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

from utils import logger



class       UpperCamelCase_      (    snake_case_ ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						str:
										UpperCamelCase__     :		int	 =					params

										UpperCamelCase__     :		Dict	 =					np.array(__lowercase)
										UpperCamelCase__     :		Any	 =					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)			->						List[str]:
										return (self.token_ids[index], self.lengths[index])
							def __len__(					self)			->						Dict:
										return len(self.lengths)
							def 							lowerCAmelCase__			(					self)			->						Optional[Any]:
										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 							lowerCAmelCase__			(					self)			->						Optional[int]:
										UpperCamelCase__     :		Union[str, Any]	 =					self.params.max_model_input_size
										UpperCamelCase__     :		Optional[int]	 =					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)]

										UpperCamelCase__     :		int	 =					[]
										UpperCamelCase__     :		int	 =					[]
										if self.params.mlm:
													UpperCamelCase__     :		List[str]	 =					self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
										else:
													UpperCamelCase__     :		Optional[Any]	 =					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:
																UpperCamelCase__     :		int	 =					[]
																for sub_s in divide_chunks(seq_  ,    max_len - 2):
																			if sub_s[0] != cls_id:
																						UpperCamelCase__     :		Optional[int]	 =					np.insert(__lowercase  ,    0  ,    __lowercase)
																			if sub_s[-1] != sep_id:
																						UpperCamelCase__     :		List[str]	 =					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])

										UpperCamelCase__     :		str	 =					np.array(__lowercase)
										UpperCamelCase__     :		Dict	 =					np.array(__lowercase)
							def 							lowerCAmelCase__			(					self)			->						Dict:
										UpperCamelCase__     :		List[str]	 =					len(self)
										UpperCamelCase__     :		Optional[Any]	 =					self.lengths > 11
										UpperCamelCase__     :		Any	 =					self.token_ids[indices]
										UpperCamelCase__     :		int	 =					self.lengths[indices]
										UpperCamelCase__     :		Any	 =					len(self)
										logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""")
							def 							lowerCAmelCase__			(					self)			->						List[str]:







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







										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 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						str:
										UpperCamelCase__     :		Any	 =					[t[0] for t in batch]
										UpperCamelCase__     :		int	 =					[t[1] for t in batch]
										assert len(__lowercase) == len(__lowercase)

										# Max for paddings
										UpperCamelCase__     :		str	 =					max(__lowercase)

										# Pad token ids
										if self.params.mlm:
													UpperCamelCase__     :		Union[str, Any]	 =					self.params.special_tok_ids['pad_token']
										else:
													UpperCamelCase__     :		int	 =					self.params.special_tok_ids['unk_token']
										UpperCamelCase__     :		Tuple	 =					[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_)

										UpperCamelCase__     :		str	 =					torch.tensor(tk_)  # (bs, max_seq_len_)
										UpperCamelCase__     :		Dict	 =					torch.tensor(__lowercase)  # (bs)
										return tk_t, lg_t







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[int]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






