
"""simple docstring"""


import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
      import jax.numpy as jnp



class 	__a	(   _SCREAMING_SNAKE_CASE						):






       def __get__(	self			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				List[str]=None      )->	Any:







              """simple docstring"""
              # See docs.python.org/3/howto/descriptor.html#properties
              if obj is None:
                     return self
              if self.fget is None:
                     raise AttributeError("unreadable attribute"      )
              UpperCamelCase       			=		"""__cached_""" + self.fget.__name__
              UpperCamelCase       			=		getattr(lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_      )
              if cached is None:
                     UpperCamelCase       			=		self.fget(lowerCAmelCase_      )
                     setattr(lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_      )
              return cached





def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		val.lower()
       if val in {"y", "yes", "t", "true", "on", "1"}:
              return 1
       if val in {"n", "no", "f", "false", "off", "0"}:
              return 0
       raise ValueError(F"invalid truth value {val!r}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       if is_torch_fx_proxy(snake_case__					):
              return True
       if is_torch_available():
              import torch

              if isinstance(snake_case__		,   torch.Tensor					):
                     return True
       if is_tf_available():
              import tensorflow as tf

              if isinstance(snake_case__		,   tf.Tensor					):
                     return True

       if is_flax_available():
              import jax.numpy as jnp
              from jax.core import Tracer

              if isinstance(snake_case__		,   (jnp.ndarray, Tracer)					):
                     return True

       return isinstance(snake_case__		,   np.ndarray					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       return isinstance(snake_case__		,   np.ndarray					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       return _is_numpy(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """simple docstring"""
       import torch

       return isinstance(snake_case__		,   torch.Tensor					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       return False if not is_torch_available() else _is_torch(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       import torch

       return isinstance(snake_case__		,   torch.device					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       return False if not is_torch_available() else _is_torch_device(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       import torch

       if isinstance(snake_case__		,   snake_case__					):
              if hasattr(snake_case__		,   snake_case__					):
                     UpperCamelCase       			=		getattr(snake_case__		,   snake_case__					)
              else:
                     return False
       return isinstance(snake_case__		,   torch.dtype					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       return False if not is_torch_available() else _is_torch_dtype(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       import tensorflow as tf

       return isinstance(snake_case__		,   tf.Tensor					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       return False if not is_tf_available() else _is_tensorflow(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       import tensorflow as tf

       # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
       if hasattr(snake_case__		,   "is_symbolic_tensor"					):
              return tf.is_symbolic_tensor(snake_case__					)
       return type(snake_case__					) == tf.Tensor



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       import jax.numpy as jnp  # noqa: F811

       return isinstance(snake_case__		,   jnp.ndarray					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       return False if not is_flax_available() else _is_jax(snake_case__					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       if isinstance(snake_case__		,   (dict, UserDict)					):
              return {k: to_py_obj(snake_case__					) for k, v in obj.items()}
       elif isinstance(snake_case__		,   (list, tuple)					):
              return [to_py_obj(snake_case__					) for o in obj]
       elif is_tf_tensor(snake_case__					):
              return obj.numpy().tolist()
       elif is_torch_tensor(snake_case__					):
              return obj.detach().cpu().tolist()
       elif is_jax_tensor(snake_case__					):
              return np.asarray(snake_case__					).tolist()
       elif isinstance(snake_case__		,   (np.ndarray, np.number)					):  # tolist also works on 0d np arrays
              return obj.tolist()
       else:
              return obj



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       if isinstance(snake_case__		,   (dict, UserDict)					):
              return {k: to_numpy(snake_case__					) for k, v in obj.items()}
       elif isinstance(snake_case__		,   (list, tuple)					):
              return np.array(snake_case__					)
       elif is_tf_tensor(snake_case__					):
              return obj.numpy()
       elif is_torch_tensor(snake_case__					):
              return obj.detach().cpu().numpy()
       elif is_jax_tensor(snake_case__					):
              return np.asarray(snake_case__					)
       else:
              return obj



class 	__a	(   _SCREAMING_SNAKE_CASE						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		fields(self      )

