



'''simple docstring'''





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      test_file.split(os.path.sep		)
      if components[0:2] != ["tests", "models"]:
            raise ValueError(
                """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
                f"{test_file} instead."		)
      snake_case__  :    Dict    						=      components[-1]
      if not test_fn.endswith("""py"""		):
            raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead."		)
      if not test_fn.startswith("""test_modeling_"""		):
            raise ValueError(
                f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."		)

      snake_case__  :    int    						=      components[:-1] + [test_fn.replace(""".py"""     ,   """"""		)]
      snake_case__  :    int    						=      """.""".join(_lowerCAmelCase		)

      return test_module_path





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__  :    str    						=      get_module_path(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      importlib.import_module(_lowerCAmelCase		)

      return test_module





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    List[Any]    						=      []
      snake_case__  :    Optional[int]    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            if attr.endswith("""ModelTester"""		):
                  tester_classes.append(getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    List[str]    						=      []
      snake_case__  :    Any    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)
            # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
            # `all_model_classes` is not empty (which also excludes other special classes).
            snake_case__  :    List[str]    						=      getattr(_lowerCAmelCase     ,   """all_model_classes"""     ,   []		)
            if len(_lowerCAmelCase		) > 0:
                  test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    Any    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes		)

      # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Optional[int]    						=      test_class()
      if hasattr(_lowerCAmelCase     ,   """setUp"""		):
            test.setUp()

      snake_case__  :    Any    						=      None
      if hasattr(_lowerCAmelCase     ,   """model_tester"""		):
            # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
            if test.model_tester is not None:
                  snake_case__  :    Tuple    						=      test.model_tester.__class__

      return model_tester





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    Union[str, Any]    						=      get_test_classes(_lowerCAmelCase		)

      snake_case__  :    str    						=      []
      for test_class in test_classes:
            if model_class in test_class.all_model_classes:
                  target_test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Optional[Any]    						=      get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Union[str, Any]    						=      []
      for test_class in test_classes:
            snake_case__  :    Tuple    						=      get_model_tester_from_test_class(_lowerCAmelCase		)
            if tester_class is not None:
                  tester_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      snake_case__  :    Optional[Any]    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      {test_class: get_model_tester_from_test_class(_lowerCAmelCase		) for test_class in test_classes}
      return test_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Any    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    Any    						=      {
          model_class: get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_test_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Union[str, Any]    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    str    						=      {
          model_class: get_tester_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_to_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o.__name__
      elif isinstance(_lowerCAmelCase     ,   (list, tuple)		):
            return [to_json(_lowerCAmelCase		) for x in o]
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return {to_json(_lowerCAmelCase		): to_json(_lowerCAmelCase		) for k, v in o.items()}
      else:
            return o




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   Any  			=   params

        lowercase__:   List[Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   np.array([len(lowerCAmelCase__ ) for t in data] )

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()
 def __getitem__(   self				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        return (self.token_ids[index], self.lengths[index])
 def __len__(   self )					->				List[Any]:




        '''simple docstring'''
        return len(self.lengths )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        assert len(self.token_ids ) == len(self.lengths )
        assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   self.params.max_model_input_size
        lowercase__:   Dict  			=   self.lengths > max_len
        logger.info(F'Splitting {sum(lowerCAmelCase__ )} too long sequences.' )

        def divide_chunks(lowerCAmelCase__				, lowerCAmelCase__ ):
               return [l[i : i + n] for i in range(0				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )]

        lowercase__:   str  			=   []
        lowercase__:   List[str]  			=   []
        if self.params.mlm:
               lowercase__			,						lowercase__:   str  			=   self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
               lowercase__			,						lowercase__:   int  			=   self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

        for seq_, len_ in zip(self.token_ids				, self.lengths ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                      new_tok_ids.append(seq_ )
                      new_lengths.append(len_ )
               else:
                      lowercase__:   Optional[Any]  			=   []
                      for sub_s in divide_chunks(seq_				, max_len - 2 ):
                             if sub_s[0] != cls_id:
                                    lowercase__:   int  			=   np.insert(lowerCAmelCase__				, 0				, lowerCAmelCase__ )
                             if sub_s[-1] != sep_id:
                                    lowercase__:   Union[str, Any]  			=   np.insert(lowerCAmelCase__				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )
                             assert len(lowerCAmelCase__ ) <= max_len
                             assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                             sub_seqs.append(lowerCAmelCase__ )

                      new_tok_ids.extend(lowerCAmelCase__ )
                      new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )

        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Dict  			=   len(self )
        lowercase__:   Union[str, Any]  			=   self.lengths > 11
        lowercase__:   List[Any]  			=   self.token_ids[indices]
        lowercase__:   Optional[Any]  			=   self.lengths[indices]
        lowercase__:   List[str]  			=   len(self )
        logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        if "unk_token" not in self.params.special_tok_ids:
               return
        else:
               lowercase__:   List[str]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   str  			=   len(self )
        lowercase__:   Tuple  			=   np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
        lowercase__:   Any  			=   (unk_occs / self.lengths) < 0.5
        lowercase__:   Tuple  			=   self.token_ids[indices]
        lowercase__:   str  			=   self.lengths[indices]
        lowercase__:   Dict  			=   len(self )
        logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        if not self.params.is_master:
               return
        logger.info(F'{len(self )} sequences' )
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   [t[0] for t in batch]
        lowercase__:   Dict  			=   [t[1] for t in batch]
        assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )

        # Max for paddings
        lowercase__:   List[Any]  			=   max(lowerCAmelCase__ )

        # Pad token ids
        if self.params.mlm:
               lowercase__:   Dict  			=   self.params.special_tok_ids['pad_token']
        else:
               lowercase__:   Optional[Any]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   int  			=   [list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
        assert len(tk_ ) == len(lowerCAmelCase__ )
        assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )

        lowercase__:   Tuple  			=   torch.tensor(tk_ )  # (bs, max_seq_len_)
        lowercase__:   Optional[Any]  			=   torch.tensor(lowerCAmelCase__ )  # (bs)
        return tk_t, lg_t


