





'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 					snake_case					(   __lowerCamelCase      ,	__lowerCamelCase      ,	__lowerCamelCase	):




 """simple docstring"""



 @register_to_config
 def __init__(	self	:							List[str]							,     __A	:							int							,     __A	:							int							,     __A	:							int							,     __A	:							float							,     __A	:							int							,     __A	:							int							,     __A	:							int							,     __A	:							int							,     __A	:							str							,     __A	:							bool = False							,     ):
        super().__init__()

        __UpperCamelCase	=  nn.Linear(__A							,     __A							,     bias=__A     )

        __UpperCamelCase	=  nn.Embedding(__A							,     __A     )
        __UpperCamelCase	=  False

        __UpperCamelCase	=  nn.Dropout(p=__A     )

        __UpperCamelCase	=  TaConfig(
            d_model=__A							,     num_heads=__A							,     d_kv=__A							,     d_ff=__A							,     feed_forward_proj=__A							,     dropout_rate=__A							,     is_decoder=__A							,     is_encoder_decoder=__A							,     )
        __UpperCamelCase	=  nn.ModuleList()
        for lyr_num in range(__A     ):
               __UpperCamelCase	=  TaBlock(__A     )
               self.encoders.append(__A     )

        __UpperCamelCase	=  TaLayerNorm(__A     )
        __UpperCamelCase	=  nn.Dropout(p=__A     )






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							Any							,     __A	:							Tuple     ):
        __UpperCamelCase	=  self.input_proj(__A     )

        # terminal relative positional encodings
        __UpperCamelCase	=  encoder_inputs.shape[1]
        __UpperCamelCase	=  torch.arange(__A							,     device=encoder_inputs.device     )

        __UpperCamelCase	=  encoder_inputs_mask.sum(-1     )
        __UpperCamelCase	=  torch.roll(input_positions.unsqueeze(0     )							,     tuple(seq_lens.tolist()     )							,     dims=0     )
        x += self.position_encoding(__A     )

        __UpperCamelCase	=  self.dropout_pre(__A     )

        # inverted the attention mask
        __UpperCamelCase	=  encoder_inputs.size()
        __UpperCamelCase	=  self.get_extended_attention_mask(__A							,     __A     )

        for lyr in self.encoders:
               __UpperCamelCase	=  lyr(__A							,     __A     )[0]
        __UpperCamelCase	=  self.layer_norm(__A     )

        return self.dropout_post(__A     ), encoder_inputs_mask







'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


a__							:							Optional[Any]			=datasets.utils.logging.get_logger(__name__)

@dataclass
class 					snake_case					(   datasets.BuilderConfig	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[datasets.Features]      		=None






class 					snake_case					(   datasets.ArrowBasedBuilder	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=ArrowConfig






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return datasets.DatasetInfo(features=self.config.features     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Dict     ):
        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
        __UpperCamelCase	=  dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__A							,     (str, list, tuple)     ):
               __UpperCamelCase	=  data_files
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={'files': files}     )]
        __UpperCamelCase	=  []
        for split_name, files in data_files.items():
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(__A     ):
                             with open(__A							,     'rb'     ) as f:
                                    __UpperCamelCase	=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__A     ).schema     )
                             break
               splits.append(datasets.SplitGenerator(name=__A							,     gen_kwargs={'files': files}     )     )
        return splits






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							pa.Table     ):
        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __UpperCamelCase	=  table_cast(__A							,     self.info.features.arrow_schema     )
        return pa_table






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]     ):
        for file_idx, file in enumerate(itertools.chain.from_iterable(__A     )     ):
               with open(__A							,     'rb'     ) as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__A     )     ):
                                    __UpperCamelCase	=  pa.Table.from_batches([record_batch]     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield f'''{file_idx}_{batch_idx}''', self._cast_table(__A     )
                      except ValueError as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(__A     )}: {e}'''     )
                             raise
