import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCAmelCase      :				str     			=logging.get_logger(__name__)




@dataclass
class 						__snake_case						:





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							42
      _snake_case     		=							None
      _snake_case     		=							None



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


          """simple docstring"""


          return json.dumps(dataclasses.asdict(self)     ,	indent=2) + "\n"





@dataclass(frozen=__lowerCAmelCase		)
class 						__snake_case						:





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							None



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


          """simple docstring"""


          return json.dumps(dataclasses.asdict(self)) + "\n"





class 						__snake_case						:





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int)					->str:


          """simple docstring"""


          raise NotImplementedError()



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


          """simple docstring"""


          raise NotImplementedError()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					List[Any])					->int:


          """simple docstring"""


          raise NotImplementedError()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					Optional[int])					->Dict:


          """simple docstring"""


          raise NotImplementedError()



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


          """simple docstring"""


          raise NotImplementedError()



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


          """simple docstring"""


          if len(self.get_labels()) > 1:
              _lowerCamelCase  :		Tuple			    =    self.get_labels()[int(example.label)]
          return example




      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					List[str]     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					List[Any]=None)					->Tuple:


          """simple docstring"""


          with open(_UpperCamelCase     ,	"""r"""     ,	encoding="""utf-8-sig""") as f:
              return list(csv.reader(_UpperCamelCase     ,	delimiter="""\t"""     ,	quotechar=_UpperCamelCase))





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					Optional[Any]     ,	_UpperCamelCase  :					str=None     ,	_UpperCamelCase  :					Any=None     ,	_UpperCamelCase  :					int="classification"     ,	_UpperCamelCase  :					Tuple=False)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    [] if labels is None else labels
          _lowerCamelCase  :		Tuple			    =    [] if examples is None else examples
          _lowerCamelCase  :		List[str]			    =    mode
          _lowerCamelCase  :		List[Any]			    =    verbose



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


          """simple docstring"""


          return len(self.examples)



      def __getitem__(       self  :					str     ,	_UpperCamelCase  :					int)					->List[str]:


          """simple docstring"""


          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              return SingleSentenceClassificationProcessor(labels=self.labels     ,	examples=self.examples[idx])
          return self.examples[idx]



      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					int     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Optional[Any]=""     ,	_UpperCamelCase  :					Any=0     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					List[Any]=None     ,	_UpperCamelCase  :					int=False     ,	**_UpperCamelCase  :					Dict)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    cls(**_UpperCamelCase)
          processor.add_examples_from_csv(
              _UpperCamelCase     ,	split_name=_UpperCamelCase     ,	column_label=_UpperCamelCase     ,	column_text=_UpperCamelCase     ,	column_id=_UpperCamelCase     ,	skip_first_row=_UpperCamelCase     ,	overwrite_labels=_UpperCamelCase     ,	overwrite_examples=_UpperCamelCase     ,	)
          return processor



      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					int     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Optional[int]=None     ,	**_UpperCamelCase  :					List[str])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    cls(**_UpperCamelCase)
          processor.add_examples(_UpperCamelCase     ,	labels=_UpperCamelCase)
          return processor



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Any=""     ,	_UpperCamelCase  :					Any=0     ,	_UpperCamelCase  :					Any=1     ,	_UpperCamelCase  :					Any=None     ,	_UpperCamelCase  :					Tuple=False     ,	_UpperCamelCase  :					Optional[Any]=False     ,	_UpperCamelCase  :					List[str]=False     ,	)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self._read_tsv(_UpperCamelCase)
          if skip_first_row:
              _lowerCamelCase  :		Optional[int]			    =    lines[1:]
          _lowerCamelCase  :		int			    =    []
          _lowerCamelCase  :		str			    =    []
          _lowerCamelCase  :		List[str]			    =    []
          for i, line in enumerate(_UpperCamelCase):
              texts.append(line[column_text])
              labels.append(line[column_label])
              if column_id is not None:
                  ids.append(line[column_id])
              else:
                  _lowerCamelCase  :		Dict			    =    F"""{split_name}-{i}""" if split_name else str(_UpperCamelCase)
                  ids.append(_UpperCamelCase)

          return self.add_examples(
              _UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	overwrite_labels=_UpperCamelCase     ,	overwrite_examples=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					List[str]=None     ,	_UpperCamelCase  :					Any=None     ,	_UpperCamelCase  :					List[str]=False     ,	_UpperCamelCase  :					str=False)					->Optional[Any]:


          """simple docstring"""


          if labels is not None and len(_UpperCamelCase) != len(_UpperCamelCase):
              raise ValueError(
                  F"""Text and labels have mismatched lengths {len(_UpperCamelCase)} and {len(_UpperCamelCase)}""")
          if ids is not None and len(_UpperCamelCase) != len(_UpperCamelCase):
              raise ValueError(F"""Text and ids have mismatched lengths {len(_UpperCamelCase)} and {len(_UpperCamelCase)}""")
          if ids is None:
              _lowerCamelCase  :		Optional[Any]			    =    [None] * len(_UpperCamelCase)
          if labels is None:
              _lowerCamelCase  :		str			    =    [None] * len(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    []
          _lowerCamelCase  :		Any			    =    set()
          for text_or_text_and_label, label, guid in zip(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase):
              if isinstance(_UpperCamelCase     ,	(tuple, list)) and label is None:
                  _lowerCamelCase    ,				_lowerCamelCase  :		int			    =    text_or_text_and_label
              else:
                  _lowerCamelCase  :		Optional[Any]			    =    text_or_text_and_label
              added_labels.add(_UpperCamelCase)
              examples.append(InputExample(guid=_UpperCamelCase     ,	text_a=_UpperCamelCase     ,	text_b=_UpperCamelCase     ,	label=_UpperCamelCase))

          # Update examples
          if overwrite_examples:
              _lowerCamelCase  :		int			    =    examples
          else:
              self.examples.extend(_UpperCamelCase)

          # Update labels
          if overwrite_labels:
              _lowerCamelCase  :		str			    =    list(_UpperCamelCase)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    list(set(self.labels).union(_UpperCamelCase))

          return self.examples




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict=None     ,	_UpperCamelCase  :					Optional[Any]=False     ,	_UpperCamelCase  :					Tuple=0     ,	_UpperCamelCase  :					Optional[int]=True     ,	_UpperCamelCase  :					Optional[Any]=None     ,	)					->int:


          """simple docstring"""


          if max_length is None:
              _lowerCamelCase  :		str			    =    tokenizer.max_len

          _lowerCamelCase  :		List[str]			    =    {label: i for i, label in enumerate(self.labels)}

          _lowerCamelCase  :		Optional[int]			    =    []
          for ex_index, example in enumerate(self.examples):
              if ex_index % 1_0000 == 0:
                  logger.info(F"""Tokenizing example {ex_index}""")

              _lowerCamelCase  :		Union[str, Any]			    =    tokenizer.encode(
                  example.text_a     ,	add_special_tokens=_UpperCamelCase     ,	max_length=min(_UpperCamelCase     ,	tokenizer.max_len)     ,	)
              all_input_ids.append(_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    max(len(_UpperCamelCase) for input_ids in all_input_ids)

          _lowerCamelCase  :		Optional[int]			    =    []
          for ex_index, (input_ids, example) in enumerate(zip(_UpperCamelCase     ,	self.examples)):
              if ex_index % 1_0000 == 0:
                  logger.info(F"""Writing example {ex_index}/{len(self.examples)}""")
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              _lowerCamelCase  :		Optional[int]			    =    [1 if mask_padding_with_zero else 0] * len(_UpperCamelCase)

              # Zero-pad up to the sequence length.
              _lowerCamelCase  :		Dict			    =    batch_length - len(_UpperCamelCase)
              if pad_on_left:
                  _lowerCamelCase  :		List[Any]			    =    ([pad_token] * padding_length) + input_ids
                  _lowerCamelCase  :		str			    =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
              else:
                  _lowerCamelCase  :		Union[str, Any]			    =    input_ids + ([pad_token] * padding_length)
                  _lowerCamelCase  :		Optional[int]			    =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

              if len(_UpperCamelCase) != batch_length:
                  raise ValueError(F"""Error with input length {len(_UpperCamelCase)} vs {batch_length}""")
              if len(_UpperCamelCase) != batch_length:
                  raise ValueError(F"""Error with input length {len(_UpperCamelCase)} vs {batch_length}""")

              if self.mode == "classification":
                  _lowerCamelCase  :		Any			    =    label_map[example.label]
              elif self.mode == "regression":
                  _lowerCamelCase  :		Optional[Any]			    =    float(example.label)
              else:
                  raise ValueError(self.mode)

              if ex_index < 5 and self.verbose:
                  logger.info("""*** Example ***""")
                  logger.info(F"""guid: {example.guid}""")
                  logger.info(F"""input_ids: {' '.join([str(_UpperCamelCase) for x in input_ids])}""")
                  logger.info(F"""attention_mask: {' '.join([str(_UpperCamelCase) for x in attention_mask])}""")
                  logger.info(F"""label: {example.label} (id = {label})""")

              features.append(InputFeatures(input_ids=_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	label=_UpperCamelCase))

          if return_tensors is None:
              return features
          elif return_tensors == "tf":
              if not is_tf_available():
                  raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported""")
              import tensorflow as tf

              def gen():
                  for ex in features:
                      yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

              _lowerCamelCase  :		int			    =    tf.data.Dataset.from_generator(
                  _UpperCamelCase     ,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,	({"""input_ids""": tf.TensorShape([None]), """attention_mask""": tf.TensorShape([None])}, tf.TensorShape([]))     ,	)
              return dataset
          elif return_tensors == "pt":
              if not is_torch_available():
                  raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported""")
              import torch
              from torch.utils.data import TensorDataset

              _lowerCamelCase  :		str			    =    torch.tensor([f.input_ids for f in features]     ,	dtype=torch.long)
              _lowerCamelCase  :		Optional[Any]			    =    torch.tensor([f.attention_mask for f in features]     ,	dtype=torch.long)
              if self.mode == "classification":
                  _lowerCamelCase  :		List[str]			    =    torch.tensor([f.label for f in features]     ,	dtype=torch.long)
              elif self.mode == "regression":
                  _lowerCamelCase  :		str			    =    torch.tensor([f.label for f in features]     ,	dtype=torch.float)

              _lowerCamelCase  :		Dict			    =    TensorDataset(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)
              return dataset
          else:
              raise ValueError("""return_tensors should be one of 'tf' or 'pt'""")





import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def     A__	(       __A ,			__A ,			__A ,			__A=None						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    None
    # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
    if str(weight.dtype						) == "torch.bfloat16":
        # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
        _lowerCamelCase  :		int			    =    weight.view(torch.intaa						)
        _lowerCamelCase  :		List[Any]			    =    """bfloat16"""
    _lowerCamelCase  :		Tuple			    =    weight.cpu().numpy()
    _lowerCamelCase  :		Tuple			    =    os.path.join(__A ,			F"""{weight_name}.dat"""						)
    if index is not None:
        if dtype is None:
            _lowerCamelCase  :		Union[str, Any]			    =    str(array.dtype						)
        _lowerCamelCase  :		Optional[Any]			    =    {"""dtype""": dtype, """shape""": list(array.shape						)}
    if array.ndim == 0:
        _lowerCamelCase  :		int			    =    array[None]
    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=array.dtype ,			mode="""w+""" ,			shape=array.shape						)
    _lowerCamelCase  :		Union[str, Any]			    =    array[:]
    file_array.flush()
    return index







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    tuple(weight_info["""shape"""]						)
    if shape == ():
        # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
        _lowerCamelCase  :		Union[str, Any]			    =    (1,)

    _lowerCamelCase  :		str			    =    weight_info["""dtype"""]
    if dtype == "bfloat16":
        # NumPy does not support bfloat16 so this was saved as a int16
        _lowerCamelCase  :		Optional[int]			    =    """int16"""

    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=__A ,			shape=__A ,			mode="""r"""						)

    if len(weight_info["""shape"""]						) == 0:
        _lowerCamelCase  :		List[str]			    =    weight[0]
    _lowerCamelCase  :		List[str]			    =    torch.tensor(__A						)
    if weight_info["dtype"] == "bfloat16":
        _lowerCamelCase  :		List[Any]			    =    weight.view(torch.bfloataa						)

    return weight







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    if index is None or len(__A						) == 0:
        # Nothing to save
        return

    _lowerCamelCase  :		List[Any]			    =    os.path.join(__A ,			"""index.json"""						)
    if os.path.isfile(__A						):
        with open(__A ,			"""r""" ,			encoding="""utf-8"""						) as f:
            _lowerCamelCase  :		List[str]			    =    json.load(__A						)
    else:
        _lowerCamelCase  :		List[str]			    =    {}
    current_index.update(__A						)

    with open(__A ,			"""w""" ,			encoding="""utf-8"""						) as f:
        json.dump(__A ,			__A ,			indent=2						)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    os.makedirs(__A ,			exist_ok=__A						)
    _lowerCamelCase  :		Dict			    =    {}
    for name, parameter in state_dict.items():
        _lowerCamelCase  :		Union[str, Any]			    =    offload_weight(__A ,			__A ,			__A ,			index=__A						)

    # Update index
    save_offload_index(__A ,			__A						)




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					str     ,	_UpperCamelCase  :					Mapping     ,	_UpperCamelCase  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    dataset
          _lowerCamelCase  :		Tuple			    =    prefix



      def __getitem__(       self  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          return self.dataset[F"""{self.prefix}{key}"""]



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


          """simple docstring"""


          return iter([key for key in self.dataset if key.startswith(self.prefix)])




      def __len__(       self  :					Any)					->int:


          """simple docstring"""


          return len(self.dataset)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict[str, torch.Tensor] = None     ,	_UpperCamelCase  :					Optional[Union[str, os.PathLike]] = None     ,	_UpperCamelCase  :					Mapping = None     ,	_UpperCamelCase  :					Union[str, Any]=None     ,	)					->Any:


          """simple docstring"""


          if state_dict is None and save_folder is None:
              raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights.""")

          _lowerCamelCase  :		int			    =    {} if state_dict is None else state_dict
          _lowerCamelCase  :		Optional[int]			    =    save_folder
          if index is None and save_folder is not None:
              with open(os.path.join(_UpperCamelCase     ,	"""index.json""")) as f:
                  _lowerCamelCase  :		Union[str, Any]			    =    json.load(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    {} if index is None else index
          _lowerCamelCase  :		Optional[int]			    =    list(self.state_dict.keys())
          self.all_keys.extend([key for key in self.index if key not in self.all_keys])
          _lowerCamelCase  :		str			    =    device



      def __getitem__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          if key in self.state_dict:
              return self.state_dict[key]
          _lowerCamelCase  :		int			    =    self.index[key]
          if weight_info.get("""safetensors_file""") is not None:
              if not is_safetensors_available():
                  raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`.""")

              from safetensors import safe_open

              _lowerCamelCase  :		Any			    =    """cpu""" if self.device is None else self.device
              with safe_open(weight_info["""safetensors_file"""]     ,	framework="""pt"""     ,	device=_UpperCamelCase) as f:
                  _lowerCamelCase  :		Tuple			    =    f.get_tensor(weight_info.get("""weight_name"""     ,	_UpperCamelCase))

              if "dtype" in weight_info:
                  return tensor.to(getattr(_UpperCamelCase     ,	weight_info["""dtype"""]))
              else:
                  return tensor

          _lowerCamelCase  :		Optional[Any]			    =    os.path.join(self.save_folder     ,	F"""{key}.dat""")
          return load_offloaded_weight(_UpperCamelCase     ,	_UpperCamelCase)



      def __iter__(       self  :					Dict)					->List[Any]:


          """simple docstring"""


          return iter(self.all_keys)




      def __len__(       self  :					int)					->Any:


          """simple docstring"""


          return len(self.all_keys)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		str			    =    {}
    for module_name in submodule_names:
        # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
        # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
        result.update(
            {
                key: param
                for key, param in state_dict.items()
                if key == module_name or key.startswith(module_name + """."""						)
            }						)
    return result




