






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__snake_case			:			Optional[Any]			   =   logging.get_logger(__name__)

__snake_case			:			Optional[Any]			   =   {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Optional[Any]            =    '''mbart'''
						_SCREAMING_SNAKE_CASE		:				Any            =    ['''past_key_values''']
						_SCREAMING_SNAKE_CASE		:				Optional[Any]            =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






						def __init__(		self   ,  _UpperCamelCase=5_02_65   ,  _UpperCamelCase=10_24   ,  _UpperCamelCase=12   ,  _UpperCamelCase=40_96   ,  _UpperCamelCase=16   ,  _UpperCamelCase=12   ,  _UpperCamelCase=40_96   ,  _UpperCamelCase=16   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=True   ,  _UpperCamelCase=True   ,  _UpperCamelCase="gelu"   ,  _UpperCamelCase=10_24   ,  _UpperCamelCase=0.1   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=0.02   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=False   ,  _UpperCamelCase=1   ,  _UpperCamelCase=0   ,  _UpperCamelCase=2   ,  _UpperCamelCase=2   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  vocab_size
												lowerCAmelCase__			  =  max_position_embeddings
												lowerCAmelCase__			  =  d_model
												lowerCAmelCase__			  =  encoder_ffn_dim
												lowerCAmelCase__			  =  encoder_layers
												lowerCAmelCase__			  =  encoder_attention_heads
												lowerCAmelCase__			  =  decoder_ffn_dim
												lowerCAmelCase__			  =  decoder_layers
												lowerCAmelCase__			  =  decoder_attention_heads
												lowerCAmelCase__			  =  dropout
												lowerCAmelCase__			  =  attention_dropout
												lowerCAmelCase__			  =  activation_dropout
												lowerCAmelCase__			  =  activation_function
												lowerCAmelCase__			  =  init_std
												lowerCAmelCase__			  =  encoder_layerdrop
												lowerCAmelCase__			  =  decoder_layerdrop
												lowerCAmelCase__			  =  classifier_dropout
												lowerCAmelCase__			  =  use_cache
												lowerCAmelCase__			  =  encoder_layers
												lowerCAmelCase__			  =  scale_embedding  # scale factor will be sqrt(d_model) if True
												super().__init__(
												    pad_token_id=_UpperCamelCase   ,  bos_token_id=_UpperCamelCase   ,  eos_token_id=_UpperCamelCase   ,  is_encoder_decoder=_UpperCamelCase   ,  forced_eos_token_id=_UpperCamelCase   ,  **_UpperCamelCase   ,  )




class 			__SCREAMING_SNAKE_CASE    (      __lowercase):






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__			  =  OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ]      )

																		if self.use_past:
																								lowerCAmelCase__			  =  {0: 'batch'}
																								lowerCAmelCase__			  =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																		else:
																								lowerCAmelCase__			  =  {0: 'batch', 1: 'decoder_sequence'}
																								lowerCAmelCase__			  =  {0: 'batch', 1: 'decoder_sequence'}

																		if self.use_past:
																								self.fill_with_past_key_values_(_UpperCamelCase   ,  direction='inputs'      )
												elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		lowerCAmelCase__			  =  OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ]      )
																		if self.use_past:
																								lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_layers
																								for i in range(_UpperCamelCase      ):
																														lowerCAmelCase__			  =  {0: 'batch', 2: 'past_sequence + sequence'}
																														lowerCAmelCase__			  =  {0: 'batch', 2: 'past_sequence + sequence'}
												else:
																		lowerCAmelCase__			  =  OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																		        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																		    ]      )

												return common_inputs






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__			  =  super().outputs
												else:
																		lowerCAmelCase__			  =  super(_UpperCamelCase   ,  self      ).outputs
																		if self.use_past:
																								lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_layers
																								for i in range(_UpperCamelCase      ):
																														lowerCAmelCase__			  =  {0: 'batch', 2: 'past_sequence + sequence'}
																														lowerCAmelCase__			  =  {0: 'batch', 2: 'past_sequence + sequence'}
												return common_outputs






