

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    Tuple=True	,     SCREAMING_SNAKE_CASE_		:    int="pt"			):



     '''simple docstring'''





     lowercase__    :   str      =       {'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) and not line.startswith(' '			) else {}
     lowercase__    :   Optional[Any]      =       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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    Optional[int]=None	,     ):



     '''simple docstring'''





     lowercase__    :   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       SCREAMING_SNAKE_CASE__					(__snake_case     ):





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





    def __len__(      self):
         return len(self.src_lens)





    def __getitem__(      self						,   a):
         lowercase__    :   Union[str, Any]      =       index + 1  # linecache starts at 1
         lowercase__    :   int      =       self.prefix + linecache.getline(str(self.src_file)						,   a).rstrip('\n')
         lowercase__    :   Tuple      =       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
         lowercase__    :   List[Any]      =       (
             self.tokenizer.question_encoder if isinstance(self.tokenizer						,   a) else self.tokenizer
         )
         lowercase__    :   Any      =       self.tokenizer.generator if isinstance(self.tokenizer						,   a) else self.tokenizer

         lowercase__    :   int      =       encode_line(a						,   a						,   self.max_source_length						,   'right')
         lowercase__    :   Optional[int]      =       encode_line(a						,   a						,   self.max_target_length						,   'right')

         lowercase__    :   str      =       source_inputs['input_ids'].squeeze()
         lowercase__    :   Optional[Any]      =       target_inputs['input_ids'].squeeze()
         lowercase__    :   Optional[int]      =       source_inputs['attention_mask'].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }





    @staticmethod
    def       snake_case_				(      a):
         return [len(a) for x in Path(a).open().readlines()]





    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       torch.stack([x['input_ids'] for x in batch])
         lowercase__    :   Union[str, Any]      =       torch.stack([x['attention_mask'] for x in batch])
         lowercase__    :   Dict      =       torch.stack([x['decoder_input_ids'] for x in batch])
         lowercase__    :   List[str]      =       (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer						,   a)
             else self.tokenizer.pad_token_id
         )
         lowercase__    :   List[str]      =       (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer						,   a)
             else self.tokenizer.pad_token_id
         )
         lowercase__    :   List[Any]      =       trim_batch(a						,   a)
         lowercase__      , lowercase__    :   List[str]      =       trim_batch(a						,   a						,   attention_mask=a)
         lowercase__    :   Optional[Any]      =       {
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch



snake_case_			  = getLogger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[List]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__    :   Dict      =       get_git_info()
     save_json(SCREAMING_SNAKE_CASE_	,     os.path.join(SCREAMING_SNAKE_CASE_	,     'git_log.json'			)			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Tuple=4	,     **SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





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

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Union[str, 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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Callable	,     SCREAMING_SNAKE_CASE_		:    Iterable			):



     '''simple docstring'''





     return list(map(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





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

     def white_space_fix(SCREAMING_SNAKE_CASE_		:    List[str]			):
          return " ".join(text.split()			)

     def remove_punc(SCREAMING_SNAKE_CASE_		:    int			):
          lowercase__    :   Tuple      =       set(string.punctuation			)
          return "".join(ch for ch in text if ch not in exclude			)

     def lower(SCREAMING_SNAKE_CASE_		:    Tuple			):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_			)			)			)			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





     lowercase__    :   int      =       normalize_answer(SCREAMING_SNAKE_CASE_			).split()
     lowercase__    :   Any      =       normalize_answer(SCREAMING_SNAKE_CASE_			).split()
     lowercase__    :   List[Any]      =       Counter(SCREAMING_SNAKE_CASE_			) & Counter(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   int      =       sum(common.values()			)
     if num_same == 0:
          return 0
     lowercase__    :   Optional[Any]      =       1.0 * num_same / len(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Optional[Any]      =       1.0 * num_same / len(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Dict      =       (2 * precision * recall) / (precision + recall)
     return fa

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     return normalize_answer(SCREAMING_SNAKE_CASE_			) == normalize_answer(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





     assert len(SCREAMING_SNAKE_CASE_			) == len(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





     return model_prefix.startswith('rag'			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       {p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     lowercase__    :   Any      =       '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
               lowercase__    :   Any      =       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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """switch_transformers"""
    __lowerCamelCase						:       Optional[Any]														=     ["""past_key_values"""]
    __lowerCamelCase						:       Any														=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(      self						,   a=3_2128						,   a=768						,   a=64						,   a=2048						,   a=64						,   a=12						,   a=3						,   a=12						,   a=3						,   a=12						,   a=8						,   a=False						,   a=0.01						,   a="float32"						,   a=False						,   a=32						,   a=128						,   a=0.1						,   a=1e-6						,   a=0.001						,   a=0.001						,   a=1.0						,   a="relu"						,   a=True						,   a=False						,   a=True						,   a=0						,   a=1						,   **a						,   ):
         lowercase__    :   Optional[int]      =       vocab_size
         lowercase__    :   List[Any]      =       d_model
         lowercase__    :   List[Any]      =       d_kv
         lowercase__    :   Any      =       d_ff

         lowercase__    :   Optional[int]      =       num_sparse_encoder_layers

         lowercase__    :   Tuple      =       num_layers
         lowercase__    :   Optional[Any]      =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__    :   str      =       num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              lowercase__    :   Optional[Any]      =       self.num_layers // self.num_sparse_encoder_layers
         else:
              lowercase__    :   Optional[int]      =       self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              lowercase__    :   Union[str, Any]      =       self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              lowercase__    :   int      =       self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowercase__    :   List[Any]      =       num_heads
         lowercase__    :   Union[str, Any]      =       num_experts
         lowercase__    :   str      =       expert_capacity
         lowercase__    :   List[Any]      =       router_bias
         lowercase__    :   Optional[int]      =       router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
         lowercase__    :   str      =       router_dtype

         lowercase__    :   Optional[int]      =       router_ignore_padding_tokens
         lowercase__    :   int      =       relative_attention_num_buckets
         lowercase__    :   Optional[Any]      =       relative_attention_max_distance

         lowercase__    :   List[str]      =       dropout_rate
         lowercase__    :   str      =       layer_norm_epsilon
         lowercase__    :   int      =       initializer_factor
         lowercase__    :   int      =       feed_forward_proj
         lowercase__    :   Dict      =       use_cache
         lowercase__    :   int      =       add_router_probs

         lowercase__    :   int      =       router_z_loss_coef
         lowercase__    :   List[Any]      =       router_aux_loss_coef

         lowercase__    :   int      =       self.feed_forward_proj.split('-')
         lowercase__    :   Optional[int]      =       act_info[-1]
         lowercase__    :   Dict      =       act_info[0] == 'gated'

         if len(a) > 1 and act_info[0] != "gated" or len(a) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\'')

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              lowercase__    :   Optional[int]      =       'gelu_new'

         super().__init__(
             pad_token_id=a						,   eos_token_id=a						,   is_encoder_decoder=a						,   **a						,   )

