


'''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  ,					a = "cpu"  ,					a = "openai/clip-vit-large-patch14"    ):
          UpperCamelCase__										=				device
          UpperCamelCase__										=				CLIPTokenizerFast.from_pretrained(a    )
          UpperCamelCase__										=				[0.4814_5466, 0.457_8275, 0.4082_1073]
          UpperCamelCase__										=				[0.2686_2954, 0.2613_0258, 0.2757_7711]
          UpperCamelCase__										=				torchvision.transforms.Normalize(self.image_mean  ,					self.image_std    )
          UpperCamelCase__										=				torchvision.transforms.Resize(2_24    )
          UpperCamelCase__										=				torchvision.transforms.CenterCrop(2_24    )


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				self.resize(a    )
          UpperCamelCase__										=				self.center_crop(a    )
          UpperCamelCase__										=				self.normalize(a    )
          return images


    def __call__(     self  ,					a=None  ,					a=None  ,					**a    ):
          UpperCamelCase__										=				self.tokenizer(text=a  ,					**a    )
          UpperCamelCase__										=				self.preprocess_img(a    )
          UpperCamelCase__										=				{key: value.to(self.device    ) for (key, value) in encoding.items()}
          return encoding

class 					lowercase_     (    nn.Module   ):


    def __init__(     self  ,					a=10  ,					a=0.01  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=False  ,					a=True  ,					a="image"  ,					a=True  ,					a=False  ,					a=False  ,					a=False  ,					):
          super().__init__()
          UpperCamelCase__										=				None
          UpperCamelCase__										=				device if device else get_device()
          if vqgan:
                UpperCamelCase__										=				vqgan
          else:
                UpperCamelCase__										=				load_vqgan(self.device  ,					conf_path=a  ,					ckpt_path=a    )
          self.vqgan.eval()
          if clip:
                UpperCamelCase__										=				clip
          else:
                UpperCamelCase__										=				CLIPModel.from_pretrained("openai/clip-vit-base-patch32"    )
          self.clip.to(self.device    )
          UpperCamelCase__										=				ProcessorGradientFlow(device=self.device    )

          UpperCamelCase__										=				iterations
          UpperCamelCase__										=				lr
          UpperCamelCase__										=				log
          UpperCamelCase__										=				make_grid
          UpperCamelCase__										=				return_val
          UpperCamelCase__										=				quantize
          UpperCamelCase__										=				self.vqgan.decoder.z_shape


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


    def    __a							(     self  ,					a=None  ,					a=None    ):
          if not (path or img):
                raise ValueError("Input either path or tensor"    )
          if img is not None:
                raise NotImplementedError
          UpperCamelCase__										=				preprocess(Image.open(a    )  ,					target_image_size=2_56    ).to(self.device    )
          UpperCamelCase__										=				preprocess_vqgan(a    )
          UpperCamelCase__					,	*UpperCamelCase__										=				self.vqgan.encode(a    )
          return z


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				self.latent.detach().requires_grad_()
          UpperCamelCase__										=				base_latent + transform_vector
          if self.quantize:
                UpperCamelCase__					,	*UpperCamelCase__										=				self.vqgan.quantize(a    )
          else:
                UpperCamelCase__										=				trans_latent
          return self.vqgan.decode(a    )


    def    __a							(     self  ,					a  ,					a  ,					a=None    ):
          UpperCamelCase__										=				self.clip_preprocessor(text=a  ,					images=a  ,					return_tensors="pt"  ,					padding=a    )
          UpperCamelCase__										=				self.clip(**a    )
          UpperCamelCase__										=				clip_outputs.logits_per_image
          if weights is not None:
                UpperCamelCase__										=				similarity_logits * weights
          return similarity_logits.sum()


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				self._get_clip_similarity(pos_prompts["prompts"]  ,					a  ,					weights=(1 / pos_prompts["weights"])    )
          if neg_prompts:
                UpperCamelCase__										=				self._get_clip_similarity(neg_prompts["prompts"]  ,					a  ,					weights=neg_prompts["weights"]    )
          else:
                UpperCamelCase__										=				torch.tensor([1]  ,					device=self.device    )
          UpperCamelCase__										=				-torch.log(a    ) + torch.log(a    )
          return loss


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				torch.randn_like(self.latent  ,					requires_grad=a  ,					device=self.device    )
          UpperCamelCase__										=				torch.optim.Adam([vector]  ,					lr=self.lr    )

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


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          wandb.init(reinit=a  ,					project="face-editor"    )
          wandb.config.update({"Positive Prompts": positive_prompts}    )
          wandb.config.update({"Negative Prompts": negative_prompts}    )
          wandb.config.update({"lr": self.lr, "iterations": self.iterations}    )
          if image_path:
                UpperCamelCase__										=				Image.open(a    )
                UpperCamelCase__										=				image.resize((2_56, 2_56)    )
                wandb.log("Original Image"  ,					wandb.Image(a    )    )


    def    __a							(     self  ,					a    ):
          if not prompts:
                return []
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				[]
          if isinstance(a  ,					a    ):
                UpperCamelCase__										=				[prompt.strip() for prompt in prompts.split("|"    )]
          for prompt in prompts:
                if isinstance(a  ,					(tuple, list)    ):
                      UpperCamelCase__										=				prompt[0]
                      UpperCamelCase__										=				float(prompt[1]    )
                elif ":" in prompt:
                      UpperCamelCase__					,	UpperCamelCase__										=				prompt.split(":"    )
                      UpperCamelCase__										=				float(a    )
                else:
                      UpperCamelCase__										=				prompt
                      UpperCamelCase__										=				1.0
                processed_prompts.append(a    )
                weights.append(a    )
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(a  ,					device=self.device    ),
          }


    def    __a							(     self  ,					a  ,					a=None  ,					a=None  ,					a=True  ,					a=False  ,					a=True  ,					a=True  ,					a=None  ,					):
          if image_path:
                UpperCamelCase__										=				self._get_latent(a    )
          else:
                UpperCamelCase__										=				torch.randn(self.latent_dim  ,					device=self.device    )
          if self.log:
                self._init_logging(a  ,					a  ,					a    )

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

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

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








import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


snake_case_			  = datasets.utils.logging.get_logger(__name__)

snake_case_			  = ['''names''', '''prefix''']
snake_case_			  = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
snake_case_			  = ['''encoding_errors''', '''on_bad_lines''']
snake_case_			  = ['''date_format''']


@dataclass
class       SCREAMING_SNAKE_CASE__					(datasets.BuilderConfig     ):
    __lowerCamelCase						:       str														=     ","
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[Union[int, List[int], str]]														=     "infer"
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[Union[int, str, List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[Union[List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[Literal["c", "python", "pyarrow"]]														=     None
    __lowerCamelCase						:       Dict[Union[int, str], Callable[[Any], Any]]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[Union[int, List[int]]]														=     None
    __lowerCamelCase						:       Optional[int]														=     None
    __lowerCamelCase						:       Optional[Union[str, List[str]]]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     "."
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     '"'
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       int														=     1_0000
    __lowerCamelCase						:       Optional[datasets.Features]														=     None
    __lowerCamelCase						:       Optional[str]														=     "strict"
    __lowerCamelCase						:       Literal["error", "warn", "skip"]														=     "error"
    __lowerCamelCase						:       Optional[str]														=     None





    def       snake_case_				(      self):
         if self.delimiter is not None:
              lowercase__    :   List[Any]      =       self.delimiter
         if self.column_names is not None:
              lowercase__    :   Optional[int]      =       self.column_names





    @property
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       {
             'sep': self.sep,
             'header': self.header,
             'names': self.names,
             'index_col': self.index_col,
             'usecols': self.usecols,
             'prefix': self.prefix,
             'mangle_dupe_cols': self.mangle_dupe_cols,
             'engine': self.engine,
             'converters': self.converters,
             'true_values': self.true_values,
             'false_values': self.false_values,
             'skipinitialspace': self.skipinitialspace,
             'skiprows': self.skiprows,
             'nrows': self.nrows,
             'na_values': self.na_values,
             'keep_default_na': self.keep_default_na,
             'na_filter': self.na_filter,
             'verbose': self.verbose,
             'skip_blank_lines': self.skip_blank_lines,
             'thousands': self.thousands,
             'decimal': self.decimal,
             'lineterminator': self.lineterminator,
             'quotechar': self.quotechar,
             'quoting': self.quoting,
             'escapechar': self.escapechar,
             'comment': self.comment,
             'encoding': self.encoding,
             'dialect': self.dialect,
             'error_bad_lines': self.error_bad_lines,
             'warn_bad_lines': self.warn_bad_lines,
             'skipfooter': self.skipfooter,
             'doublequote': self.doublequote,
             'memory_map': self.memory_map,
             'float_precision': self.float_precision,
             'chunksize': self.chunksize,
             'encoding_errors': self.encoding_errors,
             'on_bad_lines': self.on_bad_lines,
             'date_format': self.date_format,
         }

         # some kwargs must not be passed if they don't have a default value
         # some others are deprecated and we can also not pass them if they are the default value
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
              if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,   a):
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 2):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

         return pd_read_csv_kwargs



class       SCREAMING_SNAKE_CASE__					(datasets.ArrowBasedBuilder     ):
    __lowerCamelCase						:       Optional[Any]														=     CsvConfig





    def       snake_case_				(      self):
         return datasets.DatasetInfo(features=self.config.features)





    def       snake_case_				(      self						,   a):
         if not self.config.data_files:
              raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""")
         lowercase__    :   Any      =       dl_manager.download_and_extract(self.config.data_files)
         if isinstance(a						,   (str, list, tuple)):
              lowercase__    :   List[str]      =       data_files
              if isinstance(a						,   a):
                   lowercase__    :   Optional[Any]      =       [files]
              lowercase__    :   Optional[int]      =       [dl_manager.iter_files(a) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,   gen_kwargs={'files': files})]
         lowercase__    :   int      =       []
         for split_name, files in data_files.items():
              if isinstance(a						,   a):
                   lowercase__    :   Optional[int]      =       [files]
              lowercase__    :   Tuple      =       [dl_manager.iter_files(a) for file in files]
              splits.append(datasets.SplitGenerator(name=a						,   gen_kwargs={'files': files}))
         return splits





    def       snake_case_				(      self						,   a):
         if self.config.features is not None:
              lowercase__    :   Optional[int]      =       self.config.features.arrow_schema
              if all(not require_storage_cast(a) for feature in self.config.features.values()):
                   # cheaper cast
                   lowercase__    :   Dict      =       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,   schema=a)
              else:
                   # more expensive cast; allows str <-> int/float or str to Audio for example
                   lowercase__    :   Optional[Any]      =       table_cast(a						,   a)
         return pa_table





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         lowercase__    :   Optional[int]      =       (
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(a) else object
                 for name, dtype, feature in zip(schema.names						,   schema.types						,   self.config.features.values())
             }
             if schema is not None
             else None
         )
         for file_idx, file in enumerate(itertools.chain.from_iterable(a)):
              lowercase__    :   int      =       pd.read_csv(a						,   iterator=a						,   dtype=a						,   **self.config.pd_read_csv_kwargs)
              try:
                   for batch_idx, df in enumerate(a):
                        lowercase__    :   List[str]      =       pa.Table.from_pandas(a)
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                        yield (file_idx, batch_idx), self._cast_table(a)
              except ValueError as e:
                   logger.error(f"""Failed to read file '{file}' with error {type(a)}: {e}""")
                   raise

