
"""simple docstring"""




import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
  from ... import PreTrainedTokenizerBase, TensorType

A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''vision-encoder-decoder'''
       UpperCamelCase_ =		True
       def __init__(  self			,					**A_							)  ->						Any:





         """simple docstring"""




         super().__init__(**A_							)
         if "encoder" not in kwargs or "decoder" not in kwargs:
           raise ValueError(
               f'''A configuraton of type {self.model_type} cannot be instantiated because '''
               f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''							)

         _lowercase:      int   			=  kwargs.pop('''encoder'''							)
         _lowercase:      Union[str, Any]   			=  encoder_config.pop('''model_type'''							)
         _lowercase:      Optional[Any]   			=  kwargs.pop('''decoder'''							)
         _lowercase:      Optional[Any]   			=  decoder_config.pop('''model_type'''							)

         _lowercase:      List[Any]   			=  AutoConfig.for_model(A_			,					**A_							)
         _lowercase:      Optional[Any]   			=  AutoConfig.for_model(A_			,					**A_							)
         _lowercase:      Optional[Any]   			=  True
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					**A_							)  ->						PretrainedConfig:





         """simple docstring"""




         logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''							)
         _lowercase:      Dict   			=  True
         _lowercase:      Optional[int]   			=  True

         return cls(encoder=encoder_config.to_dict()			,					decoder=decoder_config.to_dict()			,					**A_							)



       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  copy.deepcopy(self.__dict__							)
         _lowercase:      Any   			=  self.encoder.to_dict()
         _lowercase:      Optional[int]   			=  self.decoder.to_dict()
         _lowercase:      Any   			=  self.__class__.model_type
         return output


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		version.parse('''1.11'''			)
       @property
       def 				lowercase_				(  self							)  ->						Mapping[str, Mapping[int, str]]:





         """simple docstring"""




         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]							)
       @property
       def 				lowercase_				(  self							)  ->						float:





         """simple docstring"""




         return 1E-4



       @property
       def 				lowercase_				(  self							)  ->						Mapping[str, Mapping[int, str]]:





         """simple docstring"""




         return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       @property
       def 				lowercase_				(  self							)  ->						Mapping[str, Mapping[int, str]]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  OrderedDict()
         _lowercase:      Optional[int]   			=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         _lowercase:      Any   			=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         _lowercase:      Dict   			=  {0: '''batch''', 1: '''encoder_sequence'''}

         return common_inputs



       def 				lowercase_				(  self			,					A_			,					A_ = -1			,					A_ = -1			,					A_ = False			,					A_ = None			,					)  ->						Mapping[str, Any]:





         """simple docstring"""




         import torch

         _lowercase:      List[Any]   			=  OrderedDict()

         _lowercase:      Optional[Any]   			=  super().generate_dummy_inputs(
             A_			,					batch_size=A_			,					seq_length=A_			,					is_pair=A_			,					framework=A_							)

         _lowercase			,					_lowercase:      List[Any]   			=  dummy_input['''input_ids'''].shape
         _lowercase:      Optional[int]   			=  (batch, encoder_sequence, self._config.encoder_hidden_size)
         _lowercase:      Optional[Any]   			=  dummy_input.pop('''input_ids'''							)
         _lowercase:      List[str]   			=  dummy_input.pop('''attention_mask'''							)
         _lowercase:      Dict   			=  torch.zeros(A_							)

         return common_inputs


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       @property
       def 				lowercase_				(  self							)  ->						None:





         """simple docstring"""




         pass
       def 				lowercase_				(  self			,					A_							)  ->						OnnxConfig:





         """simple docstring"""




         return VisionEncoderDecoderEncoderOnnxConfig(A_							)



       def 				lowercase_				(  self			,					A_			,					A_			,					A_ = "default"							)  ->						OnnxConfig:





         """simple docstring"""




         _lowercase:      str   			=  encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(A_			,					A_							)


"""simple docstring"""




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

from utils import logger


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      List[str]   			=  params

         _lowercase:      str   			=  np.array(A_							)
         _lowercase:      Optional[int]   			=  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			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return (self.token_ids[index], self.lengths[index])
       def __len__(  self							)  ->						List[str]:





         """simple docstring"""




         return len(self.lengths							)
       def 				lowercase_				(  self							)  ->						Dict:





         """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 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.params.max_model_input_size
         _lowercase:      Tuple   			=  self.lengths > max_len
         logger.info(f'''Splitting {sum(A_							)} too long sequences.'''							)

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

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

         _lowercase:      Optional[Any]   			=  np.array(A_							)
         _lowercase:      List[str]   			=  np.array(A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  len(self							)
         _lowercase:      Optional[Any]   			=  self.lengths > 11
         _lowercase:      int   			=  self.token_ids[indices]
         _lowercase:      List[str]   			=  self.lengths[indices]
         _lowercase:      Optional[int]   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:      List[str]   			=  [t[0] for t in batch]
         _lowercase:      Dict   			=  [t[1] for t in batch]
         assert len(A_							) == len(A_							)

         # Max for paddings
         _lowercase:      Tuple   			=  max(A_							)

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

         _lowercase:      str   			=  torch.tensor(tk_							)  # (bs, max_seq_len_)
         _lowercase:      int   			=  torch.tensor(A_							)  # (bs)
         return tk_t, lg_t
