






"""simple docstring"""


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True     ,      a__="pt" ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			{"""add_prefix_space""": True} if isinstance(a__     ,      a__ ) and not line.startswith(' ' ) else {}
						lowerCAmelCase   :Dict						=			padding_side
						return tokenizer(
						    [line]     ,      max_length=a__     ,      padding='max_length' if pad_to_max_length else None     ,      truncation=a__     ,      return_tensors=a__     ,      add_special_tokens=a__     ,      **a__     ,      )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__=None     ,      ):





						'''simple docstring'''






						lowerCAmelCase   :Union[str, Any]						=			input_ids.ne(a__ ).any(dim=0 )
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class 						__UpperCamelCase     (     UpperCamelCase__						):
					def __init__(							self							:	Tuple     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Dict="train"     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	Union[str, Any]=None     , UpperCAmelCase							:	Tuple=None     , UpperCAmelCase							:	List[str]=""     , )	->       Tuple:
											super().__init__()
											lowerCAmelCase   :List[str]						=			Path(__snake_case   ).joinpath(type_path + '.source'   )
											lowerCAmelCase   :Tuple						=			Path(__snake_case   ).joinpath(type_path + '.target'   )
											lowerCAmelCase   :int						=			self.get_char_lens(self.src_file   )
											lowerCAmelCase   :Optional[Any]						=			max_source_length
											lowerCAmelCase   :int						=			max_target_length
											assert min(self.src_lens   ) > 0, f"""found empty line in {self.src_file}"""
											lowerCAmelCase   :Any						=			tokenizer
											lowerCAmelCase   :str						=			prefix
											if n_obs is not None:
																	lowerCAmelCase   :Union[str, Any]						=			self.src_lens[:n_obs]
											lowerCAmelCase   :Dict						=			src_lang
											lowerCAmelCase   :List[str]						=			tgt_lang
					def __len__(							self							:	Tuple   )	->       Union[str, Any]:
											return len(self.src_lens   )
					def __getitem__(							self							:	int     , UpperCAmelCase							:	List[Any]   )	->       List[str]:
											lowerCAmelCase   :str						=			index + 1  # linecache starts at 1
											lowerCAmelCase   :List[Any]						=			self.prefix + linecache.getline(str(self.src_file   )     , __snake_case   ).rstrip('\n'   )
											lowerCAmelCase   :Tuple						=			linecache.getline(str(self.tgt_file   )     , __snake_case   ).rstrip('\n'   )
											assert source_line, f"""empty source line for index {index}"""
											assert tgt_line, f"""empty tgt line for index {index}"""

											# Need to add eos token manually for T5
											if isinstance(self.tokenizer     , __snake_case   ):
																	source_line += self.tokenizer.eos_token
																	tgt_line += self.tokenizer.eos_token

											# Pad source and target to the right
											lowerCAmelCase   :Dict						=			(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer     , __snake_case   ) else self.tokenizer
											)
											lowerCAmelCase   :Optional[Any]						=			self.tokenizer.generator if isinstance(self.tokenizer     , __snake_case   ) else self.tokenizer

											lowerCAmelCase   :Union[str, Any]						=			encode_line(__snake_case     , __snake_case     , self.max_source_length     , 'right'   )
											lowerCAmelCase   :str						=			encode_line(__snake_case     , __snake_case     , self.max_target_length     , 'right'   )

											lowerCAmelCase   :Tuple						=			source_inputs["""input_ids"""].squeeze()
											lowerCAmelCase   :Any						=			target_inputs["""input_ids"""].squeeze()
											lowerCAmelCase   :Any						=			source_inputs["""attention_mask"""].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}
					@staticmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	List[str]   )	->       Dict:
											return [len(__snake_case   ) for x in Path(__snake_case   ).open().readlines()]
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	List[str]   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			torch.stack([x['input_ids'] for x in batch]   )
											lowerCAmelCase   :Any						=			torch.stack([x['attention_mask'] for x in batch]   )
											lowerCAmelCase   :Optional[Any]						=			torch.stack([x['decoder_input_ids'] for x in batch]   )
											lowerCAmelCase   :Dict						=			(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer     , __snake_case   )
											    else self.tokenizer.pad_token_id
											)
											lowerCAmelCase   :Any						=			(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer     , __snake_case   )
											    else self.tokenizer.pad_token_id
											)
											lowerCAmelCase   :Tuple						=			trim_batch(__snake_case     , __snake_case   )
											lowerCAmelCase   :Dict						=			trim_batch(__snake_case     , __snake_case     , attention_mask=__snake_case   )
											lowerCAmelCase   :Union[str, Any]						=			{
											    """input_ids""": source_ids,
											    """attention_mask""": source_mask,
											    """decoder_input_ids""": y,
											}
											return batch


__SCREAMING_SNAKE_CASE   =   getLogger(__name__)


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						return list(itertools.chain.from_iterable(a__ ) )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			get_git_info()
						save_json(a__     ,      os.path.join(a__     ,      'git_log.json' ) )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__=4     ,      **a__ ):





