





'''simple docstring'''






import os

import jsonlines
import numpy as np
from tqdm import tqdm


snake_case_     =	20_48
snake_case_     =	40_96
snake_case_     =	42
snake_case_     =	os.environ.pop("""PROCESS_TRAIN""", """false""")
snake_case_     =	{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[str]:
    def choose_first(UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Dict=False		):
        assert isinstance(UpperCamelCase__    ,       UpperCamelCase__		)
        if len(UpperCamelCase__		) == 1:
            A       :       List[str]							=						answer[0]
            return {k: [answer[k]] for k in answer} if is_long_answer else answer
        for a in answer:
            if is_long_answer:
                A       :       Any							=						{k: [a[k]] for k in a}
            if len(a['''start_token''']		) > 0:
                break
        return a

    A       :       Any							=						{'''id''': example['''id''']}
    A       :       Union[str, Any]							=						example['''annotations''']
    A       :       Union[str, Any]							=						annotation['''yes_no_answer''']
    if 0 in yes_no_answer or 1 in yes_no_answer:
        A       :       Tuple							=						['''yes'''] if 1 in yes_no_answer else ['''no''']
        A       :       Any							=						[]
        A       :       Union[str, Any]							=						[]
        A       :       str							=						['''<cls>''']
    else:
        A       :       Dict							=						['''short''']
        A       :       List[Any]							=						choose_first(annotation['''short_answers''']		)
        if len(out['''start_token''']		) == 0:
            # answer will be long if short is not available
            A       :       Optional[int]							=						['''long''']
            A       :       str							=						choose_first(annotation['''long_answer''']    ,       is_long_answer=UpperCamelCase__		)
            A       :       str							=						[]
        answer.update(UpperCamelCase__		)

    # disregard some samples
    if len(answer['''start_token''']		) > 1 or answer["start_token"] == answer["end_token"]:
        A       :       Union[str, Any]							=						True
    else:
        A       :       str							=						False

    A       :       int							=						['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
    if not all(isinstance(answer[k]    ,       UpperCamelCase__		) for k in cols		):
        raise ValueError('''Issue in ID'''    ,       example['''id''']		)

    return answer
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Tuple=False		)		->  Optional[int]:
    A       :       Dict							=						_get_single_answer(UpperCamelCase__		)
    # bytes are of no use
    del answer["start_byte"]
    del answer["end_byte"]

    # handle yes_no answers explicitly
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        A       :       Optional[Any]							=						example['''document''']['''tokens''']
        A       :       Dict							=						[]
        for i in range(len(doc['''token''']		)		):
            if not doc["is_html"][i]:
                context.append(doc['''token'''][i]		)
        return {
            "context": " ".join(UpperCamelCase__		),
            "answer": {
                "start_token": -100,  # ignore index in cross-entropy
                "end_token": -100,  # ignore index in cross-entropy
                "category": answer["category"],
                "span": answer["category"],  # extra
            },
        }

    # later, help in removing all no answers
    if answer["start_token"] == [-1]:
        return {
            "context": "None",
            "answer": {
                "start_token": -1,
                "end_token": -1,
                "category": "null",
                "span": "None",  # extra
            },
        }

    # handling normal samples

    A       :       List[str]							=						['''start_token''', '''end_token''']
    answer.update({k: answer[k][0] if len(answer[k]		) > 0 else answer[k] for k in cols}		)  # e.g. [10] == 10

    A       :       Tuple							=						example['''document''']['''tokens''']
    A       :       Optional[Any]							=						answer['''start_token''']
    A       :       Any							=						answer['''end_token''']

    A       :       List[str]							=						[]
    for i in range(len(doc['''token''']		)		):
        if not doc["is_html"][i]:
            context.append(doc['''token'''][i]		)
        else:
            if answer["start_token"] > i:
                start_token -= 1
            if answer["end_token"] > i:
                end_token -= 1
    A       :       Any							=						''' '''.join(context[start_token:end_token]		)

