






import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class 					_snake_case						(			datasets.BuilderConfig ):
	_A          =   None




class 					_snake_case						(			datasets.ArrowBasedBuilder ):
	_A          =   PandasConfig






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return datasets.DatasetInfo(features=self.config.features       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Tuple:

					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}'       )
					snake_case__      :Optional[Any]         =   dl_manager.download_and_extract(self.config.data_files       )
					if isinstance(_A					,(str, list, tuple)       ):
									snake_case__      :Tuple         =   data_files
									if isinstance(_A					,_A       ):
													snake_case__      :List[str]         =   [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									snake_case__      :List[str]         =   [dl_manager.iter_files(_A       ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,gen_kwargs={"files": files}       )]
					snake_case__      :Any         =   []
					for split_name, files in data_files.items():
									if isinstance(_A					,_A       ):
													snake_case__      :int         =   [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									snake_case__      :List[str]         =   [dl_manager.iter_files(_A       ) for file in files]
									splits.append(datasets.SplitGenerator(name=_A					,gen_kwargs={"files": files}       )       )
					return splits






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[str]:

					if self.config.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
									snake_case__      :Optional[int]         =   table_cast(_A					,self.config.features.arrow_schema       )
					return pa_table






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Dict:

					for i, file in enumerate(itertools.chain.from_iterable(_A       )       ):
									with open(_A					,"rb"       ) as f:
													snake_case__      :List[Any]         =   pa.Table.from_pandas(pd.read_pickle(_A       )       )
													yield i, self._cast_table(_A       )







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						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask