


'''simple docstring'''

import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)

def _SCREAMING_SNAKE_CASE      (A       )       ->       str:






       """simple docstring"""







       if model.config.model_type == "gpt2":
              return model.transformer.h[0].mlp.c_fc
       return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn
 class   __lowerCAmelCase						(nn.Module     ):





       '''simple docstring'''






       def __init__(self    :    int   ,						UpperCamelCase    :    nn.Module   ,						UpperCamelCase    :    int ):

              '''simple docstring'''







              super().__init__()
              lowercase__      			= module
              lowercase__      			= nn.Sequential(
                  nn.Linear(module.in_features   ,						UpperCamelCase   ,						bias=UpperCamelCase )   ,						nn.Linear(UpperCamelCase   ,						module.out_features   ,						bias=UpperCamelCase )   ,						)
              lowercase__      			= (2.0 / (5 * min(module.in_features   ,						module.out_features ))) ** 0.5
              nn.init.normal_(self.adapter[0].weight   ,						std=UpperCamelCase )
              nn.init.zeros_(self.adapter[1].weight )
              self.adapter.to(module.weight.device )






       def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						*UpperCamelCase    :    str   ,						**UpperCamelCase    :    Optional[Any] ):

              '''simple docstring'''







              return self.module(UpperCamelCase   ,						*UpperCamelCase   ,						**UpperCamelCase ) + self.adapter(UpperCamelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							"""bigscience/bloom-1b7"""

      # Constant values
      lowerCAmelCase__					:     Optional[Any]  					=							2.109_659_552_692_574

      lowerCAmelCase__					:     Union[str, Any]  					=							"""Hello my name is"""
      lowerCAmelCase__					:     Tuple  					=							set()
      EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""     )
      EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""     )
      EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""     )
      lowerCAmelCase__					:     Union[str, Any]  					=							10



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= AutoTokenizer.from_pretrained(self.model_name )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             super().setUp()

             # Models and tokenizer
             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						torch_dtype=torch.floataa   ,						device_map='''auto''' )
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             del self.model_fpaa
             del self.model_abit

             gc.collect()
             torch.cuda.empty_cache()



      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= self.model_abit.config

             self.assertTrue(hasattr(UpperCamelCase   ,						'''quantization_config''' ) )

             lowercase__      			= config.to_dict()
             lowercase__      			= config.to_diff_dict()

             lowercase__      			= config.to_json_string()



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             from bitsandbytes.nn import Paramsabit

             lowercase__      			= self.model_fpaa.get_memory_footprint()
             lowercase__      			= self.model_abit.get_memory_footprint()

             self.assertAlmostEqual(mem_fpaa / mem_abit   ,						self.EXPECTED_RELATIVE_DIFFERENCE )
             lowercase__      			= get_some_linear_layer(self.model_abit )
             self.assertTrue(linear.weight.__class__ == Paramsabit )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             from transformers import TaPreTrainedModel

             self.model_fpaa.get_memory_footprint()
             self.model_abit.get_memory_footprint()

             for name, module in self.model_abit.named_modules():
                    if isinstance(UpperCamelCase   ,						torch.nn.Linear ):
                           if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                                  # 4-bit parameters are packed in uint8 variables
                                  self.assertTrue(module.weight.dtype == torch.uinta )



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )
             lowercase__      			= self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             self.assertIn(self.tokenizer.decode(output_sequences[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= BitsAndBytesConfig()
             lowercase__      			= True

             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						quantization_config=UpperCamelCase   ,						device_map='''auto''' )

             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )
             lowercase__      			= model_abit_from_config.generate(
                 input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             self.assertIn(self.tokenizer.decode(output_sequences[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             with self.assertRaises(UpperCamelCase ), tempfile.TemporaryDirectory() as tmpdirname:
                    self.model_abit.save_pretrained(UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= BitsAndBytesConfig()

             with self.assertRaises(UpperCamelCase ):
                    lowercase__      			= AutoModelForCausalLM.from_pretrained(
                        self.model_name   ,						quantization_config=UpperCamelCase   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto'''   ,						bnb_abit_quant_type='''nf4'''   ,						)



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             with self.assertRaises(UpperCamelCase ):
                    # Tries with `str`
                    self.model_abit.to('''cpu''' )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `dtype``
                    self.model_abit.to(torch.floataa )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.to(torch.device('''cuda:0''' ) )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.float()

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.half()

             # Test if we did not break anything
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )

             lowercase__      			= self.model_fpaa.to(torch.floataa )
             lowercase__      			= self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.to('''cpu''' )

