'''simple docstring'''




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      List[Any]=False    )    ->    str:


       """simple docstring"""







       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
                  " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
                  " instructions."    )
              raise

       if not is_sharded:
              _UpperCAmelCase     :  Optional[int]							      =    os.path.abspath(_UpperCAmelCase    )
              logger.info(F"""Loading PyTorch weights from {pt_path}"""    )

              _UpperCAmelCase     :  Tuple							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )
              logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters."""    )

              _UpperCAmelCase     :  Tuple							      =    convert_pytorch_state_dict_to_flax(_UpperCAmelCase			,	_UpperCAmelCase    )
       else:
              # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
              _UpperCAmelCase     :  Optional[Any]							      =    convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase			,	_UpperCAmelCase    )
       return flax_state_dict

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple[str]			,	_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      Dict[str, jnp.ndarray]			,	_UpperCAmelCase  :      str			,	)    ->    (Tuple[str], np.ndarray):


       """simple docstring"""







       def is_key_or_prefix_key_in_dict(_UpperCAmelCase  :      Tuple[str]    ) -> bool:
              return len(set(_UpperCAmelCase    ) & {key, (model_prefix,) + key}    ) > 0

       # layer norm
       _UpperCAmelCase     :  int							      =    pt_tuple_key[:-1] + ("scale",)
       if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer mean
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("mean",)
       if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer var
       _UpperCAmelCase     :  List[str]							      =    pt_tuple_key[:-1] + ("var",)
       if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       _UpperCAmelCase     :  Optional[Any]							      =    pt_tuple_key[:-1] + ("embedding",)
       if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       _UpperCAmelCase     :  int							      =    pt_tuple_key[:-1] + ("kernel",)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              _UpperCAmelCase     :  int							      =    pt_tensor.transpose(2			,	3			,	1			,	0    )
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("kernel",)
       if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              _UpperCAmelCase     :  Tuple							      =    pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       _UpperCAmelCase     :  Any							      =    pt_tuple_key[:-1] + ("weight",)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("bias",)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
       _UpperCAmelCase     :  List[str]							      =    None
       if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
              _UpperCAmelCase     :  List[str]							      =    pt_tuple_key[-2] + "_g"
       elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
              _UpperCAmelCase     :  int							      =    pt_tuple_key[-2] + "_v"
       if name is not None:
              _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-3] + (name,)
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      List[Any]    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    {k: v.numpy() for k, v in pt_state_dict.items()}

       _UpperCAmelCase     :  Optional[Any]							      =    flax_model.base_model_prefix

       # use params dict if the model contains batch norm layers
       if "params" in flax_model.params:
              _UpperCAmelCase     :  str							      =    flax_model.params["params"]
       else:
              _UpperCAmelCase     :  Union[str, Any]							      =    flax_model.params
       _UpperCAmelCase     :  List[Any]							      =    flatten_dict(_UpperCAmelCase    )

       # add batch_stats keys,values to dict
       if "batch_stats" in flax_model.params:
              _UpperCAmelCase     :  Dict							      =    flatten_dict(flax_model.params["batch_stats"]    )
              random_flax_state_dict.update(_UpperCAmelCase    )

       _UpperCAmelCase     :  str							      =    {}

       _UpperCAmelCase     :  List[Any]							      =    (model_prefix not in flax_model_params) and (
           model_prefix in {k.split("."    )[0] for k in pt_state_dict.keys()}
       )
       _UpperCAmelCase     :  List[str]							      =    (model_prefix in flax_model_params) and (
           model_prefix not in {k.split("."    )[0] for k in pt_state_dict.keys()}
       )

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              _UpperCAmelCase     :  int							      =    tuple(pt_key.split("."    )    )

              # remove base model prefix if necessary
              _UpperCAmelCase     :  Any							      =    pt_tuple_key[0] == model_prefix
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[1:]

              # Correctly rename weight parameters
              _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    rename_key_and_reshape_tensor(
                  _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

              # add model prefix if necessary
              _UpperCAmelCase     :  Optional[int]							      =    (model_prefix,) + flax_key in random_flax_state_dict
              if load_base_model_into_model_with_head and require_base_model_prefix:
                     _UpperCAmelCase     :  Union[str, Any]							      =    (model_prefix,) + flax_key

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

        # add batch stats if the model contains batchnorm layers
              if "batch_stats" in flax_model.params:
                     if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                            _UpperCAmelCase     :  List[str]							      =    jnp.asarray(_UpperCAmelCase    )
                            continue
                     # remove num_batches_tracked key
                     if "num_batches_tracked" in flax_key[-1]:
                            flax_state_dict.pop(_UpperCAmelCase			,	_UpperCAmelCase    )
                            continue