    # checking above code
    if assertion:
        A       :       str							=						doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
        A       :       Tuple							=						doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
        A       :       Any							=						''' '''.join([old[i] for i in range(len(UpperCamelCase__		)		) if not is_html[i]]		)
        if new != old:
            print('''ID:'''    ,       example['''id''']		)
            print('''New:'''    ,       UpperCamelCase__    ,       end='''\n'''		)
            print('''Old:'''    ,       UpperCamelCase__    ,       end='''\n\n'''		)

    return {
        "context": " ".join(UpperCamelCase__		),
        "answer": {
            "start_token": start_token,
            "end_token": end_token - 1,  # this makes it inclusive
            "category": answer["category"],  # either long or short
            "span": new,  # extra
        },
    }
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Union[str, Any]=2_048    ,       UpperCamelCase__    : Tuple=4_096    ,       UpperCamelCase__    : Dict=True		)		->  str:
    # overlap will be of doc_stride - q_len

    A       :       Any							=						get_context_and_ans(UpperCamelCase__    ,       assertion=UpperCamelCase__		)
    A       :       Any							=						out['''answer''']

    # later, removing these samples
    if answer["start_token"] == -1:
        return {
            "example_id": example["id"],
            "input_ids": [[-1]],
            "labels": {
                "start_token": [-1],
                "end_token": [-1],
                "category": ["null"],
            },
        }

    A       :       Optional[int]							=						tokenizer(example['''question''']['''text''']    ,       out['''context''']		).input_ids
    A       :       Any							=						input_ids.index(tokenizer.sep_token_id		) + 1

    # return yes/no
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        A       :       Dict							=						[]
        A       :       Tuple							=						[]
        A       :       Optional[Any]							=						input_ids[:q_len]
        A       :       int							=						range(UpperCamelCase__    ,       len(UpperCamelCase__		)    ,       max_length - doc_stride		)
        for i in doc_start_indices:
            A       :       int							=						i + max_length - q_len
            A       :       Dict							=						input_ids[i:end_index]
            inputs.append(q_indices + slice		)
            category.append(answer['''category'''][0]		)
            if slice[-1] == tokenizer.sep_token_id:
                break

        return {
            "example_id": example["id"],
            "input_ids": inputs,
            "labels": {
                "start_token": [-100] * len(UpperCamelCase__		),
                "end_token": [-100] * len(UpperCamelCase__		),
                "category": category,
            },
        }

    A       :       List[str]							=						out['''context'''].split()
    A       :       List[str]							=						splitted_context[answer['''end_token''']]
    A       :       Union[str, Any]							=						len(
        tokenizer(
            ''' '''.join(splitted_context[: answer['''start_token''']]		)    ,       add_special_tokens=UpperCamelCase__    ,       ).input_ids		)
    A       :       Optional[Any]							=						len(
        tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]		)    ,       add_special_tokens=UpperCamelCase__		).input_ids		)

    answer["start_token"] += q_len
    answer["end_token"] += q_len

    # fixing end token
    A       :       Union[str, Any]							=						len(tokenizer(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__		).input_ids		)
    if num_sub_tokens > 1:
        answer["end_token"] += num_sub_tokens - 1

    A       :       List[Any]							=						input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
    A       :       Union[str, Any]							=						answer['''start_token''']
    A       :       Optional[Any]							=						answer['''end_token''']

    if assertion:
        A       :       Tuple							=						tokenizer.decode(UpperCamelCase__		)
        if answer["span"] != new:
            print('''ISSUE IN TOKENIZATION'''		)
            print('''OLD:'''    ,       answer['''span''']		)
            print('''NEW:'''    ,       UpperCamelCase__    ,       end='''\n\n'''		)

    if len(UpperCamelCase__		) <= max_length:
        return {
            "example_id": example["id"],
            "input_ids": [input_ids],
            "labels": {
                "start_token": [answer["start_token"]],
                "end_token": [answer["end_token"]],
                "category": answer["category"],
            },
        }

