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

from utils import logger





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A= params

            _A= np.array(snake_case_       )
            _A= np.array([len(snake_case_       ) 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__       ):
            return (self.token_ids[index], self.lengths[index])



     def __len__(					self       ):
            return len(self.lengths       )



     def 			a__  (					self       ):
            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 			a__  (					self       ):
            _A= self.params.max_model_input_size
            _A= self.lengths > max_len
            logger.info(f"Splitting {sum(snake_case_       )} too long sequences."       )

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

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

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

            _A= np.array(snake_case_       )
            _A= np.array(snake_case_       )



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



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



     def 			a__  (					self       ):
            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 			a__  (					self				,      lowerCAmelCase__       ):
            _A= [t[0] for t in batch]
            _A= [t[1] for t in batch]
            assert len(snake_case_       ) == len(snake_case_       )

            # Max for paddings
            _A= max(snake_case_       )

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

            _A= torch.tensor(tk_       )  # (bs, max_seq_len_)
            _A= torch.tensor(snake_case_       )  # (bs)
            return tk_t, lg_t
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      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	3
            _A=	250

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



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

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

            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxLengthCriteria(max_length=10       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxNewTokensCriteria(start_length=5				,      max_new_tokens=5       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

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



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	MaxTimeCriteria(max_time=0.1       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	MaxTimeCriteria(max_time=0.1				,      initial_timestamp=time.time() - 0.2       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      10       )

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

            _A=	validate_stopping_criteria(StoppingCriteriaList()				,      11       )

            self.assertEqual(len(lowerCAmelCase__       )				,      1       )