						'''simple docstring'''






						with open(a__     ,      'w' ) as f:
												json.dump(a__     ,      a__     ,      indent=a__     ,      **a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						with open(a__ ) as f:
												return json.load(a__ )


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			git.Repo(search_parent_directories=a__ )
						lowerCAmelCase   :Optional[Any]						=			{
						    """repo_id""": str(a__ ),
						    """repo_sha""": str(repo.head.object.hexsha ),
						    """repo_branch""": str(repo.active_branch ),
						    """hostname""": str(socket.gethostname() ),
						}
						return repo_infos


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						return list(map(a__     ,      a__ ) )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						with open(a__     ,      'wb' ) as f:
												return pickle.dump(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						def remove_articles(a__ ):
												return re.sub(R'\b(a|an|the)\b'     ,      ' '     ,      a__ )

						def white_space_fix(a__ ):
												return " ".join(text.split() )

						def remove_punc(a__ ):
												lowerCAmelCase   :Optional[int]						=			set(string.punctuation )
												return "".join(ch for ch in text if ch not in exclude )

						def lower(a__ ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(a__ ) ) ) )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			normalize_answer(a__ ).split()
						lowerCAmelCase   :Tuple						=			normalize_answer(a__ ).split()
						lowerCAmelCase   :Any						=			Counter(a__ ) & Counter(a__ )
						lowerCAmelCase   :Any						=			sum(common.values() )
						if num_same == 0:
												return 0
						lowerCAmelCase   :List[Any]						=			1.0 * num_same / len(a__ )
						lowerCAmelCase   :Union[str, Any]						=			1.0 * num_same / len(a__ )
						lowerCAmelCase   :List[str]						=			(2 * precision * recall) / (precision + recall)
						return fa


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						return normalize_answer(a__ ) == normalize_answer(a__ )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						assert len(a__ ) == len(a__ )
						lowerCAmelCase   :int						=			0
						for hypo, pred in zip(a__     ,      a__ ):
												em += exact_match_score(a__     ,      a__ )
						if len(a__ ) > 0:
												em /= len(a__ )
						return {"em": em}


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						return model_prefix.startswith('rag' )







def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			{p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						lowerCAmelCase   :Any						=			"""dropout_rate"""
						for p in extra_params:
												if getattr(a__     ,      a__     ,      a__ ):
																		if not hasattr(a__     ,      a__ ) and not hasattr(a__     ,      equivalent_param[p] ):
																								logger.info('config doesn\'t have a `{}` attribute'.format(a__ ) )
																								delattr(a__     ,      a__ )
																								continue
																		lowerCAmelCase   :str						=			p if hasattr(a__     ,      a__ ) else equivalent_param[p]
																		setattr(a__     ,      a__     ,      getattr(a__     ,      a__ ) )
																		delattr(a__     ,      a__ )
						return hparams, config





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=OmegaConf.load(lowerCamelCase )
				if display:
								print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None ):
				if conf_path is None:
								__magic_name__						:						List[str]										="""./model_checkpoints/vqgan_only.yaml"""
				__magic_name__						:						Dict										=load_config(lowerCamelCase						,  display=lowerCamelCase )
				__magic_name__						:						Tuple										=VQModel(**config.model.params )
				if ckpt_path is None:
								__magic_name__						:						Optional[Any]										="""./model_checkpoints/vqgan_only.pt"""
				__magic_name__						:						Tuple										=torch.load(lowerCamelCase						,  map_location=lowerCamelCase )
				if ".ckpt" in ckpt_path:
								__magic_name__						:						Any										=sd["""state_dict"""]
				model.load_state_dict(lowerCamelCase						,  strict=lowerCamelCase )
				model.to(lowerCamelCase )
				del sd
				return model




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=model.encode(lowerCamelCase )
				print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
				__magic_name__						:						List[Any]										=model.decode(lowerCamelCase )
				return xrec




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__					,			__magic_name__						:						Optional[int]										=string.rsplit("""."""						,  1 )
				if reload:
								__magic_name__						:						Optional[int]										=importlib.import_module(lowerCamelCase )
								importlib.reload(lowerCamelCase )
				return getattr(importlib.import_module(lowerCamelCase						,  package=lowerCamelCase )						,  cls )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if "target" not in config:
								raise KeyError("""Expected key `target` to instantiate.""" )
				return get_obj_from_str(config["""target"""] )(**config.get("""params"""						,  {} ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True						,  lowerCamelCase=True ):
				__magic_name__						:						str										=instantiate_from_config(lowerCamelCase )
				if sd is not None:
								model.load_state_dict(lowerCamelCase )
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# load the specified checkpoint
				if ckpt:
								__magic_name__						:						str										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
								__magic_name__						:						Any										=pl_sd["""global_step"""]
								print(F"loaded model from global step {global_step}." )
				else:
								__magic_name__						:						List[Any]										={"""state_dict""": None}
								__magic_name__						:						Optional[Any]										=None
				__magic_name__						:						Tuple										=load_model_from_config(config.model						,  pl_sd["""state_dict"""]						,  gpu=lowerCamelCase						,  eval_mode=lowerCamelCase )["""model"""]
				return model, global_step




