


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

from utils import logger




class 	a__       (							SCREAMING_SNAKE_CASE__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			params

														lowerCAmelCase__      =			np.array(snake_case__     )
														lowerCAmelCase__      =			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_     ) ->							Optional[Any]:
														return (self.token_ids[index], self.lengths[index])







							def __len__(	self     ) ->							Optional[int]:
														return len(self.lengths     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			self.params.max_model_input_size
														lowerCAmelCase__      =			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__     )]

														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														if self.params.mlm:
																					lowerCAmelCase__      =			self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
														else:
																					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(snake_case__      ,					0      ,					snake_case__     )
																																			if sub_s[-1] != sep_id:
																																										lowerCAmelCase__      =			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]     )

														lowerCAmelCase__      =			np.array(snake_case__     )
														lowerCAmelCase__      =			np.array(snake_case__     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														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     ) ->							List[Any]:
														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]:
														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      ,					lowerCamelCase_     ) ->							List[Any]:
														lowerCAmelCase__      =			[t[0] for t in batch]
														lowerCAmelCase__      =			[t[1] for t in batch]
														assert len(snake_case__     ) == len(snake_case__     )

														# Max for paddings
														lowerCAmelCase__      =			max(snake_case__     )

														# 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(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_     )

														lowerCAmelCase__      =			torch.tensor(tk_     )  # (bs, max_seq_len_)
														lowerCAmelCase__      =			torch.tensor(snake_case__     )  # (bs)
														return tk_t, lg_t



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig