
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  :					List[str]						    =							"""platform"""
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class __snake_case		:
    _a		:						Any=				PegasusConfig
    _a		:						Dict=				{}
    _a		:						List[Any]=				"gelu"
    def __init__(  self		,snake_case		,snake_case=13		,snake_case=7		,snake_case=True		,snake_case=False		,snake_case=99		,snake_case=32		,snake_case=5		,snake_case=4		,snake_case=37		,snake_case=0.1		,snake_case=0.1		,snake_case=20		,snake_case=2		,snake_case=1		,snake_case=0		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      parent
           lowercase							:     int       	=      batch_size
           lowercase							:     int       	=      seq_length
           lowercase							:     Dict       	=      is_training
           lowercase							:     List[str]       	=      use_labels
           lowercase							:     int       	=      vocab_size
           lowercase							:     Tuple       	=      hidden_size
           lowercase							:     Dict       	=      num_hidden_layers
           lowercase							:     Dict       	=      num_attention_heads
           lowercase							:     int       	=      intermediate_size

           lowercase							:     str       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Any       	=      max_position_embeddings
           lowercase							:     List[str]       	=      eos_token_id
           lowercase							:     Any       	=      pad_token_id
           lowercase							:     Optional[Any]       	=      bos_token_id
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      ids_tensor([self.batch_size, self.seq_length - 1]		,self.vocab_size      ).clip(3		,self.vocab_size      )
           lowercase							:     str       	=      np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )		,1      )
           lowercase							:     Optional[Any]       	=      np.concatenate([input_ids, eos_tensor]		,axis=1      )

           lowercase							:     Dict       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )

           lowercase							:     str       	=      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		,)
           lowercase							:     int       	=      prepare_pegasus_inputs_dict(__a		,__a		,__a      )
           return config, inputs_dict
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      20
           lowercase							:     str       	=      model_class_name(__a      )

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

           lowercase							:     List[Any]       	=      (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           lowercase							:     Union[str, Any]       	=      model.init_cache(decoder_input_ids.shape[0]		,__a		,__a      )
           lowercase							:     List[Any]       	=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,dtype="""i4"""      )

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

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

           lowercase							:     List[str]       	=      model.decode(__a		,__a      )

           lowercase							:     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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      20
           lowercase							:     Any       	=      model_class_name(__a      )

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

           lowercase							:     Optional[Any]       	=      (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

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

           lowercase							:     Tuple       	=      model.init_cache(decoder_input_ids.shape[0]		,__a		,__a      )
           lowercase							:     Union[str, Any]       	=      jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,)

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

           lowercase							:     Optional[int]       	=      model.decode(__a		,__a		,decoder_attention_mask=__a      )

           lowercase							:     Union[str, 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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	) ->    Dict:




       if attention_mask is None:
              lowercase							:     Tuple       	=      np.not_equal(_lowerCamelCase					,	config.pad_token_id				).astype(np.inta				)
       if decoder_attention_mask is None:
              lowercase							:     str       	=      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 __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						int=				(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    _a		:						int=				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    _a		:						Any=				True
    _a		:						Union[str, Any]=				False
    _a		:						Optional[Any]=				False
    _a		:						str=				False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      FlaxPegasusModelTester(self      )
           lowercase							:     int       	=      ConfigTester(self		,config_class=__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__      ):
                         lowercase							:     Dict       	=      self._prepare_for_class(__a		,__a      )
                         lowercase							:     Optional[int]       	=      model_class(__a      )

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

                         with self.subTest("""JIT Enabled"""      ):
                                lowercase							:     List[Any]       	=      encode_jitted(**__a      ).to_tuple()

                         with self.subTest("""JIT Disabled"""      ):
                                with jax.disable_jit():
                                       lowercase							:     Optional[int]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__      ):
                         lowercase							:     List[str]       	=      model_class(__a      )
                         lowercase							:     Optional[int]       	=      model.encode(inputs_dict["""input_ids"""]		,inputs_dict["""attention_mask"""]      )

                         lowercase							:     str       	=      {
                             "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(snake_case		,snake_case		,snake_case      ):
                                return model.decode(
                                    decoder_input_ids=__a		,decoder_attention_mask=__a		,encoder_outputs=__a		,)

                         with self.subTest("""JIT Enabled"""      ):
                                lowercase							:     str       	=      decode_jitted(**__a      ).to_tuple()

                         with self.subTest("""JIT Disabled"""      ):
                                with jax.disable_jit():
                                       lowercase							:     str       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for model_class_name in self.all_model_classes:
                  lowercase							:     Optional[int]       	=      model_class_name.from_pretrained("""google/pegasus-large"""		,from_pt=__a      )
                  lowercase							:     Dict       	=      np.ones((1, 1)      )
                  lowercase							:     List[str]       	=      model(__a      )
                  self.assertIsNotNone(__a      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""      )
           lowercase							:     Any       	=      PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""      )

           lowercase							:     Optional[int]       	=      [
               " 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!\" ",
           ]

           lowercase							:     List[Any]       	=      [
               "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.",
           ]

           lowercase							:     int       	=      tokenizer(__a		,return_tensors="""np"""		,truncation=__a		,max_length=512		,padding=__a      )
           lowercase							:     Union[str, Any]       	=      model.generate(**__a		,num_beams=2      ).sequences
           lowercase							:     Union[str, Any]       	=      tokenizer.batch_decode(__a		,skip_special_tokens=__a      )
           assert tgt_text == decoded





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class   UpperCAmelCase_    :







							def __init__(   self,			__a = "cpu",			__a = "openai/clip-vit-large-patch14"):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    device
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizerFast.from_pretrained(__a)
														_lowerCAmelCase     :							Any    =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
														_lowerCAmelCase     :							Union[str, Any]    =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
														_lowerCAmelCase     :							Tuple    =    torchvision.transforms.Normalize(self.image_mean,			self.image_std)
														_lowerCAmelCase     :							Optional[int]    =    torchvision.transforms.Resize(224)
														_lowerCAmelCase     :							Dict    =    torchvision.transforms.CenterCrop(224)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







							def __call__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







							def __init__(   self,			__a=10,			__a=0.01,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=False,			__a=True,			__a="image",			__a=True,			__a=False,			__a=False,			__a=False,			):

														'''simple docstring'''

														super().__init__()
														_lowerCAmelCase     :							List[str]    =    None
														_lowerCAmelCase     :							List[str]    =    device if device else get_device()
														if vqgan:
																					_lowerCAmelCase     :							Union[str, Any]    =    vqgan
														else:
																					_lowerCAmelCase     :							Optional[Any]    =    load_vqgan(self.device,			conf_path=__a,			ckpt_path=__a)
														self.vqgan.eval()
														if clip:
																					_lowerCAmelCase     :							str    =    clip
														else:
																					_lowerCAmelCase     :							int    =    CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
														self.clip.to(self.device)
														_lowerCAmelCase     :							Optional[int]    =    ProcessorGradientFlow(device=self.device)

														_lowerCAmelCase     :							Any    =    iterations
														_lowerCAmelCase     :							List[Any]    =    lr
														_lowerCAmelCase     :							Tuple    =    log
														_lowerCAmelCase     :							List[str]    =    make_grid
														_lowerCAmelCase     :							int    =    return_val
														_lowerCAmelCase     :							Dict    =    quantize
														_lowerCAmelCase     :							Any    =    self.vqgan.decoder.z_shape







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    []
														if output_path is None:
																					_lowerCAmelCase     :							List[Any]    =    "./animation.gif"
														if input_path is None:
																					_lowerCAmelCase     :							str    =    self.save_path
														_lowerCAmelCase     :							str    =    sorted(glob(input_path + "/*"))
														if not len(__a):
																					raise ValueError(
																					    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
																					    " function?)")
														if len(__a) == 1:
																					print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
														_lowerCAmelCase     :							Optional[int]    =    total_duration / len(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    [frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase     :							Any    =    1.5
																					_lowerCAmelCase     :							List[str]    =    3
														for file_name in paths:
																					if file_name.endswith(".png"):
																												images.append(imageio.imread(__a))
														imageio.mimsave(__a,			__a,			duration=__a)
														print(f"gif saved to {output_path}")







							def    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.latent.detach().requires_grad_()
														_lowerCAmelCase     :							Dict    =    base_latent + transform_vector
														if self.quantize:
																					_lowerCAmelCase   ,    *_lowerCAmelCase     :							List[Any]    =    self.vqgan.quantize(__a)
														else:
																					_lowerCAmelCase     :							Any    =    trans_latent
														return self.vqgan.decode(__a)







