





'''simple docstring'''






import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import (
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
         MraModel,
     )
     from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=8    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=99    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=36    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=4    ,   _UpperCAmelCase=None    ,   ):
       A       :       Union[str, Any]							=						parent
       A       :       int							=						batch_size
       A       :       Optional[int]							=						seq_length
       A       :       Optional[Any]							=						is_training
       A       :       Any							=						use_input_mask
       A       :       int							=						use_token_type_ids
       A       :       int							=						use_labels
       A       :       Union[str, Any]							=						vocab_size
       A       :       int							=						hidden_size
       A       :       Optional[Any]							=						num_hidden_layers
       A       :       List[Any]							=						num_attention_heads
       A       :       List[str]							=						intermediate_size
       A       :       List[Any]							=						hidden_act
       A       :       Any							=						hidden_dropout_prob
       A       :       Dict							=						attention_probs_dropout_prob
       A       :       List[str]							=						max_position_embeddings
       A       :       int							=						type_vocab_size
       A       :       List[str]							=						type_sequence_label_size
       A       :       Any							=						initializer_range
       A       :       Dict							=						num_labels
       A       :       Optional[Any]							=						num_choices
       A       :       Optional[int]							=						scope


   def 					snake_case				( self					):
       A       :       List[str]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size					)

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

       A       :       str							=						None
       if self.use_token_type_ids:
           A       :       Optional[int]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.type_vocab_size					)

       A       :       str							=						None
       A       :       Union[str, Any]							=						None
       A       :       Dict							=						None
       if self.use_labels:
           A       :       Tuple							=						ids_tensor([self.batch_size]    ,   self.type_sequence_label_size					)
           A       :       List[Any]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels					)
           A       :       int							=						ids_tensor([self.batch_size]    ,   self.num_choices					)

       A       :       Dict							=						self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def 					snake_case				( self					):
       return MraConfig(
           vocab_size=self.vocab_size    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   type_vocab_size=self.type_vocab_size    ,   is_decoder=_UpperCAmelCase    ,   initializer_range=self.initializer_range    ,   )


   def 					snake_case				( self					):
       A       :       str							=						self.get_config()
       A       :       Optional[Any]							=						300
       return config


   def 					snake_case				( self					):
       (
           (
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	
       )       :       int							=						self.prepare_config_and_inputs()

       A       :       int							=						True
       A       :       List[str]							=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
       A       :       Optional[int]							=						ids_tensor([self.batch_size, self.seq_length]    ,   vocab_size=2					)

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       str							=						MraModel(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Dict							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase					)
       A       :       Dict							=						model(_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase					)
       A       :       Any							=						model(_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       Dict							=						True
       A       :       Optional[Any]							=						MraModel(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Optional[int]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   )
       A       :       Union[str, Any]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   )
       A       :       Optional[Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[str]							=						MraForMaskedLM(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       int							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Any							=						MraForQuestionAnswering(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       List[Any]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   start_positions=_UpperCAmelCase    ,   end_positions=_UpperCAmelCase    ,   )
       self.parent.assertEqual(result.start_logits.shape    ,   (self.batch_size, self.seq_length)					)
       self.parent.assertEqual(result.end_logits.shape    ,   (self.batch_size, self.seq_length)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						self.num_labels
       A       :       Optional[Any]							=						MraForSequenceClassification(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       List[str]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[Any]							=						self.num_labels
       A       :       Union[str, Any]							=						MraForTokenClassification(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Union[str, Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       str							=						self.num_choices
       A       :       Tuple							=						MraForMultipleChoice(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       int							=						input_ids.unsqueeze(1					).expand(-1    ,   self.num_choices    ,   -1					).contiguous()
       A       :       Optional[int]							=						token_type_ids.unsqueeze(1					).expand(-1    ,   self.num_choices    ,   -1					).contiguous()
       A       :       Optional[Any]							=						input_mask.unsqueeze(1					).expand(-1    ,   self.num_choices    ,   -1					).contiguous()
       A       :       str							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   token_type_ids=_UpperCAmelCase    ,   labels=_UpperCAmelCase    ,   )
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)					)


   def 					snake_case				( self					):
       A       :       int							=						self.prepare_config_and_inputs()
       (
           (
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	(
           A
       ),	
       )       :       Optional[Any]							=						config_and_inputs
       A       :       int							=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   (
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False

