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


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

A_	     =   ["names", "prefix"]
A_	     =   ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
A_	     =   ["encoding_errors", "on_bad_lines"]
A_	     =   ["date_format"]






@dataclass
class   __lowercase       (     datasets.BuilderConfig     ):
						lowercase       			=  ","
						lowercase       			=  None
						lowercase       			=  "infer"
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  True
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  False
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  True
						lowercase       			=  True
						lowercase       			=  False
						lowercase       			=  True
						lowercase       			=  None
						lowercase       			=  "."
						lowercase       			=  None
						lowercase       			=  '"'
						lowercase       			=  0
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  None
						lowercase       			=  True
						lowercase       			=  True
						lowercase       			=  0
						lowercase       			=  True
						lowercase       			=  False
						lowercase       			=  None
						lowercase       			=  10000
						lowercase       			=  None
						lowercase       			=  "strict"
						lowercase       			=  "error"
						lowercase       			=  None

						def 							__a		(						self					:   List[str] )				->		Union[str, Any]:


									'''simple docstring'''

									if self.delimiter is not None:
												lowercase       		=		self.delimiter
									if self.column_names is not None:
												lowercase       		=		self.column_names



						@property
						def 							__a		(						self					:   Optional[Any] )				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		{
									    '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()				,				__lowerCamelCase ):
															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   __lowercase       (     datasets.ArrowBasedBuilder     ):
						lowercase       			=  CsvConfig

						def 							__a		(						self					:   int )				->		List[Any]:


									'''simple docstring'''

									return datasets.DatasetInfo(features=self.config.features )

						def 							__a		(						self					:   Tuple				,				__lowerCamelCase					:   List[Any] )				->		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}' )
									lowercase       		=		dl_manager.download_and_extract(self.config.data_files )
									if isinstance(__lowerCamelCase				,				(str, list, tuple) ):
												lowercase       		=		data_files
												if isinstance(__lowerCamelCase				,				__lowerCamelCase ):
															lowercase       		=		[files]
												lowercase       		=		[dl_manager.iter_files(__lowerCamelCase ) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,				gen_kwargs={'''files''': files} )]
									lowercase       		=		[]
									for split_name, files in data_files.items():
												if isinstance(__lowerCamelCase				,				__lowerCamelCase ):
															lowercase       		=		[files]
												lowercase       		=		[dl_manager.iter_files(__lowerCamelCase ) for file in files]
												splits.append(datasets.SplitGenerator(name=__lowerCamelCase				,				gen_kwargs={'''files''': files} ) )
									return splits

						def 							__a		(						self					:   int				,				__lowerCamelCase					:   Optional[Any] )				->		Tuple:


									'''simple docstring'''

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



						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   int )				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		self.config.features.arrow_schema if self.config.features else None
									# dtype allows reading an int column as str
									lowercase       		=		(
									    {
									        name: dtype.to_pandas_dtype() if not require_storage_cast(__lowerCamelCase ) 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(__lowerCamelCase ) ):
												lowercase       		=		pd.read_csv(__lowerCamelCase				,				iterator=__lowerCamelCase				,				dtype=__lowerCamelCase				,				**self.config.pd_read_csv_kwargs )
												try:
															for batch_idx, df in enumerate(__lowerCamelCase ):
																		lowercase       		=		pa.Table.from_pandas(__lowerCamelCase )
																		# 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(__lowerCamelCase )
												except ValueError as e:
															logger.error(f'Failed to read file \'{file}\' with error {type(__lowerCamelCase )}: {e}' )
															raise











'''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 							_a				:





       '''simple docstring'''






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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						device
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTokenizerFast.from_pretrained(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
             SCREAMING_SNAKE_CASE   :      str    					=						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
             SCREAMING_SNAKE_CASE   :      Dict    					=						torchvision.transforms.Normalize(self.image_mean,							self.image_std  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						torchvision.transforms.Resize(224  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torchvision.transforms.CenterCrop(224  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.resize(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.center_crop(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.normalize(A  )
             return images







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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.tokenizer(text=A,							**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.preprocess_img(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						{key: value.to(self.device  ) for (key, value) in encoding.items()}
             return encoding






class 							_a				(			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,							):







             '''simple docstring'''







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

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







       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=5,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[]
             if output_path is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						'./animation.gif'
             if input_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.save_path
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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?)'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						total_duration / len(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[frame_duration] * len(A  )
             if extend_frames:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						1.5
                   SCREAMING_SNAKE_CASE   :      int    					=						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  ):







             '''simple docstring'''







             if not (path or img):
                   raise ValueError('Input either path or tensor'  )
             if img is not None:
                   raise NotImplementedError
             SCREAMING_SNAKE_CASE   :      str    					=						preprocess(Image.open(A  ),							target_image_size=256  ).to(self.device  )
             SCREAMING_SNAKE_CASE   :      Any    					=						preprocess_vqgan(A  )
             SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      Tuple    					=						self.vqgan.encode(A  )
             return z







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.latent.detach().requires_grad_()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						base_latent + transform_vector
             if self.quantize:
                   SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.quantize(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						trans_latent
             return self.vqgan.decode(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.clip_preprocessor(text=A,							images=A,							return_tensors='pt',							padding=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.clip(**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						clip_outputs.logits_per_image
             if weights is not None:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						similarity_logits * weights
             return similarity_logits.sum()







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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

             for i in range(self.iterations  ):
                   optim.zero_grad()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._add_vector(A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						loop_post_process(A  )
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						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  ):







             '''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:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						Image.open(A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						image.resize((256, 256)  )
                   wandb.log('Original Image',							wandb.Image(A  )  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if not prompts:
                   return []
             SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[]
             if isinstance(A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[prompt.strip() for prompt in prompts.split('|'  )]
             for prompt in prompts:
                   if isinstance(A,							(tuple, list)  ):
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						prompt[0]
                         SCREAMING_SNAKE_CASE   :      Any    					=						float(prompt[1]  )
                   elif ":" in prompt:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						prompt.split(':'  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						float(A  )
                   else:
                         SCREAMING_SNAKE_CASE   :      Dict    					=						prompt
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						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,							):







             '''simple docstring'''







             if image_path:
                   SCREAMING_SNAKE_CASE   :      int    					=						self._get_latent(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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."
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.process_prompts(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.process_prompts(A  )
             if save_final and save_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join('./outputs/',							'_'.join(pos_prompts['prompts']  )  )
             if not os.path.exists(A  ):
                   os.makedirs(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						save_path + '_' + get_timestamp()
                   os.makedirs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						save_path

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.decode(self.latent  )[0]
             if show_intermediate:
                   print('Original Image'  )
                   show_pil(custom_to_pil(A  )  )

             SCREAMING_SNAKE_CASE   :      int    					=						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"  )  )