                     # also add unexpected weight so that warning is thrown
                     _UpperCAmelCase     :  Union[str, Any]							      =    jnp.asarray(_UpperCAmelCase    )

              else:
                     # also add unexpected weight so that warning is thrown
                     _UpperCAmelCase     :  Optional[int]							      =    jnp.asarray(_UpperCAmelCase    )

       return unflatten_dict(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple			,	_UpperCAmelCase  :      Optional[Any]    )    ->    Optional[int]:


       """simple docstring"""







       import torch

       # Load the index
       _UpperCAmelCase     :  Union[str, Any]							      =    {}
       for shard_file in shard_filenames:
              # load using msgpack utils
              _UpperCAmelCase     :  List[str]							      =    torch.load(_UpperCAmelCase    )
              _UpperCAmelCase     :  int							      =    {k: v.numpy() for k, v in pt_state_dict.items()}

              _UpperCAmelCase     :  List[Any]							      =    flax_model.base_model_prefix

              # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
              if "batch_stats" in flax_model.params:
                     _UpperCAmelCase     :  Optional[Any]							      =    flax_model.params["params"]

                     _UpperCAmelCase     :  List[Any]							      =    flatten_dict(_UpperCAmelCase    )
                     random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]    )    )
              else:
                     _UpperCAmelCase     :  List[Any]							      =    flax_model.params
                     _UpperCAmelCase     :  List[str]							      =    flatten_dict(_UpperCAmelCase    )

              _UpperCAmelCase     :  Optional[int]							      =    (model_prefix not in flax_model_params) and (
                  model_prefix in {k.split("."    )[0] for k in pt_state_dict.keys()}
              )
              _UpperCAmelCase     :  int							      =    (model_prefix in flax_model_params) and (
                  model_prefix not in {k.split("."    )[0] for k in pt_state_dict.keys()}
              )
              # Need to change some parameters name to match Flax names
              for pt_key, pt_tensor in pt_state_dict.items():
                     _UpperCAmelCase     :  int							      =    tuple(pt_key.split("."    )    )

                     # remove base model prefix if necessary
                     _UpperCAmelCase     :  List[Any]							      =    pt_tuple_key[0] == model_prefix
                     if load_model_with_head_into_base_model and has_base_model_prefix:
                            _UpperCAmelCase     :  str							      =    pt_tuple_key[1:]

                     # Correctly rename weight parameters
                     _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    rename_key_and_reshape_tensor(
                         _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )
                     # add model prefix if necessary
                     _UpperCAmelCase     :  Tuple							      =    (model_prefix,) + flax_key in random_flax_state_dict
                     if load_base_model_into_model_with_head and require_base_model_prefix:
                            _UpperCAmelCase     :  Tuple							      =    (model_prefix,) + flax_key

                     if flax_key in random_flax_state_dict:
                            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                                   raise ValueError(
                                       F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                       F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

            # add batch stats if the model contains batchnorm layers
                     if "batch_stats" in flax_model.params:
                            if "mean" in flax_key[-1]:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    jnp.asarray(_UpperCAmelCase    )
                                   continue
                            if "var" in flax_key[-1]:
                                   _UpperCAmelCase     :  str							      =    jnp.asarray(_UpperCAmelCase    )
                                   continue
                            # remove num_batches_tracked key
                            if "num_batches_tracked" in flax_key[-1]:
                                   flax_state_dict.pop(_UpperCAmelCase			,	_UpperCAmelCase    )
                                   continue

                            # also add unexpected weight so that warning is thrown
                            _UpperCAmelCase     :  List[str]							      =    jnp.asarray(_UpperCAmelCase    )

                     else:
                            # also add unexpected weight so that warning is thrown
                            _UpperCAmelCase     :  Any							      =    jnp.asarray(_UpperCAmelCase    )
       return unflatten_dict(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]			,	_UpperCAmelCase  :      Any    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    os.path.abspath(_UpperCAmelCase    )
       logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""    )

       # import correct flax class
       _UpperCAmelCase     :  List[str]							      =    getattr(_UpperCAmelCase			,	"Flax" + model.__class__.__name__    )

       # load flax weight dict
       with open(_UpperCAmelCase			,	"rb"    ) as state_f:
              try:
                     _UpperCAmelCase     :  Dict							      =    from_bytes(_UpperCAmelCase			,	state_f.read()    )
              except UnpicklingError:
                     raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """    )

       return load_flax_weights_in_pytorch_model(_UpperCAmelCase			,	_UpperCAmelCase    )


def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
                  " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
                  " instructions."    )
              raise