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.half()

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.float()






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @classmethod
      def       UpperCamelCase__		(cls    :    Dict ):

             '''simple docstring'''







             lowercase__      			= '''t5-small'''
             lowercase__      			= '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
             lowercase__      			= AutoTokenizer.from_pretrained(cls.model_name )
             lowercase__      			= '''Translate in German: Hello, my dog is cute'''



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             gc.collect()
             torch.cuda.empty_cache()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             from transformers import TaForConditionalGeneration

             lowercase__      			= TaForConditionalGeneration._keep_in_fpaa_modules
             lowercase__      			= None

             # test with `t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )

             # test with `flan-t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(
                 self.dense_act_model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )
             lowercase__      			= modules






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             import bitsandbytes as bnb

             from transformers import TaForConditionalGeneration

             # test with `t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )

             # there was a bug with decoders - this test checks that it is fixed
             self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q   ,						bnb.nn.Linearabit ) )

             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )

             # test with `flan-t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(
                 self.dense_act_model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             super().setUp()
             # model_name
             lowercase__      			= '''bigscience/bloom-560m'''
             lowercase__      			= '''t5-small'''

             # Different types of model

             lowercase__      			= AutoModel.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # Sequence classification model
             lowercase__      			= AutoModelForSequenceClassification.from_pretrained(
                 self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # CausalLM model
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # Seq2seq model
             lowercase__      			= AutoModelForSeqaSeqLM.from_pretrained(
                 self.seq_to_seq_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             del self.base_model
             del self.sequence_model
             del self.model_abit
             del self.seq_to_seq_model

             gc.collect()
             torch.cuda.empty_cache()






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             from bitsandbytes.nn import Paramsabit

             self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )

             # Other heads should be nn.Parameter
             self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
             self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
             self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             super().setUp()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             del self.pipe

             gc.collect()
             torch.cuda.empty_cache()






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= pipeline(
                 '''text-generation'''   ,						model=self.model_name   ,						model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa}   ,						max_new_tokens=self.MAX_NEW_TOKENS   ,						)

             # Real second forward pass
             lowercase__      			= self.pipe(self.input_text )
             self.assertIn(pipeline_output[0]['''generated_text''']   ,						self.EXPECTED_OUTPUTS )






@require_torch_multi_gpu
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             super().setUp()






      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''balanced''' )

             # Check correct device map
             self.assertEqual(set(model_parallel.hf_device_map.values() )   ,						{0, 1} )

             # Check that inference pass works on the model
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )

             # Second real batch
             lowercase__      			= model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )
             self.assertIn(self.tokenizer.decode(output_parallel[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= '''facebook/opt-350m'''
             super().setUp()






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
                    return

             # Step 1: freeze all parameters
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase )

             self.assertEqual(set(model.hf_device_map.values() )   ,						{torch.cuda.current_device()} )

             for param in model.parameters():
                    lowercase__      			= False  # freeze the model - train adapters later
                    if param.ndim == 1:
                           # cast the small parameters (e.g. layernorm) to fp32 for stability
                           lowercase__      			= param.data.to(torch.floataa )

        # Step 2: add adapters
             for _, module in model.named_modules():
                    if "OPTAttention" in repr(type(UpperCamelCase ) ):
                           lowercase__      			= LoRALayer(module.q_proj   ,						rank=16 )
                           lowercase__      			= LoRALayer(module.k_proj   ,						rank=16 )
                           lowercase__      			= LoRALayer(module.v_proj   ,						rank=16 )

        # Step 3: dummy batch
             lowercase__      			= self.tokenizer('''Test batch '''   ,						return_tensors='''pt''' ).to(0 )

             # Step 4: Check if the gradient is not None
             with torch.cuda.amp.autocast():
                    lowercase__      			= model.forward(**UpperCamelCase )
                    out.logits.norm().backward()

             for module in model.modules():
                    if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                           self.assertTrue(module.adapter[1].weight.grad is not None )
                           self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
                    elif isinstance(UpperCamelCase   ,						nn.Embedding ):
                           self.assertTrue(module.weight.grad is None )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[Any]  					=							"""gpt2-xl"""
      lowerCAmelCase__					:     Dict  					=							3.3_191_854_854_152_187











