from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE:						Optional[int]    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						str    	=       {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''led'''
		A_						 =      {
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''hidden_size''': '''d_model''',
		    '''attention_probs_dropout_prob''': '''attention_dropout''',
		    '''initializer_range''': '''init_std''',
		}



		def __init__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=50_265  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=16_384  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=1_024  ,					__SCREAMING_SNAKE_CASE    :				str=12  ,					__SCREAMING_SNAKE_CASE    :				str=4_096  ,					__SCREAMING_SNAKE_CASE    :				Dict=16  ,					__SCREAMING_SNAKE_CASE    :				Dict=12  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=4_096  ,					__SCREAMING_SNAKE_CASE    :				Dict=16  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=0.0  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=0.0  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]="gelu"  ,					__SCREAMING_SNAKE_CASE    :				List[str]=1_024  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0.1  ,					__SCREAMING_SNAKE_CASE    :				Dict=0.0  ,					__SCREAMING_SNAKE_CASE    :				str=0.0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0.0_2  ,					__SCREAMING_SNAKE_CASE    :				Dict=2  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=0.0  ,					__SCREAMING_SNAKE_CASE    :				Any=1  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0  ,					__SCREAMING_SNAKE_CASE    :				Any=2  ,					__SCREAMING_SNAKE_CASE    :				Union[List[int], int] = 512  ,					**__SCREAMING_SNAKE_CASE    :				int  ,					):






					"""simple docstring"""
					_snake_case              =      vocab_size
					_snake_case              =      max_encoder_position_embeddings
					_snake_case              =      max_decoder_position_embeddings
					_snake_case              =      d_model
					_snake_case              =      encoder_ffn_dim
					_snake_case              =      encoder_layers
					_snake_case              =      encoder_attention_heads
					_snake_case              =      decoder_ffn_dim
					_snake_case              =      decoder_layers
					_snake_case              =      decoder_attention_heads
					_snake_case              =      dropout
					_snake_case              =      attention_dropout
					_snake_case              =      activation_dropout
					_snake_case              =      activation_function
					_snake_case              =      init_std
					_snake_case              =      encoder_layerdrop
					_snake_case              =      decoder_layerdrop
					_snake_case              =      classifier_dropout
					_snake_case              =      use_cache
					_snake_case              =      encoder_layers
					_snake_case              =      attention_window

					super().__init__(
					    pad_token_id=__SCREAMING_SNAKE_CASE  ,					bos_token_id=__SCREAMING_SNAKE_CASE  ,					eos_token_id=__SCREAMING_SNAKE_CASE  ,					is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,					decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE  ,					)


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

@dataclass
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )  ,					indent=2   ) + "\n"

@dataclass(frozen=_lowercase							)
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      None
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )   ) + "\n"

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					if len(self.get_labels()   ) > 1:
								_snake_case              =      self.get_labels()[int(example.label   )]
					return example







		@classmethod
		def 				_a				(			cls    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None   ):






					"""simple docstring"""
					with open(__SCREAMING_SNAKE_CASE  ,					"r"  ,					encoding="utf-8-sig"   ) as f:
								return list(csv.reader(__SCREAMING_SNAKE_CASE  ,					delimiter="\t"  ,					quotechar=__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]="classification"  ,					__SCREAMING_SNAKE_CASE    :				Any=False   ):






					"""simple docstring"""
					_snake_case              =      [] if labels is None else labels
					_snake_case              =      [] if examples is None else examples
					_snake_case              =      mode
					_snake_case              =      verbose



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					return len(self.examples   )



		def __getitem__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					if isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								return SingleSentenceClassificationProcessor(labels=self.labels  ,					examples=self.examples[idx]   )
					return self.examples[idx]



		@classmethod
		def 				_a				(			cls    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=""  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0  ,					__SCREAMING_SNAKE_CASE    :				Dict=1  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=False  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples_from_csv(
					    __SCREAMING_SNAKE_CASE  ,					split_name=__SCREAMING_SNAKE_CASE  ,					column_label=__SCREAMING_SNAKE_CASE  ,					column_text=__SCREAMING_SNAKE_CASE  ,					column_id=__SCREAMING_SNAKE_CASE  ,					skip_first_row=__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE  ,					)
					return processor



		@classmethod
		def 				_a				(			cls    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples(__SCREAMING_SNAKE_CASE  ,					labels=__SCREAMING_SNAKE_CASE   )
					return processor



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict=""  ,					__SCREAMING_SNAKE_CASE    :				str=0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=False  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				str=False  ,					):






