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  UpperCamelCase				( datasets.BuilderConfig ):

     '''simple docstring'''

     A_										=  ','
     A_										=  None
     A_										=  'infer'
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  True
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  False
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  True
     A_										=  True
     A_										=  False
     A_										=  True
     A_										=  None
     A_										=  '.'
     A_										=  None
     A_										=  '"'
     A_										=  0
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  None
     A_										=  True
     A_										=  True
     A_										=  0
     A_										=  True
     A_										=  False
     A_										=  None
     A_										=  10_000
     A_										=  None
     A_										=  'strict'
     A_										=  'error'
     A_										=  None



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           if self.delimiter is not None:
                 _lowerCamelCase			    =				self.delimiter
           if self.column_names is not None:
                 _lowerCamelCase			    =				self.column_names





     @property
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				{
               "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  UpperCamelCase				( datasets.ArrowBasedBuilder ):

     '''simple docstring'''

     A_										=  CsvConfig



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return datasets.DatasetInfo(features=self.config.features						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           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}'						)
           _lowerCamelCase			    =				dl_manager.download_and_extract(self.config.data_files						)
           if isinstance(A_				,				(str, list, tuple)						):
                 _lowerCamelCase			    =				data_files
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase			    =				[files]
                 _lowerCamelCase			    =				[dl_manager.iter_files(A_						) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,				gen_kwargs={'''files''': files}						)]
           _lowerCamelCase			    =				[]
           for split_name, files in data_files.items():
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase			    =				[files]
                 _lowerCamelCase			    =				[dl_manager.iter_files(A_						) for file in files]
                 splits.append(datasets.SplitGenerator(name=A_				,				gen_kwargs={'''files''': files}						)						)
           return splits



     def 		UpperCamelCase_						(					self				,				A_						) ->				pa.Table:






           """simple docstring"""
           if self.config.features is not None:
                 _lowerCamelCase			    =				self.config.features.arrow_schema
                 if all(not require_storage_cast(A_						) for feature in self.config.features.values()						):
                       # cheaper cast
                       _lowerCamelCase			    =				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
                       _lowerCamelCase			    =				table_cast(A_				,				A_						)
           return pa_table





     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.config.features.arrow_schema if self.config.features else None
           # dtype allows reading an int column as str
           _lowerCamelCase			    =				(
               {
                   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_						)						):
                 _lowerCamelCase			    =				pd.read_csv(A_				,				iterator=A_				,				dtype=A_				,				**self.config.pd_read_csv_kwargs						)
                 try:
                       for batch_idx, df in enumerate(A_						):
                             _lowerCamelCase			    =				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
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  UpperCamelCase				:

     '''simple docstring'''

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






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _lowerCamelCase			    =				torchvision.transforms.Normalize(self.image_mean				,				self.image_std						)
           _lowerCamelCase			    =				torchvision.transforms.Resize(2_24						)
           _lowerCamelCase			    =				torchvision.transforms.CenterCrop(2_24						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(text=A_				,				**A_						)
           _lowerCamelCase			    =				self.preprocess_img(A_						)
           _lowerCamelCase			    =				{key: value.to(self.device						) for (key, value) in encoding.items()}
           return encoding





class  UpperCamelCase				( nn.Module ):

     '''simple docstring'''

     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				,				) ->				None:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				device if device else get_device()
           if vqgan:
                 _lowerCamelCase			    =				vqgan
           else:
                 _lowerCamelCase			    =				load_vqgan(self.device				,				conf_path=A_				,				ckpt_path=A_						)
           self.vqgan.eval()
           if clip:
                 _lowerCamelCase			    =				clip
           else:
                 _lowerCamelCase			    =				CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''						)
           self.clip.to(self.device						)
           _lowerCamelCase			    =				ProcessorGradientFlow(device=self.device						)

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



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None				,				A_=5				,				A_=True						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           if output_path is None:
                 _lowerCamelCase			    =				'''./animation.gif'''
           if input_path is None:
                 _lowerCamelCase			    =				self.save_path
           _lowerCamelCase			    =				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?)'''						)
           _lowerCamelCase			    =				total_duration / len(A_						)
           _lowerCamelCase			    =				[frame_duration] * len(A_						)
           if extend_frames:
                 _lowerCamelCase			    =				1.5
                 _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_=None				,				A_=None						) ->				Union[str, Any]:






           """simple docstring"""
           if not (path or img):
                 raise ValueError('''Input either path or tensor'''						)
           if img is not None:
                 raise NotImplementedError
           _lowerCamelCase			    =				preprocess(Image.open(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.latent.detach().requires_grad_()
           _lowerCamelCase			    =				base_latent + transform_vector
           if self.quantize:
                 _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.quantize(A_						)
           else:
                 _lowerCamelCase			    =				trans_latent
           return self.vqgan.decode(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.clip_preprocessor(text=A_				,				images=A_				,				return_tensors='''pt'''				,				padding=A_						)
           _lowerCamelCase			    =				self.clip(**A_						)
           _lowerCamelCase			    =				clip_outputs.logits_per_image
           if weights is not None:
                 _lowerCamelCase			    =				similarity_logits * weights
           return similarity_logits.sum()



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self._get_clip_similarity(pos_prompts['''prompts''']				,				A_				,				weights=(1 / pos_prompts['''weights'''])						)
           if neg_prompts:
                 _lowerCamelCase			    =				self._get_clip_similarity(neg_prompts['''prompts''']				,				A_				,				weights=neg_prompts['''weights''']						)
           else:
                 _lowerCamelCase			    =				torch.tensor([1]				,				device=self.device						)
           _lowerCamelCase			    =				-torch.log(A_						) + torch.log(A_						)
           return loss



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				torch.randn_like(self.latent				,				requires_grad=A_				,				device=self.device						)
           _lowerCamelCase			    =				torch.optim.Adam([vector]				,				lr=self.lr						)

           for i in range(self.iterations						):
                 optim.zero_grad()
                 _lowerCamelCase			    =				self._add_vector(A_						)
                 _lowerCamelCase			    =				loop_post_process(A_						)
                 _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           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:
                 _lowerCamelCase			    =				Image.open(A_						)
                 _lowerCamelCase			    =				image.resize((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






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





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=True				,				A_=False				,				A_=True				,				A_=True				,				A_=None				,				) ->				str:






           """simple docstring"""
           if image_path:
                 _lowerCamelCase			    =				self._get_latent(A_						)
           else:
                 _lowerCamelCase			    =				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."
           _lowerCamelCase			    =				self.process_prompts(A_						)
           _lowerCamelCase			    =				self.process_prompts(A_						)
           if save_final and save_path is None:
                 _lowerCamelCase			    =				os.path.join('''./outputs/'''				,				'''_'''.join(pos_prompts['''prompts''']						)						)
           if not os.path.exists(A_						):
                 os.makedirs(A_						)
           else:
                 _lowerCamelCase			    =				save_path + '''_''' + get_timestamp()
                 os.makedirs(A_						)
           _lowerCamelCase			    =				save_path

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

           _lowerCamelCase			    =				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'						)						)