'''simple docstring'''


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=2				,		snake_case_=True				,		snake_case_=False				,		snake_case_=1_0				,		snake_case_=3				,		snake_case_=3_2 * 8				,		snake_case_=3_2 * 8				,		snake_case_=4				,		snake_case_=6_4				,		):
       UpperCAmelCase_							:       Optional[int]       =				parent
       UpperCAmelCase_							:       Dict       =				batch_size
       UpperCAmelCase_							:       Optional[Any]       =				is_training
       UpperCAmelCase_							:       Optional[Any]       =				use_auxiliary_loss
       UpperCAmelCase_							:       int       =				num_queries
       UpperCAmelCase_							:       Tuple       =				num_channels
       UpperCAmelCase_							:       Optional[int]       =				min_size
       UpperCAmelCase_							:       Optional[int]       =				max_size
       UpperCAmelCase_							:       int       =				num_labels
       UpperCAmelCase_							:       List[Any]       =				hidden_dim
       UpperCAmelCase_							:       Any       =				hidden_dim


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       int       =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
           snake_case_						)

       UpperCAmelCase_							:       Optional[Any]       =				torch.ones([self.batch_size, self.min_size, self.max_size]				,		device=snake_case_						)

       UpperCAmelCase_							:       Optional[Any]       =				(
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,		device=snake_case_						) > 0.5
       ).float()
       UpperCAmelCase_							:       List[str]       =				(torch.rand((self.batch_size, self.num_labels)				,		device=snake_case_						) > 0.5).long()

       UpperCAmelCase_							:       str       =				self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       str       =				MaskaFormerConfig(
           hidden_size=self.hidden_dim				,		)
       UpperCAmelCase_							:       List[str]       =				self.num_queries
       UpperCAmelCase_							:       Any       =				self.num_labels

       UpperCAmelCase_							:       Union[str, Any]       =				[1, 1, 1, 1]
       UpperCAmelCase_							:       List[str]       =				self.num_channels

       UpperCAmelCase_							:       Union[str, Any]       =				6_4
       UpperCAmelCase_							:       Tuple       =				1_2_8
       UpperCAmelCase_							:       Tuple       =				self.hidden_dim
       UpperCAmelCase_							:       str       =				self.hidden_dim
       UpperCAmelCase_							:       List[str]       =				self.hidden_dim
       return config


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Dict       =				self.prepare_config_and_inputs()
       UpperCAmelCase_							:       Union[str, Any]       =				{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):
       UpperCAmelCase_							:       Optional[Any]       =				output.encoder_hidden_states
       UpperCAmelCase_							:       Optional[int]       =				output.pixel_decoder_hidden_states
       UpperCAmelCase_							:       Dict       =				output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(snake_case_						)				,		len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(snake_case_						)				,		len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(snake_case_						)				,		config.decoder_layers						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=False						):

       with torch.no_grad():
            UpperCAmelCase_							:       List[Any]       =				MaskaFormerModel(config=snake_case_						)
            model.to(snake_case_						)
            model.eval()

            UpperCAmelCase_							:       List[str]       =				model(pixel_values=snake_case_				,		pixel_mask=snake_case_						)
            UpperCAmelCase_							:       Any       =				model(snake_case_				,		output_hidden_states=snake_case_						)

       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape				,		(self.batch_size, self.num_queries, self.hidden_dim)				,		)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

       if output_hidden_states:
            self.check_output_hidden_state(snake_case_				,		snake_case_						)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):
       UpperCAmelCase_							:       int       =				MaskaFormerForUniversalSegmentation(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()

       def comm_check_on_output(snake_case_						):
            # let's still check that all the required stuff is there
            self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
            self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
            self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
            # okay, now we need to check the logits shape
            # due to the encoder compression, masks have a //4 spatial size
            self.parent.assertEqual(
                result.masks_queries_logits.shape				,		(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)				,		)
            # + 1 for null class
            self.parent.assertEqual(
                result.class_queries_logits.shape				,		(self.batch_size, self.num_queries, self.num_labels + 1)						)

       with torch.no_grad():
            UpperCAmelCase_							:       Union[str, Any]       =				model(pixel_values=snake_case_				,		pixel_mask=snake_case_						)
            UpperCAmelCase_							:       Any       =				model(snake_case_						)

            comm_check_on_output(snake_case_						)

