



import argparse
from collections import defaultdict

import yaml


a_  :Any     = '''docs/source/en/_toctree.yml'''
def 			lowercase_				(A					:					Tuple ):
       snake_case__				:      Tuple        =      defaultdict(__a )
       snake_case__				:      Optional[Any]        =      []
       snake_case__				:      Optional[Any]        =      []
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']} )
              else:
                     new_doc_list.append(__a )

       snake_case__				:      Tuple        =      new_doc_list
       snake_case__				:      List[str]        =      [key for key, value in counts.items() if value > 1]

       snake_case__				:      Optional[int]        =      []
       for duplicate_key in duplicates:
              snake_case__				:      Dict        =      list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
              if len(__a ) > 1:
                     raise ValueError(
                         F'''{duplicate_key} is present several times in the documentation table of content at '''
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.' )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]} )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
       snake_case__				:      Union[str, Any]        =      sorted(__a						,   key=lambda A : s["title"].lower() )

       # "overview" gets special treatment and is always first
       if len(__a ) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )

       overview_doc.extend(__a )

       # Sort
       return overview_doc
def 			lowercase_				(A					:					str=False ):





       with open(__a						,   encoding='utf-8' ) as f:
              snake_case__				:      Any        =      yaml.safe_load(f.read() )

       # Get to the API doc
       snake_case__				:      str        =      0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       snake_case__				:      List[str]        =      content[api_idx]['sections']

       # Then to the model doc
       snake_case__				:      Optional[int]        =      0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       snake_case__				:      int        =      api_doc[scheduler_idx]['sections']
       snake_case__				:      List[Any]        =      clean_doc_toc(__a )

       snake_case__				:      str        =      False
       if new_scheduler_doc != scheduler_doc:
              snake_case__				:      List[Any]        =      True
              if overwrite:
                     snake_case__				:      str        =      new_scheduler_doc

       if diff:
              if overwrite:
                     snake_case__				:      Optional[int]        =      api_doc
                     with open(__a						,   'w'						,   encoding='utf-8' ) as f:
                            f.write(yaml.dump(__a						,   allow_unicode=__a ) )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def 			lowercase_				(A					:					List[Any]=False ):





       with open(__a						,   encoding='utf-8' ) as f:
              snake_case__				:      List[str]        =      yaml.safe_load(f.read() )

       # Get to the API doc
       snake_case__				:      Optional[Any]        =      0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       snake_case__				:      str        =      content[api_idx]['sections']

       # Then to the model doc
       snake_case__				:      Tuple        =      0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       snake_case__				:      List[Any]        =      False
       snake_case__				:      Any        =      api_doc[pipeline_idx]['sections']
       snake_case__				:      List[Any]        =      []

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     snake_case__				:      str        =      pipeline_doc['section']
                     snake_case__				:      Union[str, Any]        =      clean_doc_toc(__a )
                     if overwrite:
                            snake_case__				:      str        =      new_sub_pipeline_doc
              new_pipeline_docs.append(__a )

       # sort overall pipeline doc
       snake_case__				:      Optional[int]        =      clean_doc_toc(__a )

       if new_pipeline_docs != pipeline_docs:
              snake_case__				:      List[str]        =      True
              if overwrite:
                     snake_case__				:      Optional[Any]        =      new_pipeline_docs

       if diff:
              if overwrite:
                     snake_case__				:      Optional[Any]        =      api_doc
                     with open(__a						,   'w'						,   encoding='utf-8' ) as f:
                            f.write(yaml.dump(__a						,   allow_unicode=__a ) )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )


if __name__ == "__main__":
 a_  :Optional[Any]     = argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 a_  :List[str]     = parser.parse_args()

 check_scheduler_doc(args.fix_and_overwrite)
 check_pipeline_doc(args.fix_and_overwrite)








"""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 				A_  (__a					, __a					, __a					, __a					, __a=True					, __a="pt"					):

					'''simple docstring'''





					A_								=    {"add_prefix_space": True} if isinstance(__a					, __a					) and not line.startswith(" "					) else {}
					A_								=    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 				A_  (__a					, __a					, __a=None					, ):

					'''simple docstring'''





					A_								=    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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			def __init__(					self    :       Optional[Any]							, _snake_case    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       Dict="train"							, _snake_case    :       List[Any]=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Any=""							, )   ->			List[str]:







								"""simple docstring"""
								super().__init__()
								A_								=    Path(_snake_case						).joinpath(type_path + ".source"						)
								A_								=    Path(_snake_case						).joinpath(type_path + ".target"						)
								A_								=    self.get_char_lens(self.src_file						)
								A_								=    max_source_length
								A_								=    max_target_length
								assert min(self.src_lens						) > 0, F'found empty line in {self.src_file}'
								A_								=    tokenizer
								A_								=    prefix
								if n_obs is not None:
													A_								=    self.src_lens[:n_obs]
								A_								=    src_lang
								A_								=    tgt_lang




			def __len__(					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								return len(self.src_lens						)




			def __getitem__(					self    :       str							, _snake_case    :       Optional[int]						)   ->			Dict[str, torch.Tensor]:







								"""simple docstring"""
								A_								=    index + 1  # linecache starts at 1
								A_								=    self.prefix + linecache.getline(str(self.src_file						)							, _snake_case						).rstrip("\n"						)
								A_								=    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
								A_								=    (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer							, _snake_case						) else self.tokenizer
								)
								A_								=    self.tokenizer.generator if isinstance(self.tokenizer							, _snake_case						) else self.tokenizer

								A_								=    encode_line(_snake_case							, _snake_case							, self.max_source_length							, "right"						)
								A_								=    encode_line(_snake_case							, _snake_case							, self.max_target_length							, "right"						)

								A_								=    source_inputs["input_ids"].squeeze()
								A_								=    target_inputs["input_ids"].squeeze()
								A_								=    source_inputs["attention_mask"].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}




			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       List[Any]						)   ->			str:







								"""simple docstring"""
								return [len(_snake_case						) for x in Path(_snake_case						).open().readlines()]






			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       List[str]						)   ->			Dict[str, torch.Tensor]:







								"""simple docstring"""
								A_								=    torch.stack([x["input_ids"] for x in batch]						)
								A_								=    torch.stack([x["attention_mask"] for x in batch]						)
								A_								=    torch.stack([x["decoder_input_ids"] for x in batch]						)
								A_								=    (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer							, _snake_case						)
								    else self.tokenizer.pad_token_id
								)
								A_								=    (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer							, _snake_case						)
								    else self.tokenizer.pad_token_id
								)
								A_								=    trim_batch(_snake_case							, _snake_case						)
								A_			,     A_								=    trim_batch(_snake_case							, _snake_case							, attention_mask=_snake_case						)
								A_								=    {
								    "input_ids": source_ids,
								    "attention_mask": source_mask,
								    "decoder_input_ids": y,
								}
								return batch





UpperCamelCase_       :    Any						      =			getLogger(__name__)
def 				A_  (__a					):

					'''simple docstring'''





					return list(itertools.chain.from_iterable(__a					)					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    get_git_info()
					save_json(__a					, os.path.join(__a					, "git_log.json"					)					)
def 				A_  (__a					, __a					, __a=4					, **__a					):

					'''simple docstring'''





					with open(__a					, "w"					) as f:
										json.dump(__a					, __a					, indent=__a					, **__a					)
def 				A_  (__a					):

					'''simple docstring'''





					with open(__a					) as f:
										return json.load(__a					)
def 				A_  ():

					'''simple docstring'''





					A_								=    git.Repo(search_parent_directories=__a					)
					A_								=    {
					    "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 				A_  (__a					, __a					):

					'''simple docstring'''





					return list(map(__a					, __a					)					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					with open(__a					, "wb"					) as f:
										return pickle.dump(__a					, __a					)
def 				A_  (__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					):
										A_								=    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 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    normalize_answer(__a					).split()
					A_								=    normalize_answer(__a					).split()
					A_								=    Counter(__a					) & Counter(__a					)
					A_								=    sum(common.values()					)
					if num_same == 0:
										return 0
					A_								=    1.0 * num_same / len(__a					)
					A_								=    1.0 * num_same / len(__a					)
					A_								=    (2 * precision * recall) / (precision + recall)
					return fa
def 				A_  (__a					, __a					):

					'''simple docstring'''





					return normalize_answer(__a					) == normalize_answer(__a					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					assert len(__a					) == len(__a					)
					A_								=    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 				A_  (__a					):

					'''simple docstring'''





					return model_prefix.startswith("rag"					)




def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_								=    {p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					A_								=    "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
															A_								=    p if hasattr(__a					, __a					) else equivalent_param[p]
															setattr(__a					, __a					, getattr(__a					, __a					)					)
															delattr(__a					, __a					)
					return hparams, config