       # check if we have bf16 weights
       _UpperCAmelCase     :  List[str]							      =    flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase    : x.dtype == jnp.bfloataa			,	_UpperCAmelCase    )    ).values()
       if any(_UpperCAmelCase    ):
              # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
              # and bf16 is not fully supported in PT yet.
              logger.warning(
                  "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
                  "before loading those in PyTorch model."    )
              _UpperCAmelCase     :  Optional[int]							      =    jax.tree_util.tree_map(
                  lambda _UpperCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params			,	_UpperCAmelCase    )

       _UpperCAmelCase     :  Tuple							      =    flatten_dict(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[str]							      =    pt_model.state_dict()

       _UpperCAmelCase     :  Tuple							      =    (pt_model.base_model_prefix in flax_state) and (
           pt_model.base_model_prefix not in {k.split("."    )[0] for k in pt_model_dict.keys()}
       )
       _UpperCAmelCase     :  Union[str, Any]							      =    (pt_model.base_model_prefix not in flax_state) and (
           pt_model.base_model_prefix in {k.split("."    )[0] for k in pt_model_dict.keys()}
       )

       # keep track of unexpected & missing keys
       _UpperCAmelCase     :  List[Any]							      =    []
       _UpperCAmelCase     :  str							      =    set(pt_model_dict.keys()    )

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              _UpperCAmelCase     :  Tuple							      =    flax_key_tuple[0] == pt_model.base_model_prefix
              _UpperCAmelCase     :  Optional[Any]							      =    ".".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

              # adapt flax_key to prepare for loading from/to base model only
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _UpperCAmelCase     :  str							      =    flax_key_tuple[1:]
              elif load_base_model_into_model_with_head and require_base_model_prefix:
                     _UpperCAmelCase     :  int							      =    (pt_model.base_model_prefix,) + flax_key_tuple

              # rename flax weights to PyTorch format
              if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
                     # conv layer
                     _UpperCAmelCase     :  int							      =    flax_key_tuple[:-1] + ("weight",)
                     _UpperCAmelCase     :  Optional[Any]							      =    jnp.transpose(_UpperCAmelCase			,	(3, 2, 0, 1)    )
              elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
                     # linear layer
                     _UpperCAmelCase     :  List[Any]							      =    flax_key_tuple[:-1] + ("weight",)
                     _UpperCAmelCase     :  List[Any]							      =    flax_tensor.T
              elif flax_key_tuple[-1] in ["scale", "embedding"]:
                     _UpperCAmelCase     :  int							      =    flax_key_tuple[:-1] + ("weight",)

              # adding batch stats from flax batch norm to pt
              elif "mean" in flax_key_tuple[-1]:
                     _UpperCAmelCase     :  Optional[Any]							      =    flax_key_tuple[:-1] + ("running_mean",)
              elif "var" in flax_key_tuple[-1]:
                     _UpperCAmelCase     :  List[str]							      =    flax_key_tuple[:-1] + ("running_var",)

              if "batch_stats" in flax_state:
                     _UpperCAmelCase     :  Union[str, Any]							      =    ".".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
              else:
                     _UpperCAmelCase     :  Optional[Any]							      =    ".".join(_UpperCAmelCase    )

              # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
              _UpperCAmelCase     :  Optional[Any]							      =    {}
              # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
              for key in pt_model_dict:
                     _UpperCAmelCase     :  Optional[int]							      =    key.split("."    )
                     _UpperCAmelCase     :  Tuple							      =    None
                     if key_components[-3::2] == ["parametrizations", "original0"]:
                            _UpperCAmelCase     :  Union[str, Any]							      =    key_components[-2] + "_g"
                     elif key_components[-3::2] == ["parametrizations", "original1"]:
                            _UpperCAmelCase     :  int							      =    key_components[-2] + "_v"
                     if name is not None:
                            _UpperCAmelCase     :  List[str]							      =    key_components[:-3] + [name]
                            _UpperCAmelCase     :  Optional[Any]							      =    ".".join(_UpperCAmelCase    )
                            _UpperCAmelCase     :  Any							      =    key

              if flax_key in special_pt_names:
                     _UpperCAmelCase     :  Union[str, Any]							      =    special_pt_names[flax_key]

              if flax_key in pt_model_dict:
                     if flax_tensor.shape != pt_model_dict[flax_key].shape:
                            raise ValueError(
                                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )
                     else:
                            # add weight to pytorch dict
                            _UpperCAmelCase     :  Optional[Any]							      =    np.asarray(_UpperCAmelCase    ) if not isinstance(_UpperCAmelCase			,	np.ndarray    ) else flax_tensor
                            _UpperCAmelCase     :  int							      =    torch.from_numpy(_UpperCAmelCase    )
                            # remove from missing keys
                            missing_keys.remove(_UpperCAmelCase    )
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(_UpperCAmelCase    )

       pt_model.load_state_dict(_UpperCAmelCase    )