            UpperCAmelCase_							:       Union[str, Any]       =				model(
                pixel_values=snake_case_				,		pixel_mask=snake_case_				,		mask_labels=snake_case_				,		class_labels=snake_case_						)

       comm_check_on_output(snake_case_						)

       self.parent.assertTrue(result.loss is not None						)
       self.parent.assertEqual(result.loss.shape				,		torch.Size([1]						)						)







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  lowerCamelCase_		:str						   =		{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  lowerCamelCase_		:Optional[int]						   =		False
  lowerCamelCase_		:Any						   =		False
  lowerCamelCase_		:Dict						   =		False
  lowerCamelCase_		:Optional[Any]						   =		False


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Tuple       =				MaskaFormerModelTester(self						)
       UpperCAmelCase_							:       str       =				ConfigTester(self				,		config_class=snake_case_				,		has_text_modality=snake_case_						)


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


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(snake_case_				,		**snake_case_				,		output_hidden_states=snake_case_						)


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case_						)


  @unittest.skip(reason='Mask2Former does not use inputs_embeds'						)
  def _UpperCamelCase					(			self						):
       pass


  @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
  def _UpperCamelCase					(			self						):
       pass


  @unittest.skip(reason='Mask2Former is not a generative model'						)
  def _UpperCamelCase					(			self						):
       pass


  @unittest.skip(reason='Mask2Former does not use token embeddings'						)
  def _UpperCamelCase					(			self						):
       pass


  @require_torch_multi_gpu
  @unittest.skip(
      reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
  def _UpperCamelCase					(			self						):
       pass


  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
  def _UpperCamelCase					(			self						):
       pass


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:       Dict       =				model_class(snake_case_						)
            UpperCAmelCase_							:       int       =				inspect.signature(model.forward						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_							:       List[str]       =				[*signature.parameters.keys()]

            UpperCAmelCase_							:       int       =				['pixel_values']
            self.assertListEqual(arg_names[:1]				,		snake_case_						)


  @slow
  def _UpperCamelCase					(			self						):

       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
            UpperCAmelCase_							:       int       =				MaskaFormerModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Optional[Any]       =				(self.model_tester.min_size,) * 2
       UpperCAmelCase_							:       Tuple       =				{
           'pixel_values': torch.randn((2, 3, *size)				,		device=snake_case_						),
           'mask_labels': torch.randn((2, 1_0, *size)				,		device=snake_case_						),
           'class_labels': torch.zeros(2				,		1_0				,		device=snake_case_						).long(),
       }
       UpperCAmelCase_							:       Dict       =				self.model_tester.get_config()

       UpperCAmelCase_							:       List[Any]       =				MaskaFormerForUniversalSegmentation(snake_case_						).to(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				model(**snake_case_						)
       self.assertTrue(outputs.loss is not None						)


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       List[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(snake_case_				,		**snake_case_				,		output_hidden_states=snake_case_						)


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:       Union[str, Any]       =				model_class(snake_case_						).to(snake_case_						)
            UpperCAmelCase_							:       Optional[Any]       =				model(**snake_case_				,		output_attentions=snake_case_						)
            self.assertTrue(outputs.attentions is not None						)


  def _UpperCamelCase					(			self						):

       if not self.model_tester.is_training:
            return

       UpperCAmelCase_							:       Union[str, Any]       =				self.all_model_classes[1]
       UpperCAmelCase_							:       str       =				self.model_tester.prepare_config_and_inputs()

       UpperCAmelCase_							:       Optional[int]       =				model_class(snake_case_						)
       model.to(snake_case_						)
       model.train()

       UpperCAmelCase_							:       int       =				model(snake_case_				,		mask_labels=snake_case_				,		class_labels=snake_case_						).loss
       loss.backward()






  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Dict       =				self.all_model_classes[1]
       UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
       UpperCAmelCase_							:       Any       =				True
       UpperCAmelCase_							:       Union[str, Any]       =				True

       UpperCAmelCase_							:       Tuple       =				model_class(snake_case_						).to(snake_case_						)
       model.train()

       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		mask_labels=snake_case_				,		class_labels=snake_case_						)