   _UpperCamelCase       		=   ()


   def 					snake_case				( self					):
       A       :       int							=						MraModelTester(self					)
       A       :       List[str]							=						ConfigTester(self    ,   config_class=_UpperCAmelCase    ,   hidden_size=37					)


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


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           A       :       str							=						type
           self.model_tester.create_and_check_model(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       str							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase					)


   @slow
   def 					snake_case				( self					):
       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A       :       List[Any]							=						MraModel.from_pretrained(_UpperCAmelCase					)
           self.assertIsNotNone(_UpperCAmelCase					)


   @unittest.skip(reason='''MRA does not output attentions'''					)
   def 					snake_case				( self					):
       return







@require_torch
class   _lowercase						(							unittest.TestCase				):


   @slow
   def 					snake_case				( self					):
       A       :       Tuple							=						MraModel.from_pretrained('''uw-madison/mra-base-512-4'''					)
       A       :       Tuple							=						torch.arange(256					).unsqueeze(0					)

       with torch.no_grad():
           A       :       Optional[Any]							=						model(_UpperCAmelCase					)[0]

       A       :       str							=						torch.Size((1, 256, 768)					)
       self.assertEqual(output.shape    ,   _UpperCAmelCase					)

       A       :       Union[str, Any]							=						torch.tensor(
           [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]					)

       self.assertTrue(torch.allclose(output[:, :3, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)


   @slow
   def 					snake_case				( self					):
       A       :       Optional[int]							=						MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''					)
       A       :       Any							=						torch.arange(256					).unsqueeze(0					)

       with torch.no_grad():
           A       :       Optional[Any]							=						model(_UpperCAmelCase					)[0]

       A       :       int							=						50_265

       A       :       int							=						torch.Size((1, 256, vocab_size)					)
       self.assertEqual(output.shape    ,   _UpperCAmelCase					)

       A       :       List[Any]							=						torch.tensor(
           [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]					)

       self.assertTrue(torch.allclose(output[:, :3, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)


   @slow
   def 					snake_case				( self					):
       A       :       int							=						MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''					)
       A       :       Union[str, Any]							=						torch.arange(4_096					).unsqueeze(0					)

       with torch.no_grad():
           A       :       Any							=						model(_UpperCAmelCase					)[0]

       A       :       List[Any]							=						50_265

       A       :       str							=						torch.Size((1, 4_096, vocab_size)					)
       self.assertEqual(output.shape    ,   _UpperCAmelCase					)

       A       :       List[Any]							=						torch.tensor(
           [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]					)

       self.assertTrue(torch.allclose(output[:, :3, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)







'''simple docstring'''






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   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase = "cpu"    ,   _UpperCAmelCase = "openai/clip-vit-large-patch14"					):
       A       :       List[Any]							=						device
       A       :       Optional[int]							=						CLIPTokenizerFast.from_pretrained(_UpperCAmelCase					)
       A       :       Dict							=						[0.48145466, 0.4578275, 0.40821073]
       A       :       List[str]							=						[0.26862954, 0.26130258, 0.27577711]
       A       :       List[Any]							=						torchvision.transforms.Normalize(self.image_mean    ,   self.image_std					)
       A       :       Dict							=						torchvision.transforms.Resize(224					)
       A       :       List[str]							=						torchvision.transforms.CenterCrop(224					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						self.resize(_UpperCAmelCase					)
       A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase					)
       A       :       List[Any]							=						self.normalize(_UpperCAmelCase					)
       return images


   def __call__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Any							=						self.tokenizer(text=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       A       :       List[str]							=						self.preprocess_img(_UpperCAmelCase					)
       A       :       Any							=						{key: value.to(self.device					) for (key, value) in encoding.items()}
       return encoding







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase=10    ,   _UpperCAmelCase=0.01    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="image"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   ):
       super().__init__()
       A       :       Any							=						None
       A       :       Dict							=						device if device else get_device()
       if vqgan:
           A       :       Union[str, Any]							=						vqgan
       else:
           A       :       List[str]							=						load_vqgan(self.device    ,   conf_path=_UpperCAmelCase    ,   ckpt_path=_UpperCAmelCase					)
       self.vqgan.eval()
       if clip:
           A       :       str							=						clip
       else:
           A       :       Tuple							=						CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''					)
       self.clip.to(self.device					)
       A       :       Dict							=						ProcessorGradientFlow(device=self.device					)

