





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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     ):
						UpperCamelCase				:		Any				   =			input_ids.ne(SCREAMING_SNAKE_CASE     ).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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   _A						,   _A="train"						,   _A=None						,   _A=None						,   _A=None						,   _A=""						,   ):






									'''simple docstring'''







									super().__init__()
									UpperCamelCase				:		Optional[int]				   =			Path(_A    ).joinpath(type_path + """.source"""    )
									UpperCamelCase				:		Union[str, Any]				   =			Path(_A    ).joinpath(type_path + """.target"""    )
									UpperCamelCase				:		Union[str, Any]				   =			self.get_char_lens(self.src_file    )
									UpperCamelCase				:		Union[str, Any]				   =			max_source_length
									UpperCamelCase				:		Tuple				   =			max_target_length
									assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
									UpperCamelCase				:		Any				   =			tokenizer
									UpperCamelCase				:		Tuple				   =			prefix
									if n_obs is not None:
															UpperCamelCase				:		str				   =			self.src_lens[:n_obs]
									UpperCamelCase				:		Optional[Any]				   =			src_lang
									UpperCamelCase				:		Any				   =			tgt_lang







			def __len__(      self    ):






									'''simple docstring'''







									return len(self.src_lens    )







			def __getitem__(      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			index + 1  # linecache starts at 1
									UpperCamelCase				:		List[Any]				   =			self.prefix + linecache.getline(str(self.src_file    )						,   _A    ).rstrip("""\n"""    )
									UpperCamelCase				:		List[str]				   =			linecache.getline(str(self.tgt_file    )						,   _A    ).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						,   _A    ):
															source_line += self.tokenizer.eos_token
															tgt_line += self.tokenizer.eos_token

									# Pad source and target to the right
									UpperCamelCase				:		List[str]				   =			(
									    self.tokenizer.question_encoder if isinstance(self.tokenizer						,   _A    ) else self.tokenizer
									)
									UpperCamelCase				:		Tuple				   =			self.tokenizer.generator if isinstance(self.tokenizer						,   _A    ) else self.tokenizer

									UpperCamelCase				:		str				   =			encode_line(_A						,   _A						,   self.max_source_length						,   """right"""    )
									UpperCamelCase				:		List[Any]				   =			encode_line(_A						,   _A						,   self.max_target_length						,   """right"""    )

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







			@staticmethod
			def   _a      (      _A    ):






									'''simple docstring'''







									return [len(_A    ) for x in Path(_A    ).open().readlines()]







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			torch.stack([x["""input_ids"""] for x in batch]    )
									UpperCamelCase				:		List[Any]				   =			torch.stack([x["""attention_mask"""] for x in batch]    )
									UpperCamelCase				:		Optional[int]				   =			torch.stack([x["""decoder_input_ids"""] for x in batch]    )
									UpperCamelCase				:		Dict				   =			(
									    self.tokenizer.generator.pad_token_id
									    if isinstance(self.tokenizer						,   _A    )
									    else self.tokenizer.pad_token_id
									)
									UpperCamelCase				:		Optional[Any]				   =			(
									    self.tokenizer.question_encoder.pad_token_id
									    if isinstance(self.tokenizer						,   _A    )
									    else self.tokenizer.pad_token_id
									)
									UpperCamelCase				:		Tuple				   =			trim_batch(_A						,   _A    )
									UpperCamelCase   ,						UpperCamelCase				:		int				   =			trim_batch(_A						,   _A						,   attention_mask=_A    )
									UpperCamelCase				:		str				   =			{
									    """input_ids""": source_ids,
									    """attention_mask""": source_mask,
									    """decoder_input_ids""": y,
									}
									return batch


__magic_name__       :					Tuple						=			getLogger(__name__)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			get_git_info()
						save_json(SCREAMING_SNAKE_CASE							,     os.path.join(SCREAMING_SNAKE_CASE							,     """git_log.json"""     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=4							,     **SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """w"""     ) as f:
												json.dump(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     indent=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE     ) as f:
												return json.load(SCREAMING_SNAKE_CASE     )

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return list(map(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """wb"""     ) as f:
												return pickle.dump(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):

						def remove_articles(SCREAMING_SNAKE_CASE     ):
												return re.sub(r"""\b(a|an|the)\b"""							,     """ """							,     SCREAMING_SNAKE_CASE     )

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

						def remove_punc(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		Union[str, Any]				   =			set(string.punctuation     )
												return "".join(ch for ch in text if ch not in exclude     )

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

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE     )     )     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		str				   =			normalize_answer(SCREAMING_SNAKE_CASE     ).split()
						UpperCamelCase				:		Optional[int]				   =			normalize_answer(SCREAMING_SNAKE_CASE     ).split()
						UpperCamelCase				:		int				   =			Counter(SCREAMING_SNAKE_CASE     ) & Counter(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		str				   =			sum(common.values()     )
						if num_same == 0:
												return 0
						UpperCamelCase				:		Tuple				   =			1.0 * num_same / len(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Any				   =			1.0 * num_same / len(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[int]				   =			(2 * precision * recall) / (precision + recall)
						return fa

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return normalize_answer(SCREAMING_SNAKE_CASE     ) == normalize_answer(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			0
						for hypo, pred in zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												em += exact_match_score(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						if len(SCREAMING_SNAKE_CASE     ) > 0:
												em /= len(SCREAMING_SNAKE_CASE     )
						return {"em": em}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return model_prefix.startswith("""rag"""     )

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






import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase  :      int			 ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase  :      Dict			 =direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCAmelCase  :      List[str]			 =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase  :      int			 =re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowerCAmelCase  :      Union[str, Any]			 ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= None

	# source code of `config_class`
	UpperCAmelCase__:	Optional[int]									= inspect.getsource(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= _re_checkpoint.findall(SCREAMING_SNAKE_CASE      )

	# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
	# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
	for ckpt_name, ckpt_link in checkpoints:
		# allow the link to end with `/`
		if ckpt_link.endswith("/"      ):
			UpperCAmelCase__:	Any									= ckpt_link[:-1]

		# verify the checkpoint name corresponds to the checkpoint link
		UpperCAmelCase__:	int									= f"https://huggingface.co/{ckpt_name}"
		if ckpt_link == ckpt_link_from_name:
			UpperCAmelCase__:	Optional[int]									= ckpt_name
			break

	return checkpoint



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= []

	for config_class in list(CONFIG_MAPPING.values()      ):
		# Skip deprecated models
		if "models.deprecated" in config_class.__module__:
			continue
		UpperCAmelCase__:	Optional[Any]									= get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	List[str]									= config_class.__name__
		if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
			configs_without_checkpoint.append(SCREAMING_SNAKE_CASE      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		UpperCAmelCase__:	Dict									= "\n".join(sorted(SCREAMING_SNAKE_CASE      )      )
		raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"      )


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()