       UpperCAmelCase_							:       int       =				outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       UpperCAmelCase_							:       int       =				outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       UpperCAmelCase_							:       str       =				outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       UpperCAmelCase_							:       str       =				outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=snake_case_						)

       self.assertIsNotNone(encoder_hidden_states.grad						)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
       self.assertIsNotNone(attentions.grad						)







snake_case__					:      int				      =  1e-4


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
     return image






@require_vision
@slow
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase					(			self						):
       return "facebook/mask2former-swin-small-coco-instance"


  @cached_property
  def _UpperCamelCase					(			self						):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       List[Any]       =				MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(snake_case_						)
       UpperCAmelCase_							:       Any       =				self.default_image_processor
       UpperCAmelCase_							:       int       =				prepare_img()
       UpperCAmelCase_							:       List[str]       =				image_processor(snake_case_				,		return_tensors='pt'						).to(snake_case_						)
       UpperCAmelCase_							:       List[str]       =				inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0						)
       # check size
       self.assertEqual(snake_case_				,		(1, 3, 3_8_4, 3_8_4)						)

       with torch.no_grad():
            UpperCAmelCase_							:       List[Any]       =				model(**snake_case_						)

       UpperCAmelCase_							:       int       =				torch.tensor(
           [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]						).to(snake_case_						)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]				,		snake_case_				,		atol=snake_case_						)						)

       UpperCAmelCase_							:       Optional[Any]       =				torch.tensor(
           [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]						).to(snake_case_						)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,		snake_case_				,		atol=snake_case_						)						)

       UpperCAmelCase_							:       Optional[int]       =				torch.tensor(
           [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]						).to(snake_case_						)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,		snake_case_				,		atol=snake_case_						)						)


  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       int       =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(snake_case_						).eval()
       UpperCAmelCase_							:       Union[str, Any]       =				self.default_image_processor
       UpperCAmelCase_							:       Dict       =				prepare_img()
       UpperCAmelCase_							:       Tuple       =				image_processor(snake_case_				,		return_tensors='pt'						).to(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0						)
       # check size
       self.assertEqual(snake_case_				,		(1, 3, 3_8_4, 3_8_4)						)

       with torch.no_grad():
            UpperCAmelCase_							:       int       =				model(**snake_case_						)
       # masks_queries_logits
       UpperCAmelCase_							:       Optional[Any]       =				outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape				,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
       UpperCAmelCase_							:       Optional[Any]       =				[
           [-8.78_39, -9.00_56, -8.81_21],
           [-7.41_04, -7.03_13, -6.54_01],
           [-6.61_05, -6.34_27, -6.46_75],
       ]
       UpperCAmelCase_							:       Optional[Any]       =				torch.tensor(snake_case_						).to(snake_case_						)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,		snake_case_				,		atol=snake_case_						)						)
       # class_queries_logits
       UpperCAmelCase_							:       Tuple       =				outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape				,		(1, model.config.num_queries, model.config.num_labels + 1)						)
       UpperCAmelCase_							:       List[Any]       =				torch.tensor(
           [
               [1.83_24, -8.08_35, -4.19_22],
               [0.84_50, -9.00_50, -3.60_53],
               [0.30_45, -7.72_93, -3.02_75],
           ]						).to(snake_case_						)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,		snake_case_				,		atol=snake_case_						)						)






  def _UpperCamelCase					(			self						):
       UpperCAmelCase_							:       Optional[int]       =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(snake_case_						).eval()
       UpperCAmelCase_							:       Optional[int]       =				self.default_image_processor

       UpperCAmelCase_							:       Union[str, Any]       =				image_processor(
           [np.zeros((3, 8_0_0, 1_3_3_3)						), np.zeros((3, 8_0_0, 1_3_3_3)						)]				,		segmentation_maps=[np.zeros((3_8_4, 3_8_4)						).astype(np.floataa						), np.zeros((3_8_4, 3_8_4)						).astype(np.floataa						)]				,		return_tensors='pt'				,		)