              # Safety and consistency checks
              if not len(lowerCAmelCase_      ):
                     raise ValueError(f"{self.__class__.__name__} has no fields."      )
              if not all(field.default is None for field in class_fields[1:]      ):
                     raise ValueError(f"{self.__class__.__name__} should not have more than one required field."      )

              UpperCamelCase       			=		getattr(self							,   class_fields[0].name      )
              UpperCamelCase       			=		all(getattr(self							,   field.name      ) is None for field in class_fields[1:]      )

              if other_fields_are_none and not is_tensor(lowerCAmelCase_      ):
                     if isinstance(lowerCAmelCase_							,   lowerCAmelCase_      ):
                            UpperCamelCase       			=		first_field.items()
                            UpperCamelCase       			=		True
                     else:
                            try:
                                   UpperCamelCase       			=		iter(lowerCAmelCase_      )
                                   UpperCamelCase       			=		True
                            except TypeError:
                                   UpperCamelCase       			=		False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
                     if first_field_iterator:
                            for idx, element in enumerate(lowerCAmelCase_      ):
                                   if (
                                       not isinstance(lowerCAmelCase_							,   (list, tuple)      )
                                       or not len(lowerCAmelCase_      ) == 2
                                       or not isinstance(element[0]							,   lowerCAmelCase_      )
                                   ):
                                          if idx == 0:
                                                 # If we do not have an iterator of key/values, set it as attribute
                                                 UpperCamelCase       			=		first_field
                                          else:
                                                 # If we have a mixed iterator, raise an error
                                                 raise ValueError(
                                                     f"Cannot set key/value for {element}. It needs to be a tuple (key, value)."      )
                                          break
                                   setattr(self							,   element[0]							,   element[1]      )
                                   if element[1] is not None:
                                          UpperCamelCase       			=		element[1]
                     elif first_field is not None:
                            UpperCamelCase       			=		first_field
              else:
                     for field in class_fields:
                            UpperCamelCase       			=		getattr(self							,   field.name      )
                            if v is not None:
                                   UpperCamelCase       			=		v






       def __delitem__(	self			:				Optional[int]							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				List[str]      )->	Any:







              """simple docstring"""
              raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   *UpperCAmelCase_			:				Optional[Any]							,   **UpperCAmelCase_			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   *UpperCAmelCase_			:				Any							,   **UpperCAmelCase_			:				int      )->	Dict:







              """simple docstring"""
              raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."      )






       def __getitem__(	self			:				List[Any]							,   UpperCAmelCase_			:				List[Any]      )->	int:







              """simple docstring"""
              if isinstance(lowerCAmelCase_							,   lowerCAmelCase_      ):
                     UpperCamelCase       			=		dict(self.items()      )
                     return inner_dict[k]
              else:
                     return self.to_tuple()[k]






       def __setattr__(	self			:				int							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				List[str]      )->	List[str]:







              """simple docstring"""
              if name in self.keys() and value is not None:
                     # Don't call self.__setitem__ to avoid recursion errors
                     super().__setitem__(lowerCAmelCase_							,   lowerCAmelCase_      )
              super().__setattr__(lowerCAmelCase_							,   lowerCAmelCase_      )






       def __setitem__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Tuple      )->	Dict:







              """simple docstring"""
              # Will raise a KeyException if needed
              super().__setitem__(lowerCAmelCase_							,   lowerCAmelCase_      )
              # Don't call self.__setattr__ to avoid recursion errors
              super().__setattr__(lowerCAmelCase_							,   lowerCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return tuple(self[k] for k in self.keys()      )





class 	__a	(   _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE						):






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				int							,   UpperCAmelCase_			:				Tuple      )->	int:







              """simple docstring"""
              raise ValueError(
                  f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()      )}"      )





class 	__a	(   _SCREAMING_SNAKE_CASE						):
       UpperCamelCase_		:       List[Any]				=      """longest"""
       UpperCamelCase_		:       List[str]				=      """max_length"""
       UpperCamelCase_		:       str				=      """do_not_pad"""





class 	__a	(   _SCREAMING_SNAKE_CASE						):
       UpperCamelCase_		:       int				=      """pt"""
       UpperCamelCase_		:       List[Any]				=      """tf"""
       UpperCamelCase_		:       Any				=      """np"""
       UpperCamelCase_		:       Optional[int]				=      """jax"""





class 	__a	:






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Dict      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		context_managers
              UpperCamelCase       			=		ExitStack()






       def __enter__(	self			:				Optional[Any]      )->	Any:







              """simple docstring"""
              for context_manager in self.context_managers:
                     self.stack.enter_context(lowerCAmelCase_      )






       def __exit__(	self			:				Optional[Any]							,   *UpperCAmelCase_			:				Any							,   **UpperCAmelCase_			:				int      )->	Union[str, Any]:







              """simple docstring"""
              self.stack.__exit__(*lowerCAmelCase_							,   **lowerCAmelCase_      )





def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		infer_framework(snake_case__					)
       if framework == "tf":
              UpperCamelCase       			=		inspect.signature(model_class.call					)  # TensorFlow models
       elif framework == "pt":
              UpperCamelCase       			=		inspect.signature(model_class.forward					)  # PyTorch models
       else:
              UpperCamelCase       			=		inspect.signature(model_class.__call__					)  # Flax models

       for p in signature.parameters:
              if p == "return_loss" and signature.parameters[p].default is True:
                     return True

       return False



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		model_class.__name__
       UpperCamelCase       			=		infer_framework(snake_case__					)
       if framework == "tf":
              UpperCamelCase       			=		inspect.signature(model_class.call					)  # TensorFlow models
       elif framework == "pt":
              UpperCamelCase       			=		inspect.signature(model_class.forward					)  # PyTorch models
       else:
              UpperCamelCase       			=		inspect.signature(model_class.__call__					)  # Flax models

       if "QuestionAnswering" in model_name:
              return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
       else:
              return [p for p in signature.parameters if "label" in p]



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_ = ""		,   UpperCAmelCase_ = "."					)->				str:







       """simple docstring"""

       def _flatten_dict(UpperCAmelCase_		,   UpperCAmelCase_=""		,   UpperCAmelCase_="."					):
              for k, v in d.items():
                     UpperCamelCase       			=		str(snake_case__					) + delimiter + str(snake_case__					) if parent_key else k
                     if v and isinstance(snake_case__		,   snake_case__					):
                            yield from flatten_dict(snake_case__		,   snake_case__		,   delimiter=snake_case__					).items()
                     else:
                            yield key, v

       return dict(_flatten_dict(snake_case__		,   snake_case__		,   snake_case__					)					)



@contextmanager
def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_ = False					)->				str:







       """simple docstring"""
       if use_temp_dir:
              with tempfile.TemporaryDirectory() as tmp_dir:
                     yield tmp_dir
       else:
              yield working_dir



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=None					)->				Tuple:







       """simple docstring"""
       if is_numpy_array(snake_case__					):
              return np.transpose(snake_case__		,   axes=snake_case__					)
       elif is_torch_tensor(snake_case__					):
              return array.T if axes is None else array.permute(*snake_case__					)
       elif is_tf_tensor(snake_case__					):
              import tensorflow as tf

              return tf.transpose(snake_case__		,   perm=snake_case__					)
       elif is_jax_tensor(snake_case__					):
              return jnp.transpose(snake_case__		,   axes=snake_case__					)
       else:
              raise ValueError(F"Type not supported for transpose: {type(snake_case__					)}."					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       if is_numpy_array(snake_case__					):
              return np.reshape(snake_case__		,   snake_case__					)
       elif is_torch_tensor(snake_case__					):
              return array.reshape(*snake_case__					)
       elif is_tf_tensor(snake_case__					):
              import tensorflow as tf

              return tf.reshape(snake_case__		,   snake_case__					)
       elif is_jax_tensor(snake_case__					):
              return jnp.reshape(snake_case__		,   snake_case__					)
       else:
              raise ValueError(F"Type not supported for reshape: {type(snake_case__					)}."					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=None					)->				int:







       """simple docstring"""
       if is_numpy_array(snake_case__					):
              return np.squeeze(snake_case__		,   axis=snake_case__					)
       elif is_torch_tensor(snake_case__					):
              return array.squeeze() if axis is None else array.squeeze(dim=snake_case__					)
       elif is_tf_tensor(snake_case__					):
              import tensorflow as tf

              return tf.squeeze(snake_case__		,   axis=snake_case__					)
       elif is_jax_tensor(snake_case__					):
              return jnp.squeeze(snake_case__		,   axis=snake_case__					)
       else:
              raise ValueError(F"Type not supported for squeeze: {type(snake_case__					)}."					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       if is_numpy_array(snake_case__					):
              return np.expand_dims(snake_case__		,   snake_case__					)
       elif is_torch_tensor(snake_case__					):
              return array.unsqueeze(dim=snake_case__					)
       elif is_tf_tensor(snake_case__					):
              import tensorflow as tf

              return tf.expand_dims(snake_case__		,   axis=snake_case__					)
       elif is_jax_tensor(snake_case__					):
              return jnp.expand_dims(snake_case__		,   axis=snake_case__					)
       else:
              raise ValueError(F"Type not supported for expand_dims: {type(snake_case__					)}."					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       if is_numpy_array(snake_case__					):
              return np.size(snake_case__					)
       elif is_torch_tensor(snake_case__					):
              return array.numel()
       elif is_tf_tensor(snake_case__					):
              import tensorflow as tf

              return tf.size(snake_case__					)
       elif is_jax_tensor(snake_case__					):
              return array.size
       else:
              raise ValueError(F"Type not supported for expand_dims: {type(snake_case__					)}."					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       for key, value in auto_map.items():
              if isinstance(snake_case__		,   (tuple, list)					):
                     UpperCamelCase       			=		[F"{repo_id}--{v}" if (v is not None and """--""" not in v) else v for v in value]
              elif value is not None and "--" not in value:
                     UpperCamelCase       			=		F"{repo_id}--{value}"

       return auto_map



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       for base_class in inspect.getmro(snake_case__					):
              UpperCamelCase       			=		base_class.__module__
              UpperCamelCase       			=		base_class.__name__
              if module.startswith("tensorflow"					) or module.startswith("keras"					) or name == "TFPreTrainedModel":
                     return "tf"
              elif module.startswith("torch"					) or name == "PreTrainedModel":
                     return "pt"
              elif module.startswith("flax"					) or module.startswith("jax"					) or name == "FlaxPreTrainedModel":
                     return "flax"
       else:
              raise TypeError(F"Could not infer framework from class {model_class}."					)













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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=None , ):
	A_		:    int						     = input_ids.ne(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	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




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





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





	def __getitem__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = index + 1  # linecache starts at 1
		A_		:    int						     = self.prefix + linecache.getline(str(self.src_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		A_		:    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
		A_		:    Optional[Any]						     = (
		    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer
		)
		A_		:    Optional[int]						     = self.tokenizer.generator if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer

		A_		:    str						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_source_length  ,  """right"""				)
		A_		:    Optional[Any]						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_target_length  ,  """right"""				)

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





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





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






_lowerCAmelCase       =				getLogger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	return list(itertools.chain.from_iterable(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = get_git_info()
	save_json(snake_case__ , os.path.join(snake_case__ , """git_log.json"""       )       )






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






def       __UpperCamelCase						(		snake_case__       ):
	with open(snake_case__       ) as f:
		return json.load(snake_case__       )






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






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return list(map(snake_case__ , snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	with open(snake_case__ , """wb"""       ) as f:
		return pickle.dump(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):

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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__       )       )       )       )






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






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return normalize_answer(snake_case__       ) == normalize_answer(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	assert len(snake_case__       ) == len(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for hypo, pred in zip(snake_case__ , snake_case__       ):
		em += exact_match_score(snake_case__ , snake_case__       )
	if len(snake_case__       ) > 0:
		em /= len(snake_case__       )
	return {"em": em}






def       __UpperCamelCase						(		snake_case__       ):
	return model_prefix.startswith("""rag"""       )






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

