"""simple docstring"""


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

from utils import logger






class    a__    (   lowercase__	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							params

        lowercase__				 =							np.array(__lowercase  )
        lowercase__				 =							np.array([len(__lowercase  ) 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,				_UpperCAmelCase  ):
        '''simple docstring'''



        return (self.token_ids[index], self.lengths[index])


   def __len__(     self  ):
        '''simple docstring'''



        return len(self.lengths  )


   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.params.max_model_input_size
        lowercase__				 =							self.lengths > max_len
        logger.info(F'''Splitting {sum(__lowercase  )} too long sequences.'''  )

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

        lowercase__				 =							[]
        lowercase__				 =							[]
        if self.params.mlm:
             lowercase__ ,						lowercase__				 =							self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
        else:
             lowercase__ ,						lowercase__				 =							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__				 =							[]
                  for sub_s in divide_chunks(seq_,				max_len - 2  ):
                       if sub_s[0] != cls_id:
                            lowercase__				 =							np.insert(__lowercase,				0,				__lowercase  )
                       if sub_s[-1] != sep_id:
                            lowercase__				 =							np.insert(__lowercase,				len(__lowercase  ),				__lowercase  )
                       assert len(__lowercase  ) <= max_len
                       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                       sub_seqs.append(__lowercase  )

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

        lowercase__				 =							np.array(__lowercase  )
        lowercase__				 =							np.array(__lowercase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							len(self  )
        lowercase__				 =							self.lengths > 11
        lowercase__				 =							self.token_ids[indices]
        lowercase__				 =							self.lengths[indices]
        lowercase__				 =							len(self  )
        logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[t[0] for t in batch]
        lowercase__				 =							[t[1] for t in batch]
        assert len(__lowercase  ) == len(__lowercase  )

        # Max for paddings
        lowercase__				 =							max(__lowercase  )

        # Pad token ids
        if self.params.mlm:
             lowercase__				 =							self.params.special_tok_ids["pad_token"]
        else:
             lowercase__				 =							self.params.special_tok_ids["unk_token"]
        lowercase__				 =							[list(t.astype(__lowercase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowercase  )) for t in token_ids]
        assert len(tk_  ) == len(__lowercase  )
        assert all(len(__lowercase  ) == max_seq_len_ for t in tk_  )

        lowercase__				 =							torch.tensor(tk_  )  # (bs, max_seq_len_)
        lowercase__				 =							torch.tensor(__lowercase  )  # (bs)
        return tk_t, lg_t





"""simple docstring"""


import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
 import jax

 from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
 from transformers.models.bert.modeling_flax_bert import FlaxBertModel
 from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class    a__    (   unittest.TestCase	):


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["bert-base-cased", "bert-large-uncased"]:
             with self.subTest(_UpperCAmelCase  ):
                  lowercase__				 =							AutoConfig.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["roberta-base", "roberta-large"]:
             with self.subTest(_UpperCAmelCase  ):
                  lowercase__				 =							AutoConfig.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["bert-base-cased", "bert-large-uncased"]:
             lowercase__				 =							AutoTokenizer.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							FlaxBertModel.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							tokenizer("Do you support jax jitted function?",				return_tensors=TensorType.JAX  )

             @jax.jit
             def eval(**_UpperCAmelCase  ):
                  return model(**_UpperCAmelCase  )

             eval(**_UpperCAmelCase  ).block_until_ready()


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["roberta-base", "roberta-large"]:
             lowercase__				 =							AutoTokenizer.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							FlaxRobertaModel.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							tokenizer("Do you support jax jitted function?",				return_tensors=TensorType.JAX  )

             @jax.jit
             def eval(**_UpperCAmelCase  ):
                  return model(**_UpperCAmelCase  )

             eval(**_UpperCAmelCase  ).block_until_ready()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				"bert-base is not a local folder and is not a valid model identifier"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained("bert-base"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase,				revision="aaaaaa"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack",				):
             lowercase__				 =							FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(_UpperCAmelCase,				"Use `from_pt=True` to load this model"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"  )