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__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "cpu",		__magic_name__ = "openai/clip-vit-large-patch14"							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      device
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizerFast.from_pretrained(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      [0.4814_5466, 0.457_8275, 0.4082_1073]
       UpperCamelCase__							:				Union[str, Any]          =      [0.2686_2954, 0.2613_0258, 0.2757_7711]
       UpperCamelCase__							:				Dict          =      torchvision.transforms.Normalize(self.image_mean,		self.image_std							)
       UpperCamelCase__							:				List[str]          =      torchvision.transforms.Resize(224							)
       UpperCamelCase__							:				Union[str, Any]          =      torchvision.transforms.CenterCrop(224							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.resize(__magic_name__							)
       UpperCamelCase__							:				Dict          =      self.center_crop(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.normalize(__magic_name__							)
       return images




   def __call__(    self,		__magic_name__=None,		__magic_name__=None,		**__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.tokenizer(text=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.preprocess_img(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      {key: value.to(self.device							) for (key, value) in encoding.items()}
       return encoding






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__=10,		__magic_name__=0.01,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=False,		__magic_name__=True,		__magic_name__="image",		__magic_name__=True,		__magic_name__=False,		__magic_name__=False,		__magic_name__=False,		) ->      None:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Dict          =      None
       UpperCamelCase__							:				Tuple          =      device if device else get_device()
       if vqgan:
           UpperCamelCase__							:				Union[str, Any]          =      vqgan
       else:
           UpperCamelCase__							:				Any          =      load_vqgan(self.device,		conf_path=__magic_name__,		ckpt_path=__magic_name__							)
       self.vqgan.eval()
       if clip:
           UpperCamelCase__							:				Optional[Any]          =      clip
       else:
           UpperCamelCase__							:				Any          =      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''							)
       self.clip.to(self.device							)
       UpperCamelCase__							:				str          =      ProcessorGradientFlow(device=self.device							)

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




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None,		__magic_name__=5,		__magic_name__=True							) ->      Optional[Any]:






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




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None							) ->      Any:






       """simple docstring"""
       if not (path or img):
           raise ValueError('''Input either path or tensor'''							)
       if img is not None:
           raise NotImplementedError
       UpperCamelCase__							:				List[Any]          =      preprocess(Image.open(__magic_name__							),		target_image_size=256							).to(self.device							)
       UpperCamelCase__							:				str          =      preprocess_vqgan(__magic_name__							)
       UpperCamelCase__   ,*UpperCamelCase__							:				Union[str, Any]          =      self.vqgan.encode(__magic_name__							)
       return z




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.latent.detach().requires_grad_()
       UpperCamelCase__							:				Any          =      base_latent + transform_vector
       if self.quantize:
           UpperCamelCase__   ,*UpperCamelCase__							:				int          =      self.vqgan.quantize(__magic_name__							)
       else:
           UpperCamelCase__							:				Optional[int]          =      trans_latent
       return self.vqgan.decode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__=None							) ->      Tuple:






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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self._get_clip_similarity(pos_prompts['''prompts'''],		__magic_name__,		weights=(1 / pos_prompts['''weights'''])							)
       if neg_prompts:
           UpperCamelCase__							:				Tuple          =      self._get_clip_similarity(neg_prompts['''prompts'''],		__magic_name__,		weights=neg_prompts['''weights''']							)
       else:
           UpperCamelCase__							:				Optional[int]          =      torch.tensor([1],		device=self.device							)
       UpperCamelCase__							:				Tuple          =      -torch.log(__magic_name__							) + torch.log(__magic_name__							)
       return loss




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      torch.randn_like(self.latent,		requires_grad=__magic_name__,		device=self.device							)
       UpperCamelCase__							:				Optional[int]          =      torch.optim.Adam([vector],		lr=self.lr							)

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




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






       """simple docstring"""
       wandb.init(reinit=__magic_name__,		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__							:				List[str]          =      Image.open(__magic_name__							)
           UpperCamelCase__							:				List[Any]          =      image.resize((256, 256)							)
           wandb.log('''Original Image''',		wandb.Image(__magic_name__							)							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None,		__magic_name__=None,		__magic_name__=True,		__magic_name__=False,		__magic_name__=True,		__magic_name__=True,		__magic_name__=None,		) ->      str:






       """simple docstring"""
       if image_path:
           UpperCamelCase__							:				Union[str, Any]          =      self._get_latent(__magic_name__							)
       else:
           UpperCamelCase__							:				Dict          =      torch.randn(self.latent_dim,		device=self.device							)
       if self.log:
           self._init_logging(__magic_name__,		__magic_name__,		__magic_name__							)

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

       UpperCamelCase__							:				str          =      self.vqgan.decode(self.latent							)[0]
       if show_intermediate:
           print('''Original Image'''							)
           show_pil(custom_to_pil(__magic_name__							)							)

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