       # re-transform missing_keys to list
       _UpperCAmelCase     :  Optional[int]							      =    list(_UpperCAmelCase    )

       if len(_UpperCAmelCase    ) > 0:
              logger.warning(
                  "Some weights of the Flax model were not used when initializing the PyTorch model"
                  F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
                  F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
                  " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
                  F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
                  " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
                  " FlaxBertForSequenceClassification model)."    )
       else:
              logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""    )
       if len(_UpperCAmelCase    ) > 0:
              logger.warning(
                  F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
                  F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
                  " use it for predictions and inference."    )
       else:
              logger.warning(
                  F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
                  "If your task is similar to the task the model of the checkpoint was trained on, "
                  F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""    )

       return pt_model






"""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      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Tuple=True							,		__lowerCamelCase				:    Dict="pt"	):
			"""simple docstring"""
			lowerCamelCase__		:		str			={'''add_prefix_space''': True} if isinstance(__lowerCamelCase							,		__lowerCamelCase	) and not line.startswith(''' '''	) else {}
			lowerCamelCase__		:		int			=padding_side
			return tokenizer(
			    [line]							,		max_length=__lowerCamelCase							,		padding='''max_length''' if pad_to_max_length else None							,		truncation=__lowerCamelCase							,		return_tensors=__lowerCamelCase							,		add_special_tokens=__lowerCamelCase							,		**__lowerCamelCase							,		)






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Optional[int]=None							,		):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=input_ids.ne(__lowerCamelCase	).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	(      lowerCAmelCase_   ):


					'''simple docstring'''





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



					def __len__(       self							:  Dict     )->					Optional[int]:
								return len(self.src_lens     )



					def __getitem__(       self							:  List[str],  lowerCamelCase							:  Optional[int]     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=index + 1  # linecache starts at 1
								lowerCamelCase__		:		Optional[int]			=self.prefix + linecache.getline(str(self.src_file     ),  lowerCamelCase     ).rstrip('''\n'''     )
								lowerCamelCase__		:		Optional[Any]			=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
								lowerCamelCase__		:		Optional[int]			=(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer
								)
								lowerCamelCase__		:		Tuple			=self.tokenizer.generator if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer

								lowerCamelCase__		:		Optional[int]			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_source_length,  '''right'''     )
								lowerCamelCase__		:		str			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_target_length,  '''right'''     )

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



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  Union[str, Any]     )->					Optional[int]:
								return [len(lowerCamelCase     ) for x in Path(lowerCamelCase     ).open().readlines()]




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





_lowercase : Any      						=						getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    List[List]	):
			"""simple docstring"""
			return list(itertools.chain.from_iterable(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=get_git_info()
			save_json(__lowerCamelCase							,		os.path.join(__lowerCamelCase							,		'''git_log.json'''	)	)






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Dict=4							,		**__lowerCamelCase				:    int	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''w'''	) as f:
						json.dump(__lowerCamelCase							,		__lowerCamelCase							,		indent=__lowerCamelCase							,		**__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase	) as f:
						return json.load(__lowerCamelCase	)






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=git.Repo(search_parent_directories=__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			={
			    '''repo_id''': str(__lowerCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos






def      snake_case__   (		__lowerCamelCase				:    Callable							,		__lowerCamelCase				:    Iterable	):
			"""simple docstring"""
			return list(map(__lowerCamelCase							,		__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''wb'''	) as f:
						return pickle.dump(__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""

			def remove_articles(__lowerCamelCase				:    List[Any]	):
						return re.sub(R'''\b(a|an|the)\b'''							,		''' '''							,		__lowerCamelCase	)

			def white_space_fix(__lowerCamelCase				:    Any	):
						return " ".join(text.split()	)

			def remove_punc(__lowerCamelCase				:    Optional[Any]	):
						lowerCamelCase__		:		Tuple			=set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

			def lower(__lowerCamelCase				:    Any	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase	)	)	)	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		Optional[int]			=Counter(__lowerCamelCase	) & Counter(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=sum(common.values()	)
			if num_same == 0:
						return 0
			lowerCamelCase__		:		Dict			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		List[str]			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=(2 * precision * recall) / (precision + recall)
			return fa






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return normalize_answer(__lowerCamelCase	) == normalize_answer(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			assert len(__lowerCamelCase	) == len(__lowerCamelCase	)
			lowerCamelCase__		:		Any			=0
			for hypo, pred in zip(__lowerCamelCase							,		__lowerCamelCase	):
						em += exact_match_score(__lowerCamelCase							,		__lowerCamelCase	)
			if len(__lowerCamelCase	) > 0:
						em /= len(__lowerCamelCase	)
			return {"em": em}






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			return model_prefix.startswith('''rag'''	)






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




