isnan(UpperCAmelCase   )   )

            # Now test it with separate labels, to make sure that path works in XLA too.
            __snake_case   :				str        =       model_class(UpperCAmelCase   )
            model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )				,						jit_compile=UpperCAmelCase   )
            __snake_case   :				List[Any]        =       model.fit(
                UpperCAmelCase				,						UpperCAmelCase				,						validation_data=(inputs_minus_labels, labels)				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=UpperCAmelCase				,						verbose=0				,						)

            __snake_case   :				str        =       history.history["loss"][0]
            self.assertTrue(not isnan(UpperCAmelCase   )   )
            __snake_case   :				List[Any]        =       history.history["val_loss"][0]
            self.assertTrue(not isnan(UpperCAmelCase   )   )







      @slow
      def 						UpperCAmelCase		( self   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()
        __snake_case   :				Dict        =       True
        __snake_case   :				Any        =       True

        if hasattr(UpperCAmelCase				,						"use_cache"   ):
          __snake_case   :				Any        =       True

        __snake_case   :				int        =       getattr(self.model_tester				,						"encoder_seq_length"				,						self.model_tester.seq_length   )
        __snake_case   :				Tuple        =       getattr(self.model_tester				,						"key_length"				,						UpperCAmelCase   )

        for model_class in self.all_model_classes[:2]:
          __snake_case   :				Dict        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				int        =       model_class(UpperCAmelCase   )
          model.build()
          __snake_case   :				str        =       len(model(UpperCAmelCase   )   )

          for key in list(class_inputs_dict.keys()   ):
            # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
            if key not in model.input_signature:
              del class_inputs_dict[key]
            # Check it's a tensor, in case the inputs dict has some bools in it too
            elif isinstance(class_inputs_dict[key]				,						tf.Tensor   ) and class_inputs_dict[key].dtype.is_integer:
              __snake_case   :				List[str]        =       tf.cast(class_inputs_dict[key]				,						tf.intaa   )

          if set(class_inputs_dict.keys()   ) != set(model.input_signature.keys()   ):
            continue  # Some models have inputs that the preparation functions don't create, we skip those

          with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(UpperCAmelCase				,						saved_model=UpperCAmelCase   )
            __snake_case   :				str        =       os.path.join(UpperCAmelCase				,						"saved_model"				,						"1"   )
            __snake_case   :				Tuple        =       tf.keras.models.load_model(UpperCAmelCase   )
            __snake_case   :				Tuple        =       model(UpperCAmelCase   )

            if self.is_encoder_decoder:
              __snake_case   :				int        =       outputs["encoder_hidden_states"]
              __snake_case   :				str        =       outputs["encoder_attentions"]
            else:
              __snake_case   :				int        =       outputs["hidden_states"]
              __snake_case   :				Optional[Any]        =       outputs["attentions"]

            self.assertEqual(len(UpperCAmelCase   )				,						UpperCAmelCase   )

            __snake_case   :				int        =       getattr(
                self.model_tester				,						"expected_num_hidden_layers"				,						self.model_tester.num_hidden_layers + 1   )

            self.assertEqual(len(UpperCAmelCase   )				,						UpperCAmelCase   )
            self.assertListEqual(
                list(output_hidden_states[0].shape[-2:]   )				,						[self.model_tester.seq_length, self.model_tester.hidden_size]				,						)

            self.assertEqual(len(UpperCAmelCase   )				,						self.model_tester.num_hidden_layers   )
            self.assertListEqual(
                list(output_attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,						)







      @slow
      def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:






        '''simple docstring'''






        tf.keras.mixed_precision.set_global_policy("mixed_float16"   )

        # try/finally block to ensure subsequent tests run in float32
        try:
          __snake_case   :				Optional[int]        =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				List[str]        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
            __snake_case   :				Optional[Any]        =       model_class(UpperCAmelCase   )
            __snake_case   :				List[str]        =       model(UpperCAmelCase   )

            self.assertIsNotNone(UpperCAmelCase   )
        finally:
          tf.keras.mixed_precision.set_global_policy("float32"   )







      @slow
      def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:






        '''simple docstring'''






        __snake_case   :				Dict        =       self.model_tester.prepare_config_and_inputs_for_common()
        # head_mask and decoder_head_mask has different shapes than other input args
        if "head_mask" in inputs_dict:
          del inputs_dict["head_mask"]
        if "decoder_head_mask" in inputs_dict:
          del inputs_dict["decoder_head_mask"]
        if "cross_attn_head_mask" in inputs_dict:
          del inputs_dict["cross_attn_head_mask"]
        __snake_case   :				str        =       {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__   ),)
            for module_member_name in dir(UpperCAmelCase   )
            if module_member_name.endswith("MainLayer"   )
            for module_member in (getattr(UpperCAmelCase				,						UpperCAmelCase   ),)
            if isinstance(UpperCAmelCase				,						UpperCAmelCase   )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(UpperCAmelCase				,						"_keras_serializable"				,						UpperCAmelCase   )
        }

        for main_layer_class in tf_main_layer_classes:
          # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
          if "T5" in main_layer_class.__name__:
            # Take the same values than in TFT5ModelTester for this shared layer
            __snake_case   :				Tuple        =       TFSharedEmbeddings(self.model_tester.vocab_size				,						self.model_tester.hidden_size				,						name="shared"   )
            __snake_case   :				List[str]        =       False
            __snake_case   :				List[Any]        =       main_layer_class(UpperCAmelCase				,						embed_tokens=UpperCAmelCase   )
          else:
            __snake_case   :				Any        =       main_layer_class(UpperCAmelCase   )

          __snake_case   :				str        =       {
              name: tf.keras.Input(tensor.shape[1:]				,						dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
          }

          if hasattr(self.model_tester				,						"num_labels"   ):
            __snake_case   :				List[Any]        =       self.model_tester.num_labels
          else:
            __snake_case   :				Tuple        =       2

