





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__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			str=True					,	UpperCamelCase__     :			int="pt"    )->Dict:
       A__		   =		{'''add_prefix_space''': True} if isinstance(UpperCamelCase__					,	UpperCamelCase__    ) and not line.startswith(''' '''    ) else {}
       A__		   =		padding_side
       return tokenizer(
           [line]					,	max_length=UpperCamelCase__					,	padding='''max_length''' if pad_to_max_length else None					,	truncation=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	add_special_tokens=UpperCamelCase__					,	**UpperCamelCase__					,	)


def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Tuple=None					,	)->Optional[int]:
       A__		   =		input_ids.ne(UpperCamelCase__    ).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        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):



 def __init__(				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase="train",__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase="",):
        super().__init__()
        A__		   =		Path(__lowerCamelCase					).joinpath(type_path + '''.source'''					)
        A__		   =		Path(__lowerCamelCase					).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					):
        return len(self.src_lens					)



 def __getitem__(				self,__lowerCamelCase					):
        A__		   =		index + 1  # linecache starts at 1
        A__		   =		self.prefix + linecache.getline(str(self.src_file					),__lowerCamelCase					).rstrip('''\n'''					)
        A__		   =		linecache.getline(str(self.tgt_file					),__lowerCamelCase					).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,__lowerCamelCase					):
               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,__lowerCamelCase					) else self.tokenizer
        )
        A__		   =		self.tokenizer.generator if isinstance(self.tokenizer,__lowerCamelCase					) else self.tokenizer

        A__		   =		encode_line(__lowerCamelCase,__lowerCamelCase,self.max_source_length,'''right'''					)
        A__		   =		encode_line(__lowerCamelCase,__lowerCamelCase,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 							UpperCamelCase     (				__lowerCamelCase					):
        return [len(__lowerCamelCase					) for x in Path(__lowerCamelCase					).open().readlines()]




 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        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,__lowerCamelCase					)
            else self.tokenizer.pad_token_id
        )
        A__		   =		(
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer,__lowerCamelCase					)
            else self.tokenizer.pad_token_id
        )
        A__		   =		trim_batch(__lowerCamelCase,__lowerCamelCase					)
        A__   ,      A__		   =		trim_batch(__lowerCamelCase,__lowerCamelCase,attention_mask=__lowerCamelCase					)
        A__		   =		{
            '''input_ids''': source_ids,
            '''attention_mask''': source_mask,
            '''decoder_input_ids''': y,
        }
        return batch
a__: Dict							   =						getLogger(__name__)


def       UpperCamelCase__(			UpperCamelCase__     :			List[List]    )->Union[str, Any]:
       return list(itertools.chain.from_iterable(UpperCamelCase__    )    )


def       UpperCamelCase__(			UpperCamelCase__     :			str    )->None:
       A__		   =		get_git_info()
       save_json(UpperCamelCase__					,	os.path.join(UpperCamelCase__					,	'''git_log.json'''    )    )


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			Dict=4					,	**UpperCamelCase__     :			Any    )->Optional[Any]:
       with open(UpperCamelCase__					,	'''w'''    ) as f:
              json.dump(UpperCamelCase__					,	UpperCamelCase__					,	indent=UpperCamelCase__					,	**UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]    )->List[str]:
       with open(UpperCamelCase__    ) as f:
              return json.load(UpperCamelCase__    )


def       UpperCamelCase__(			)->int:
       A__		   =		git.Repo(search_parent_directories=UpperCamelCase__    )
       A__		   =		{
           '''repo_id''': str(UpperCamelCase__    ),
           '''repo_sha''': str(repo.head.object.hexsha    ),
           '''repo_branch''': str(repo.active_branch    ),
           '''hostname''': str(socket.gethostname()    ),
       }
       return repo_infos


def       UpperCamelCase__(			UpperCamelCase__     :			Callable					,	UpperCamelCase__     :			Iterable    )->List:
       return list(map(UpperCamelCase__					,	UpperCamelCase__    )    )