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )

												# Generate decoder inputs
												lowerCAmelCase__			  =  seq_length if not self.use_past else 1
												lowerCAmelCase__			  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
												lowerCAmelCase__			  =  {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												lowerCAmelCase__			  =  dict(**_UpperCamelCase   ,  **_UpperCamelCase      )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
																		else:
																								import torch
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  common_inputs['input_ids'].shape
																		lowerCAmelCase__			  =  common_inputs['decoder_input_ids'].shape[1]
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_attention_heads
																		lowerCAmelCase__			  =  (
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		lowerCAmelCase__			  =  decoder_seq_length + 3
																		lowerCAmelCase__			  =  (
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		lowerCAmelCase__			  =  torch.cat(
																		    [common_inputs['decoder_attention_mask'], torch.ones(_UpperCamelCase   ,  _UpperCamelCase      )]   ,  dim=1      )

																		lowerCAmelCase__			  =  []
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_layers
																		lowerCAmelCase__			  =  min(_UpperCamelCase   ,  _UpperCamelCase      )
																		lowerCAmelCase__			  =  max(_UpperCamelCase   ,  _UpperCamelCase      ) - min_num_layers
																		lowerCAmelCase__			  =  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																		for _ in range(_UpperCamelCase      ):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(_UpperCamelCase      ),
																								        torch.zeros(_UpperCamelCase      ),
																								        torch.zeros(_UpperCamelCase      ),
																								        torch.zeros(_UpperCamelCase      ),
																								    )      )
																		# TODO: test this.
																		lowerCAmelCase__			  =  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																		for _ in range(_UpperCamelCase   ,  _UpperCamelCase      ):
																								common_inputs["past_key_values"].append((torch.zeros(_UpperCamelCase      ), torch.zeros(_UpperCamelCase      ))      )
												return common_inputs






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
																		else:
																								import torch
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  common_inputs['input_ids'].shape
																		# Not using the same length for past_key_values
																		lowerCAmelCase__			  =  seqlen + 2
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_layers
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  self.num_attention_heads
																		lowerCAmelCase__			  =  (
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		lowerCAmelCase__			  =  common_inputs['attention_mask'].dtype
																		lowerCAmelCase__			  =  torch.cat(
																		    [common_inputs['attention_mask'], torch.ones(_UpperCamelCase   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )]   ,  dim=1      )
																		lowerCAmelCase__			  =  [
																		    (torch.zeros(_UpperCamelCase      ), torch.zeros(_UpperCamelCase      )) for _ in range(_UpperCamelCase      )
																		]
												return common_inputs






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  ):






												"""simple docstring"""
												# Copied from OnnxConfig.generate_dummy_inputs
												# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												lowerCAmelCase__			  =  compute_effective_axis_dimension(
												    _UpperCamelCase   ,  fixed_dimension=OnnxConfig.default_fixed_batch   ,  num_token_to_add=0      )

												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												lowerCAmelCase__			  =  tokenizer.num_special_tokens_to_add(_UpperCamelCase      )
												lowerCAmelCase__			  =  compute_effective_axis_dimension(
												    _UpperCamelCase   ,  fixed_dimension=OnnxConfig.default_fixed_sequence   ,  num_token_to_add=_UpperCamelCase      )

												# Generate dummy inputs according to compute batch and sequence
												lowerCAmelCase__			  =  [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
												lowerCAmelCase__			  =  dict(tokenizer(_UpperCamelCase   ,  return_tensors=_UpperCamelCase      )      )
												return common_inputs