          __snake_case   :				List[str]        =       tf.data.Dataset.from_tensor_slices(
              (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)   ))   ).batch(1   )

          __snake_case   :				Any        =       main_layer(UpperCAmelCase   )[0]
          __snake_case   :				str        =       tf.keras.layers.Dense(UpperCAmelCase				,						activation="softmax"				,						name="outputs"   )(UpperCAmelCase   )
          __snake_case   :				str        =       tf.keras.models.Model(inputs=UpperCAmelCase				,						outputs=[outputs]   )

          model.compile(loss="binary_crossentropy"				,						optimizer="adam"				,						metrics=["binary_accuracy"]   )
          model.fit(UpperCAmelCase				,						epochs=1   )

          with tempfile.TemporaryDirectory() as tmpdirname:
            __snake_case   :				Any        =       os.path.join(UpperCAmelCase				,						"keras_model.h5"   )
            model.save(UpperCAmelCase   )
            if "T5" in main_layer_class.__name__:
              __snake_case   :				Optional[Any]        =       tf.keras.models.load_model(
                  UpperCAmelCase				,						custom_objects={
                      main_layer_class.__name__: main_layer_class,
                      "TFSharedEmbeddings": TFSharedEmbeddings,
                  }				,						)
            else:
              __snake_case   :				Dict        =       tf.keras.models.load_model(
                  UpperCAmelCase				,						custom_objects={main_layer_class.__name__: main_layer_class}   )
            assert isinstance(UpperCAmelCase				,						tf.keras.Model   )
            model(UpperCAmelCase   )







      @slow
      def 						UpperCAmelCase		( self   )       ->   int:






        '''simple docstring'''






        __snake_case   :				int        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
          __snake_case   :				Dict        =       model_class(UpperCAmelCase   )

          __snake_case   :				str        =       copy.deepcopy(UpperCAmelCase   )

          if not self.is_encoder_decoder:
            __snake_case   :				int        =       inputs["input_ids"]
            del inputs["input_ids"]
          else:
            __snake_case   :				Dict        =       inputs["input_ids"]
            __snake_case   :				Any        =       inputs.get("decoder_input_ids"				,						UpperCAmelCase   )
            del inputs["input_ids"]
            inputs.pop("decoder_input_ids"				,						UpperCAmelCase   )

          if not self.is_encoder_decoder:
            __snake_case   :				List[str]        =       model.get_input_embeddings()(UpperCAmelCase   )
          else:
            __snake_case   :				Optional[int]        =       model.get_input_embeddings()(UpperCAmelCase   )
            __snake_case   :				str        =       model.get_input_embeddings()(UpperCAmelCase   )

          __snake_case   :				Dict        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )

          @tf.function
          def run_in_graph_mode():
            return model(UpperCAmelCase   )

          __snake_case   :				int        =       run_in_graph_mode()
          self.assertIsNotNone(UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				int        =       []
        if model.config.bos_token_id is not None:
          special_tokens.append(model.config.bos_token_id   )
        if model.config.pad_token_id is not None:
          special_tokens.append(model.config.pad_token_id   )
        if model.config.eos_token_id is not None:
          special_tokens.append(model.config.eos_token_id   )

        # create random bad tokens that are not special tokens
        __snake_case   :				Optional[Any]        =       []
        while len(UpperCAmelCase   ) < num_bad_tokens:
          __snake_case   :				Any        =       tf.squeeze(ids_tensor((1, 1)				,						self.model_tester.vocab_size   )				,						0   ).numpy()[0]
          if token not in special_tokens:
            bad_tokens.append(UpperCAmelCase   )
        return bad_tokens







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        for token_id in output_ids[0].numpy().tolist():
          self.assertGreaterEqual(UpperCAmelCase				,						0   )
          self.assertLess(UpperCAmelCase				,						self.model_tester.vocab_size   )







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






        '''simple docstring'''






        for bad_word_ids in bad_words_ids:
          # for all slices in batch
          for generated_ids_slice in generated_ids:
            # for all word idx
            for i in range(len(UpperCAmelCase   )				,						len(UpperCAmelCase   )   ):
              # if tokens match
              if generated_ids_slice[i - len(UpperCAmelCase   ) : i] == bad_word_ids:
                return True
        return False








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 				_lowerCamelCase (    a    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:






        '''simple docstring'''






        super().__init__()

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







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        with torch.no_grad():
          __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
          __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
        return latent







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
        with torch.no_grad():
          __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
        __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
        __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
        __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
        return Image.fromarray(img[0]   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				int        =       self.tokenizer(
            UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

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

        __snake_case   :				Union[str, Any]        =       self.tokenizer(
            ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        __snake_case   :				int        =       self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

        return torch.cat([uncond_embedding, text_embedding]   )







      def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.3				,						UpperCAmelCase = 0.6				,						UpperCAmelCase = 0.5				,						UpperCAmelCase = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:






        '''simple docstring'''






        __snake_case   :				Tuple        =       steps - int(kmin * steps   )
        __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )

        __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )

        self.scheduler.set_timesteps(UpperCAmelCase   )

        __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
        __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )

        torch.manual_seed(UpperCAmelCase   )
        __snake_case   :				str        =       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )

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

        __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )

        __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )

        with torch.no_grad():
          __snake_case   :				int        =       self.unet(
              UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample

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

        __snake_case   :				int        =       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
              __snake_case   :				int        =       self.scheduler.add_noise(
                  UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)

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

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

            __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )

            with torch.no_grad():
              __snake_case   :				List[str]        =       self.unet(
                  UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample

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

            __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample

        return self.decode(UpperCAmelCase   )