       UpperCAmelCase_							:       Union[str, Any]       =				inputs['pixel_values'].to(snake_case_						)
       UpperCAmelCase_							:       List[str]       =				[el.to(snake_case_						) for el in inputs['mask_labels']]
       UpperCAmelCase_							:       Optional[Any]       =				[el.to(snake_case_						) for el in inputs['class_labels']]

       with torch.no_grad():
            UpperCAmelCase_							:       Any       =				model(**snake_case_						)

       self.assertTrue(outputs.loss is not None						)







'''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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






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




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				device
       UpperCAmelCase_							:       Tuple       =				CLIPTokenizerFast.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       UpperCAmelCase_							:       Union[str, Any]       =				[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       UpperCAmelCase_							:       Tuple       =				torchvision.transforms.Normalize(self.image_mean				,		self.image_std						)
       UpperCAmelCase_							:       Optional[Any]       =				torchvision.transforms.Resize(2_2_4						)
       UpperCAmelCase_							:       Any       =				torchvision.transforms.CenterCrop(2_2_4						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.resize(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.center_crop(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.normalize(snake_case_						)
       return images






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




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.tokenizer(text=snake_case_				,		**snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.preprocess_img(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				{key: value.to(self.device						) for (key, value) in encoding.items()}
       return encoding







class 					__SCREAMING_SNAKE_CASE						(	nn.Module	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_=1_0				,		snake_case_=0.01				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=False				,		snake_case_=True				,		snake_case_="image"				,		snake_case_=True				,		snake_case_=False				,		snake_case_=False				,		snake_case_=False				,		):




       '''simple docstring'''

       super().__init__()
       UpperCAmelCase_							:       List[str]       =				None
       UpperCAmelCase_							:       Dict       =				device if device else get_device()
       if vqgan:
            UpperCAmelCase_							:       Any       =				vqgan
       else:
            UpperCAmelCase_							:       Dict       =				load_vqgan(self.device				,		conf_path=snake_case_				,		ckpt_path=snake_case_						)
       self.vqgan.eval()
       if clip:
            UpperCAmelCase_							:       List[str]       =				clip
       else:
            UpperCAmelCase_							:       List[Any]       =				CLIPModel.from_pretrained('openai/clip-vit-base-patch32'						)
       self.clip.to(self.device						)
       UpperCAmelCase_							:       Tuple       =				ProcessorGradientFlow(device=self.device						)

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=5				,		snake_case_=True						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None						):




       '''simple docstring'''

       if not (path or img):
            raise ValueError('Input either path or tensor'						)
       if img is not None:
            raise NotImplementedError
       UpperCAmelCase_							:       Optional[Any]       =				preprocess(Image.open(snake_case_						)				,		target_image_size=2_5_6						).to(self.device						)
       UpperCAmelCase_							:       Dict       =				preprocess_vqgan(snake_case_						)
       UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.encode(snake_case_						)
       return z


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.latent.detach().requires_grad_()
       UpperCAmelCase_							:       List[Any]       =				base_latent + transform_vector
       if self.quantize:
            UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.quantize(snake_case_						)
       else:
            UpperCAmelCase_							:       Optional[int]       =				trans_latent
       return self.vqgan.decode(snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.clip_preprocessor(text=snake_case_				,		images=snake_case_				,		return_tensors='pt'				,		padding=snake_case_						)
       UpperCAmelCase_							:       Any       =				self.clip(**snake_case_						)
       UpperCAmelCase_							:       Dict       =				clip_outputs.logits_per_image
       if weights is not None:
            UpperCAmelCase_							:       Union[str, Any]       =				similarity_logits * weights
       return similarity_logits.sum()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       wandb.init(reinit=snake_case_				,		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:
            UpperCAmelCase_							:       str       =				Image.open(snake_case_						)
            UpperCAmelCase_							:       str       =				image.resize((2_5_6, 2_5_6)						)
            wandb.log('Original Image'				,		wandb.Image(snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=True				,		snake_case_=None				,		):




       '''simple docstring'''

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

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

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

       UpperCAmelCase_							:       Optional[int]       =				loop_post_process(snake_case_						)
       for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case_				,		snake_case_				,		snake_case_						)						):
            if show_intermediate:
                 show_pil(snake_case_						)
            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(snake_case_						)}						)
       if show_final:
            show_pil(snake_case_						)
       if save_final:
            transformed_img.save(os.path.join(self.save_path				,		F'''iter_{iter:03d}_final.png'''						)						)