					"""simple docstring"""
					_snake_case              =      self._read_tsv(__SCREAMING_SNAKE_CASE   )
					if skip_first_row:
								_snake_case              =      lines[1:]
					_snake_case              =      []
					_snake_case              =      []
					_snake_case              =      []
					for i, line in enumerate(__SCREAMING_SNAKE_CASE   ):
								texts.append(line[column_text]   )
								labels.append(line[column_label]   )
								if column_id is not None:
											ids.append(line[column_id]   )
								else:
											_snake_case              =      F'{split_name}-{i}' if split_name else str(__SCREAMING_SNAKE_CASE   )
											ids.append(__SCREAMING_SNAKE_CASE   )

					return self.add_examples(
					    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False  ,					__SCREAMING_SNAKE_CASE    :				str=False   ):






					"""simple docstring"""
					if labels is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(
								    F'Text and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(F'Text and ids have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					if labels is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					_snake_case              =      set()
					for text_or_text_and_label, label, guid in zip(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								if isinstance(__SCREAMING_SNAKE_CASE  ,					(tuple, list)   ) and label is None:
											_snake_case       , _snake_case              =      text_or_text_and_label
								else:
											_snake_case              =      text_or_text_and_label
								added_labels.add(__SCREAMING_SNAKE_CASE   )
								examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,					text_a=__SCREAMING_SNAKE_CASE  ,					text_b=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					# Update examples
					if overwrite_examples:
								_snake_case              =      examples
					else:
								self.examples.extend(__SCREAMING_SNAKE_CASE   )

					# Update labels
					if overwrite_labels:
								_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      list(set(self.labels   ).union(__SCREAMING_SNAKE_CASE   )   )

					return self.examples







		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				Any=0  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					):






					"""simple docstring"""
					if max_length is None:
								_snake_case              =      tokenizer.max_len

					_snake_case              =      {label: i for i, label in enumerate(self.labels   )}

					_snake_case              =      []
					for ex_index, example in enumerate(self.examples   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Tokenizing example {ex_index}'   )

								_snake_case              =      tokenizer.encode(
								    example.text_a  ,					add_special_tokens=__SCREAMING_SNAKE_CASE  ,					max_length=min(__SCREAMING_SNAKE_CASE  ,					tokenizer.max_len   )  ,					)
								all_input_ids.append(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      max(len(__SCREAMING_SNAKE_CASE   ) for input_ids in all_input_ids   )

					_snake_case              =      []
					for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,					self.examples   )   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Writing example {ex_index}/{len(self.examples   )}'   )
								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								_snake_case              =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE   )

								# Zero-pad up to the sequence length.
								_snake_case              =      batch_length - len(__SCREAMING_SNAKE_CASE   )
								if pad_on_left:
											_snake_case              =      ([pad_token] * padding_length) + input_ids
											_snake_case              =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
								else:
											_snake_case              =      input_ids + ([pad_token] * padding_length)
											_snake_case              =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )
								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )

								if self.mode == "classification":
											_snake_case              =      label_map[example.label]
								elif self.mode == "regression":
											_snake_case              =      float(example.label   )
								else:
											raise ValueError(self.mode   )

								if ex_index < 5 and self.verbose:
											logger.info("*** Example ***"   )
											logger.info(F'guid: {example.guid}'   )
											logger.info(F'input_ids: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in input_ids]   )}'   )
											logger.info(F'attention_mask: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in attention_mask]   )}'   )
											logger.info(F'label: {example.label} (id = {label})'   )

								features.append(InputFeatures(input_ids=__SCREAMING_SNAKE_CASE  ,					attention_mask=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					if return_tensors is None:
								return features
					elif return_tensors == "tf":
								if not is_tf_available():
											raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"   )
								import tensorflow as tf

								def gen():
											for ex in features:
														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

								_snake_case              =      tf.data.Dataset.from_generator(
								    __SCREAMING_SNAKE_CASE  ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,					({"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )}, tf.TensorShape([]   ))  ,					)
								return dataset
					elif return_tensors == "pt":
								if not is_torch_available():
											raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"   )
								import torch
								from torch.utils.data import TensorDataset

								_snake_case              =      torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long   )
								_snake_case              =      torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long   )
								if self.mode == "classification":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.long   )
								elif self.mode == "regression":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.float   )

								_snake_case              =      TensorDataset(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								return dataset
					else:
								raise ValueError("return_tensors should be one of 'tf' or 'pt'"   )