    A       :       int							=						input_ids[:q_len]
    A       :       Any							=						range(UpperCamelCase__    ,       len(UpperCamelCase__		)    ,       max_length - doc_stride		)

    A       :       Optional[int]							=						[]
    A       :       Dict							=						[]
    A       :       Optional[Any]							=						[]
    A       :       List[str]							=						[]  # null, yes, no, long, short
    for i in doc_start_indices:
        A       :       Optional[int]							=						i + max_length - q_len
        A       :       int							=						input_ids[i:end_index]
        inputs.append(q_indices + slice		)
        assert len(inputs[-1]		) <= max_length, "Issue in truncating length"

        if start_token >= i and end_token <= end_index - 1:
            A       :       Any							=						start_token - i + q_len
            A       :       Dict							=						end_token - i + q_len
            answers_category.append(answer['''category'''][0]		)  # ["short"] -> "short"
        else:
            A       :       Optional[Any]							=						-100
            A       :       Any							=						-100
            answers_category.append('''null'''		)
        A       :       Tuple							=						inputs[-1][start_token : end_token + 1]

        answers_start_token.append(UpperCamelCase__		)
        answers_end_token.append(UpperCamelCase__		)
        if assertion:
            if new != old and new != [tokenizer.cls_token_id]:
                print('''ISSUE in strided for ID:'''    ,       example['''id''']		)
                print('''New:'''    ,       tokenizer.decode(UpperCamelCase__		)		)
                print('''Old:'''    ,       tokenizer.decode(UpperCamelCase__		)    ,       end='''\n\n'''		)
        if slice[-1] == tokenizer.sep_token_id:
            break

    return {
        "example_id": example["id"],
        "input_ids": inputs,
        "labels": {
            "start_token": answers_start_token,
            "end_token": answers_end_token,
            "category": answers_category,
        },
    }
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : str=2_048    ,       UpperCamelCase__    : str=4_096    ,       UpperCamelCase__    : List[str]=False		)		->  List[str]:
    A       :       List[str]							=						get_strided_contexts_and_ans(
        UpperCamelCase__    ,       UpperCamelCase__    ,       doc_stride=UpperCamelCase__    ,       max_length=UpperCamelCase__    ,       assertion=UpperCamelCase__    ,       )

    return example
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any		)		->  Any:
    with jsonlines.open(UpperCamelCase__    ,       '''a'''		) as writer:
        for example in tqdm(UpperCamelCase__    ,       total=len(UpperCamelCase__		)    ,       desc='''Saving samples ... '''		):
            A       :       Optional[int]							=						example['''labels''']
            for ids, start, end, cat in zip(
                example['''input_ids''']    ,       labels['''start_token''']    ,       labels['''end_token''']    ,       labels['''category''']    ,       ):
                if start == -1 and end == -1:
                    continue  # leave waste samples with no answer
                if cat == "null" and np.random.rand() < 0.6:
                    continue  # removing 50 % samples
                writer.write(
                    {
                        '''input_ids''': ids,
                        '''start_token''': start,
                        '''end_token''': end,
                        '''category''': CATEGORY_MAPPING[cat],
                    }		)


if __name__ == "__main__":
     from datasets import load_dataset

     from transformers import BigBirdTokenizer

     snake_case_     =	load_dataset("""natural_questions""")
     snake_case_     =	BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

     snake_case_     =	data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

     snake_case_     =	{
         """tokenizer""": tokenizer,
         """doc_stride""": DOC_STRIDE,
         """max_length""": MAX_LENGTH,
         """assertion""": False,
     }
     snake_case_     =	data.map(prepare_inputs, fn_kwargs=fn_kwargs)
     snake_case_     =	data.remove_columns(["""annotations""", """document""", """id""", """question"""])
     print(data)

     np.random.seed(SEED)
     snake_case_     =	"""nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
     save_to_disk(data, file_name=cache_file_name)







'''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'''					)					)