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.clip_preprocessor(text=__a,			images=__a,			return_tensors="pt",			padding=__a)
														_lowerCAmelCase     :							Optional[int]    =    self.clip(**__a)
														_lowerCAmelCase     :							Any    =    clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase     :							Tuple    =    similarity_logits * weights
														return similarity_logits.sum()







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(pos_prompts["prompts"],			__a,			weights=(1 / pos_prompts["weights"]))
														if neg_prompts:
																					_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(neg_prompts["prompts"],			__a,			weights=neg_prompts["weights"])
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    torch.tensor([1],			device=self.device)
														_lowerCAmelCase     :							List[str]    =    -torch.log(__a) + torch.log(__a)
														return loss







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

														for i in range(self.iterations):
																					optim.zero_grad()
																					_lowerCAmelCase     :							Any    =    self._add_vector(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    loop_post_process(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    self._get_CLIP_loss(__a,			__a,			__a)
																					print("CLIP loss",			__a)
																					if self.log:
																												wandb.log({"CLIP Loss": clip_loss})
																					clip_loss.backward(retain_graph=__a)
																					optim.step()
																					if self.return_val == "image":
																												yield custom_to_pil(transformed_img[0])
																					else:
																												yield vector







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														wandb.init(reinit=__a,			project="face-editor")
														wandb.config.update({"Positive Prompts": positive_prompts})
														wandb.config.update({"Negative Prompts": negative_prompts})
														wandb.config.update({"lr": self.lr, "iterations": self.iterations})
														if image_path:
																					_lowerCAmelCase     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if not prompts:
																					return []
														_lowerCAmelCase     :							int    =    []
														_lowerCAmelCase     :							List[str]    =    []
														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Union[str, Any]    =    [prompt.strip() for prompt in prompts.split("|")]
														for prompt in prompts:
																					if isinstance(__a,			(tuple, list)):
																												_lowerCAmelCase     :							Optional[Any]    =    prompt[0]
																												_lowerCAmelCase     :							Union[str, Any]    =    float(prompt[1])
																					elif ":" in prompt:
																												_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    prompt.split(":")
																												_lowerCAmelCase     :							Optional[Any]    =    float(__a)
																					else:
																												_lowerCAmelCase     :							Optional[int]    =    prompt
																												_lowerCAmelCase     :							List[Any]    =    1.0
																					processed_prompts.append(__a)
																					weights.append(__a)
														return {
														    "prompts": processed_prompts,
														    "weights": torch.tensor(__a,			device=self.device),
														}







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

														if image_path:
																					_lowerCAmelCase     :							List[Any]    =    self._get_latent(__a)
														else:
																					_lowerCAmelCase     :							Any    =    torch.randn(self.latent_dim,			device=self.device)
														if self.log:
																					self._init_logging(__a,			__a,			__a)

														assert pos_prompts, "You must provide at least one positive prompt."
														_lowerCAmelCase     :							int    =    self.process_prompts(__a)
														_lowerCAmelCase     :							List[str]    =    self.process_prompts(__a)
														if save_final and save_path is None:
																					_lowerCAmelCase     :							int    =    os.path.join("./outputs/",			"_".join(pos_prompts["prompts"]))
														if not os.path.exists(__a):
																					os.makedirs(__a)
														else:
																					_lowerCAmelCase     :							Tuple    =    save_path + "_" + get_timestamp()
																					os.makedirs(__a)
														_lowerCAmelCase     :							Tuple    =    save_path

														_lowerCAmelCase     :							List[Any]    =    self.vqgan.decode(self.latent)[0]
														if show_intermediate:
																					print("Original Image")
																					show_pil(custom_to_pil(__a))

														_lowerCAmelCase     :							int    =    loop_post_process(__a)
														for iter, transformed_img in enumerate(self._optimize_CLIP(__a,			__a,			__a)):
																					if show_intermediate:
																												show_pil(__a)
																					if save_intermediate:
																												transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}.png"))
																					if self.log:
																												wandb.log({"Image": wandb.Image(__a)})
														if show_final:
																					show_pil(__a)
														if save_final:
																					transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}_final.png"))