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = -1   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__			  =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    _UpperCamelCase   ,  batch_size=_UpperCamelCase   ,  seq_length=_UpperCamelCase   ,  is_pair=_UpperCamelCase   ,  framework=_UpperCamelCase      )

												elif self.task == "causal-lm":
																		lowerCAmelCase__			  =  self._generate_dummy_inputs_for_causal_lm(
																		    _UpperCamelCase   ,  batch_size=_UpperCamelCase   ,  seq_length=_UpperCamelCase   ,  is_pair=_UpperCamelCase   ,  framework=_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																		    _UpperCamelCase   ,  batch_size=_UpperCamelCase   ,  seq_length=_UpperCamelCase   ,  is_pair=_UpperCamelCase   ,  framework=_UpperCamelCase      )

												return common_inputs







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__			  =  super()._flatten_past_key_values_(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  super(_UpperCamelCase   ,  self      )._flatten_past_key_values_(
																		    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )













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


__snake_case			:			Union[str, Any]			   =   logging.get_logger(__name__)


@dataclass
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None






						def 					UpperCamelCase__					(		self      ):






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




@dataclass(frozen=__lowercase)
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				List[int]
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[Union[int, float]]            =    None






						def 					UpperCamelCase__					(		self      ):






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




class 			__SCREAMING_SNAKE_CASE    :






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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






						def 					UpperCamelCase__					(		self      ):






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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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







						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None      ):






												"""simple docstring"""
												with open(_UpperCamelCase   ,  'r'   ,  encoding='utf-8-sig'      ) as f:
																		return list(csv.reader(_UpperCamelCase   ,  delimiter='\t'   ,  quotechar=_UpperCamelCase      )      )




class 			__SCREAMING_SNAKE_CASE    (      __lowercase):






						def __init__(		self   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase="classification"   ,  _UpperCamelCase=False      ):






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






						def __len__(		self      ):






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






						def __getitem__(		self   ,  _UpperCamelCase      ):






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






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  **_UpperCamelCase      ):






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






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  cls(**_UpperCamelCase      )
												processor.add_examples(_UpperCamelCase   ,  labels=_UpperCamelCase      )
												return processor






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  ):






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

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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False      ):






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

												# Update examples
												if overwrite_examples:
																		lowerCAmelCase__			  =  examples
												else:
																		self.examples.extend(_UpperCamelCase      )

												# Update labels
												if overwrite_labels:
																		lowerCAmelCase__			  =  list(_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  list(set(self.labels      ).union(_UpperCamelCase      )      )

												return self.examples







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=0   ,  _UpperCamelCase=True   ,  _UpperCamelCase=None   ,  ):






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

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

												lowerCAmelCase__			  =  []
												for ex_index, example in enumerate(self.examples      ):
																		if ex_index % 1_00_00 == 0:
																								logger.info(F"Tokenizing example {ex_index}"      )

																		lowerCAmelCase__			  =  tokenizer.encode(
																		    example.text_a   ,  add_special_tokens=_UpperCamelCase   ,  max_length=min(_UpperCamelCase   ,  tokenizer.max_len      )   ,  )
																		all_input_ids.append(_UpperCamelCase      )

												lowerCAmelCase__			  =  max(len(_UpperCamelCase      ) for input_ids in all_input_ids      )

												lowerCAmelCase__			  =  []
												for ex_index, (input_ids, example) in enumerate(zip(_UpperCamelCase   ,  self.examples      )      ):
																		if ex_index % 1_00_00 == 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.
																		lowerCAmelCase__			  =  [1 if mask_padding_with_zero else 0] * len(_UpperCamelCase      )

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

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

																		if self.mode == "classification":
																								lowerCAmelCase__			  =  label_map[example.label]
																		elif self.mode == "regression":
																								lowerCAmelCase__			  =  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(_UpperCamelCase      ) for x in input_ids]      )}"      )
																								logger.info(F"attention_mask: {' '.join([str(_UpperCamelCase      ) for x in attention_mask]      )}"      )
																								logger.info(F"label: {example.label} (id = {label})"      )

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

												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)

																		lowerCAmelCase__			  =  tf.data.Dataset.from_generator(
																		    _UpperCamelCase   ,  ({'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

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

																		lowerCAmelCase__			  =  TensorDataset(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
																		return dataset
												else:
																		raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )





