
"""simple docstring"""

from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap




def     lowercase__						(					lowerCAmelCase				:					List[str] ) ->      Tuple:






				"""simple docstring"""




				return getitem, k




def     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					str ) ->      Any:






				"""simple docstring"""




				return setitem, k, v




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[str]:






				"""simple docstring"""




				return delitem, k




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					List[str] ,				*lowerCAmelCase				:					List[Any] ) ->      Optional[Any]:






				"""simple docstring"""




				try:
								return fun(lowerCAmelCase ,				*lowerCAmelCase ), None
				except Exception as e:
								return None, e


SCREAMING_SNAKE_CASE_    					=						(
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

SCREAMING_SNAKE_CASE_    					=						[
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

SCREAMING_SNAKE_CASE_    					=						[
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

SCREAMING_SNAKE_CASE_    					=						[
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

SCREAMING_SNAKE_CASE_    					=						[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

SCREAMING_SNAKE_CASE_    					=						[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('''key_a''', '''val_b'''),
]




@pytest.mark.parametrize(
    'operations' ,				(
        pytest.param(_add_items ,				id='add items' ),
        pytest.param(_overwrite_items ,				id='overwrite items' ),
        pytest.param(_delete_items ,				id='delete items' ),
        pytest.param(_access_absent_items ,				id='access absent items' ),
        pytest.param(_add_with_resize_up ,				id='add with resize up' ),
        pytest.param(_add_with_resize_down ,				id='add with resize down' ),
    ) ,				)
def     lowercase__						(					lowerCAmelCase				:					Optional[int] ) ->      Optional[Any]:






				"""simple docstring"""




				UpperCAmelCase									=					HashMap(initial_block_size=4 )
				UpperCAmelCase									=					{}
				for _, (fun, *args) in enumerate(lowerCAmelCase ):
								UpperCAmelCase			,							UpperCAmelCase									=					_run_operation(lowerCAmelCase ,				lowerCAmelCase ,				*lowerCAmelCase )
								UpperCAmelCase			,							UpperCAmelCase									=					_run_operation(lowerCAmelCase ,				lowerCAmelCase ,				*lowerCAmelCase )
								assert my_res == py_res
								assert str(lowerCAmelCase ) == str(lowerCAmelCase )
								assert set(lowerCAmelCase ) == set(lowerCAmelCase )
								assert len(lowerCAmelCase ) == len(lowerCAmelCase )
								assert set(my.items() ) == set(py.items() )







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






				"""simple docstring"""




				def is_public(lowerCAmelCase				:					str ) -> bool:
								return not name.startswith('_' )

				UpperCAmelCase									=					{name for name in dir({} ) if is_public(lowerCAmelCase )}
				UpperCAmelCase									=					{name for name in dir(HashMap() ) if is_public(lowerCAmelCase )}

				assert dict_public_names > hash_public_names





"""simple docstring"""

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,       'prajjwal1/bert-tiny'							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('bert-base-uncased'							)

          UpperCAmelCase									=					bertabert.config.encoder.vocab_size
          UpperCAmelCase									=					tokenizer.sep_token_id
          UpperCAmelCase									=					tokenizer.cls_token_id
          UpperCAmelCase									=					1_2_8

          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='train[:1%]'							)
          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='validation[:1%]'							)

          UpperCAmelCase									=					train_dataset.select(range(3_2							)							)
          UpperCAmelCase									=					val_dataset.select(range(1_6							)							)

          UpperCAmelCase									=					4

          def _map_to_encoder_decoder_inputs(lowercase_							):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase									=					tokenizer(batch['article']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=1_2_8							)
              UpperCAmelCase									=					inputs.input_ids
              UpperCAmelCase									=					inputs.attention_mask

              UpperCAmelCase									=					outputs.input_ids
              UpperCAmelCase									=					outputs.input_ids.copy()
              UpperCAmelCase									=					[
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase									=					outputs.attention_mask

              assert all(len(lowercase_							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(lowercase_							) == 1_2_8 for x in outputs.input_ids							)

              return batch

          def _compute_metrics(lowercase_							):
              UpperCAmelCase									=					pred.label_ids
              UpperCAmelCase									=					pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)

              UpperCAmelCase									=					sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowercase_							)							)]							) / len(lowercase_							)

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase									=					train_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=lowercase_   ,       batch_size=lowercase_   ,       remove_columns=['article', 'highlights']   ,       )
          train_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          # same for validation dataset
          UpperCAmelCase									=					val_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=lowercase_   ,       batch_size=lowercase_   ,       remove_columns=['article', 'highlights']   ,       )
          val_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          UpperCAmelCase									=					self.get_auto_remove_tmp_dir()

          UpperCAmelCase									=					SeqaSeqTrainingArguments(
              output_dir=lowercase_   ,       per_device_train_batch_size=lowercase_   ,       per_device_eval_batch_size=lowercase_   ,       predict_with_generate=lowercase_   ,       evaluation_strategy='steps'   ,       do_train=lowercase_   ,       do_eval=lowercase_   ,       warmup_steps=0   ,       eval_steps=2   ,       logging_steps=2   ,       )

          # instantiate trainer
          UpperCAmelCase									=					SeqaSeqTrainer(
              model=lowercase_   ,       args=lowercase_   ,       compute_metrics=_compute_metrics   ,       train_dataset=lowercase_   ,       eval_dataset=lowercase_   ,       tokenizer=lowercase_   ,       )

          # start training
          trainer.train()



