'''simple docstring'''




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

from utils import logger




class    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		str  ,    a		:		Optional[int]  ,    a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  params

         __lowerCamelCase          =  np.array(a   )
         __lowerCamelCase          =  np.array([len(a   ) 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		:		Any  ,    a		:		Optional[Any]   ):





         """simple docstring"""



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






     def __len__(			self		:		int   ):





         """simple docstring"""



         return len(self.lengths   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """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		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  self.params.max_model_input_size
         __lowerCamelCase          =  self.lengths > max_len
         logger.info(f"""Splitting {sum(a   )} too long sequences."""   )

         def divide_chunks(a		:		Union[str, Any]  ,    a		:		Optional[int]   ):
             return [l[i : i + n] for i in range(0  ,    len(a   )  ,    a   )]

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

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

         __lowerCamelCase          =  np.array(a   )
         __lowerCamelCase          =  np.array(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """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		:		Dict  ,    a		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  [t[0] for t in batch]
         __lowerCamelCase          =  [t[1] for t in batch]
         assert len(a   ) == len(a   )

         # Max for paddings
         __lowerCamelCase          =  max(a   )

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

         __lowerCamelCase          =  torch.tensor(tk_   )  # (bs, max_seq_len_)
         __lowerCamelCase          =  torch.tensor(a   )  # (bs)
         return tk_t, lg_t

'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )




@require_torch
class    a__				(       unittest.TestCase			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  3
         __lowerCamelCase          =  2_50

         __lowerCamelCase          =  ids_tensor((batch_size, length)  ,    a   )
         __lowerCamelCase          =  torch.ones((batch_size, length)  ,    device=a  ,    dtype=torch.float   ) / length
         return input_ids, scores






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=10   ),
                 MaxTimeCriteria(max_time=0.1   ),
             ]   )

         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxLengthCriteria(max_length=10   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxNewTokensCriteria(start_length=5  ,    max_new_tokens=5   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )

         __lowerCamelCase          =  StoppingCriteriaList([criteria]   )
         self.assertEqual(criteria_list.max_length  ,    10   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1  ,    initial_timestamp=time.time() - 0.2   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )  ,    10   )

         with self.assertWarns(a   ):
             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )  ,    11   )

         __lowerCamelCase          =  validate_stopping_criteria(StoppingCriteriaList()  ,    11   )

         self.assertEqual(len(a   )  ,    1   )