       A       :       Optional[Any]							=						iterations
       A       :       str							=						lr
       A       :       Dict							=						log
       A       :       Optional[Any]							=						make_grid
       A       :       Union[str, Any]							=						return_val
       A       :       Tuple							=						quantize
       A       :       Optional[Any]							=						self.vqgan.decoder.z_shape


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=True					):
       A       :       Any							=						[]
       if output_path is None:
           A       :       List[str]							=						'''./animation.gif'''
       if input_path is None:
           A       :       Union[str, Any]							=						self.save_path
       A       :       Optional[int]							=						sorted(glob(input_path + '''/*'''					)					)
       if not len(_UpperCAmelCase					):
           raise ValueError(
               '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
               ''' function?)'''					)
       if len(_UpperCAmelCase					) == 1:
           print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''					)
       A       :       Optional[int]							=						total_duration / len(_UpperCAmelCase					)
       A       :       str							=						[frame_duration] * len(_UpperCAmelCase					)
       if extend_frames:
           A       :       Any							=						1.5
           A       :       Optional[int]							=						3
       for file_name in paths:
           if file_name.endswith('''.png'''					):
               images.append(imageio.imread(_UpperCAmelCase					)					)
       imageio.mimsave(_UpperCAmelCase    ,   _UpperCAmelCase    ,   duration=_UpperCAmelCase					)
       print(f'''gif saved to {output_path}'''					)


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if not (path or img):
           raise ValueError('''Input either path or tensor'''					)
       if img is not None:
           raise NotImplementedError
       A       :       Tuple							=						preprocess(Image.open(_UpperCAmelCase					)    ,   target_image_size=256					).to(self.device					)
       A       :       List[str]							=						preprocess_vqgan(_UpperCAmelCase					)
       A,	*A       :       Dict							=						self.vqgan.encode(_UpperCAmelCase					)
       return z


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.latent.detach().requires_grad_()
       A       :       List[str]							=						base_latent + transform_vector
       if self.quantize:
           A,	*A       :       List[str]							=						self.vqgan.quantize(_UpperCAmelCase					)
       else:
           A       :       Any							=						trans_latent
       return self.vqgan.decode(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       A       :       List[str]							=						self.clip_preprocessor(text=_UpperCAmelCase    ,   images=_UpperCAmelCase    ,   return_tensors='''pt'''    ,   padding=_UpperCAmelCase					)
       A       :       int							=						self.clip(**_UpperCAmelCase					)
       A       :       Dict							=						clip_outputs.logits_per_image
       if weights is not None:
           A       :       Optional[Any]							=						similarity_logits * weights
       return similarity_logits.sum()


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						self._get_clip_similarity(pos_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=(1 / pos_prompts['''weights'''])					)
       if neg_prompts:
           A       :       str							=						self._get_clip_similarity(neg_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=neg_prompts['''weights''']					)
       else:
           A       :       Union[str, Any]							=						torch.tensor([1]    ,   device=self.device					)
       A       :       Optional[int]							=						-torch.log(_UpperCAmelCase					) + torch.log(_UpperCAmelCase					)
       return loss


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						torch.randn_like(self.latent    ,   requires_grad=_UpperCAmelCase    ,   device=self.device					)
       A       :       Optional[int]							=						torch.optim.Adam([vector]    ,   lr=self.lr					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       wandb.init(reinit=_UpperCAmelCase    ,   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:
           A       :       Optional[Any]							=						Image.open(_UpperCAmelCase					)
           A       :       Dict							=						image.resize((256, 256)					)
           wandb.log('''Original Image'''    ,   wandb.Image(_UpperCAmelCase					)					)


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       if image_path:
           A       :       List[str]							=						self._get_latent(_UpperCAmelCase					)
       else:
           A       :       List[Any]							=						torch.randn(self.latent_dim    ,   device=self.device					)
       if self.log:
           self._init_logging(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

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

       A       :       Dict							=						self.vqgan.decode(self.latent					)[0]
       if show_intermediate:
           print('''Original Image'''					)
           show_pil(custom_to_pil(_UpperCAmelCase					)					)

       A       :       List[Any]							=						loop_post_process(_UpperCAmelCase					)
       for iter, transformed_img in enumerate(self._optimize_CLIP(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)					):
           if show_intermediate:
               show_pil(_UpperCAmelCase					)
           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(_UpperCAmelCase					)}					)
       if show_final:
           show_pil(_UpperCAmelCase					)
       if save_final:
           transformed_img.save(os.path.join(self.save_path    ,   f'''iter_{iter:03d}_final.png'''					)					)