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Union[str, Any]    )->List[Any]:
       with open(UpperCamelCase__					,	'''wb'''    ) as f:
              return pickle.dump(UpperCamelCase__					,	UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]    )->Tuple:

       def remove_articles(UpperCamelCase__     :			Tuple    ):
              return re.sub(r'''\b(a|an|the)\b'''					,	''' '''					,	UpperCamelCase__    )

       def white_space_fix(UpperCamelCase__     :			Optional[int]    ):
              return " ".join(text.split()    )

       def remove_punc(UpperCamelCase__     :			List[str]    ):
              A__		   =		set(string.punctuation    )
              return "".join(ch for ch in text if ch not in exclude    )

       def lower(UpperCamelCase__     :			int    ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__    )    )    )    )


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			List[Any]    )->Tuple:
       A__		   =		normalize_answer(UpperCamelCase__    ).split()
       A__		   =		normalize_answer(UpperCamelCase__    ).split()
       A__		   =		Counter(UpperCamelCase__    ) & Counter(UpperCamelCase__    )
       A__		   =		sum(common.values()    )
       if num_same == 0:
              return 0
       A__		   =		1.0 * num_same / len(UpperCamelCase__    )
       A__		   =		1.0 * num_same / len(UpperCamelCase__    )
       A__		   =		(2 * precision * recall) / (precision + recall)
       return fa


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Any    )->str:
       return normalize_answer(UpperCamelCase__    ) == normalize_answer(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			List[str]    )->Dict:
       assert len(UpperCamelCase__    ) == len(UpperCamelCase__    )
       A__		   =		0
       for hypo, pred in zip(UpperCamelCase__					,	UpperCamelCase__    ):
              em += exact_match_score(UpperCamelCase__					,	UpperCamelCase__    )
       if len(UpperCamelCase__    ) > 0:
              em /= len(UpperCamelCase__    )
       return {"em": em}


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]    )->List[str]:
       return model_prefix.startswith('''rag'''    )


def       UpperCamelCase__(			UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]    )->List[Any]:
       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(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    ):
                     if not hasattr(UpperCamelCase__					,	UpperCamelCase__    ) and not hasattr(UpperCamelCase__					,	equivalent_param[p]    ):
                            logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCamelCase__    )    )
                            delattr(UpperCamelCase__					,	UpperCamelCase__    )
                            continue
                     A__		   =		p if hasattr(UpperCamelCase__					,	UpperCamelCase__    ) else equivalent_param[p]
                     setattr(UpperCamelCase__					,	UpperCamelCase__					,	getattr(UpperCamelCase__					,	UpperCamelCase__    )    )
                     delattr(UpperCamelCase__					,	UpperCamelCase__    )
       return hparams, config












import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ComputeEnvironment.AMAZON_SAGEMAKER
 __SCREAMING_SNAKE_CASE							=      True
 __SCREAMING_SNAKE_CASE							=      '''ml.p3.2xlarge'''
 __SCREAMING_SNAKE_CASE							=      '''accelerate_sagemaker_execution_role'''
 __SCREAMING_SNAKE_CASE							=      '''hf-sm'''
 __SCREAMING_SNAKE_CASE							=      '''us-east-1'''
 __SCREAMING_SNAKE_CASE							=      1
 __SCREAMING_SNAKE_CASE							=      '''accelerate-sagemaker-1'''
 __SCREAMING_SNAKE_CASE							=      '''1.6'''
 __SCREAMING_SNAKE_CASE							=      '''4.4'''
 __SCREAMING_SNAKE_CASE							=      '''train.py'''
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''False''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''--do_test''',
     '''False''',
     '''--do_predict''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
        assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase					)
        assert isinstance(converted_args['''do_train'''],__lowerCamelCase					)
        assert isinstance(converted_args['''epochs'''],__lowerCamelCase					)
        assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase					)
        assert isinstance(converted_args['''max_steps'''],__lowerCamelCase					)

        with pytest.raises(__lowerCamelCase					):
               _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)





