


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


UpperCAmelCase_				:			Union[str, Any]   =	1e-4

if is_torch_available():
   import torch

   from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
   from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class 			_a  :
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	str=1_6					,							SCREAMING_SNAKE_CASE__    :	int=1_3					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=7					,							SCREAMING_SNAKE_CASE__    :	str=1_4					,							SCREAMING_SNAKE_CASE__    :	Dict=1_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1_9					,							SCREAMING_SNAKE_CASE__    :	int=5					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=4					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					,							SCREAMING_SNAKE_CASE__    :	str=1_6					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=2					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=4					,							SCREAMING_SNAKE_CASE__    :	int=4					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="gelu"					,							SCREAMING_SNAKE_CASE__    :	List[str]=0.1					,							SCREAMING_SNAKE_CASE__    :	int=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=[1, 2, 3, 4, 5]					,							SCREAMING_SNAKE_CASE__    :	Any=2_5					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=5					,							)		->      List[str]:
             a_ :		List[str]							=  d_model
             a_ :		str							=  parent
             a_ :		Union[str, Any]							=  batch_size
             a_ :		List[Any]							=  prediction_length
             a_ :		Tuple							=  context_length
             a_ :		Optional[int]							=  cardinality
             a_ :		Tuple							=  num_time_features
             a_ :		Union[str, Any]							=  lags_sequence
             a_ :		Tuple							=  embedding_dimension
             a_ :		int							=  is_training
             a_ :		List[str]							=  hidden_size
             a_ :		Tuple							=  num_hidden_layers
             a_ :		Optional[Any]							=  num_attention_heads
             a_ :		int							=  intermediate_size
             a_ :		Union[str, Any]							=  hidden_act
             a_ :		Optional[Any]							=  hidden_dropout_prob
             a_ :		Union[str, Any]							=  attention_probs_dropout_prob

             a_ :		Union[str, Any]							=  context_length
             a_ :		str							=  prediction_length + label_length
             a_ :		Union[str, Any]							=  label_length

             a_ :		Dict							=  moving_average
             a_ :		Tuple							=  autocorrelation_factor
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      int:
             return AutoformerConfig(
                 d_model=self.d_model					,							encoder_layers=self.num_hidden_layers					,							decoder_layers=self.num_hidden_layers					,							encoder_attention_heads=self.num_attention_heads					,							decoder_attention_heads=self.num_attention_heads					,							encoder_ffn_dim=self.intermediate_size					,							decoder_ffn_dim=self.intermediate_size					,							dropout=self.hidden_dropout_prob					,							attention_dropout=self.attention_probs_dropout_prob					,							prediction_length=self.prediction_length					,							context_length=self.context_length					,							label_length=self.label_length					,							lags_sequence=self.lags_sequence					,							num_time_features=self.num_time_features					,							num_static_categorical_features=1					,							cardinality=[self.cardinality]					,							embedding_dimension=[self.embedding_dimension]					,							moving_average=self.moving_average					,							)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Union[str, Any]:
             a_ :		Any							=  config.context_length + max(config.lags_sequence					)

             a_ :		Any							=  ids_tensor([self.batch_size, 1]					,							config.cardinality[0]					)
             a_ :		Any							=  floats_tensor([self.batch_size, _past_length, config.num_time_features]					)
             a_ :		Union[str, Any]							=  floats_tensor([self.batch_size, _past_length]					)
             a_ :		Optional[int]							=  floats_tensor([self.batch_size, _past_length]					) > 0.5

             # decoder inputs
             a_ :		Optional[Any]							=  floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]					)
             a_ :		Any							=  floats_tensor([self.batch_size, config.prediction_length]					)

             a_ :		Dict							=  {
                 '''past_values''': past_values,
                 '''static_categorical_features''': static_categorical_features,
                 '''past_time_features''': past_time_features,
                 '''past_observed_mask''': past_observed_mask,
                 '''future_time_features''': future_time_features,
                 '''future_values''': future_values,
             }
             return inputs_dict
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[int]:
             a_ :		Tuple							=  self.get_config()
             a_ :		List[Any]							=  self.prepare_autoformer_inputs_dict(a_					)
             return config, inputs_dict
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[Any]:
             a_ :		str							=  self.prepare_config_and_inputs()
             return config, inputs_dict

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	int					)		->      Dict:
             a_ :		int							=  AutoformerModel(config=a_					).to(a_					).eval()
             a_ :		Union[str, Any]							=  model(**a_					)

             a_ :		str							=  outputs.encoder_last_hidden_state
             a_ :		int							=  outputs.last_hidden_state

             with tempfile.TemporaryDirectory() as tmpdirname:
                    a_ :		int							=  model.get_encoder()
                    encoder.save_pretrained(a_					)
                    a_ :		Any							=  AutoformerEncoder.from_pretrained(a_					).to(a_					)

             a_ :		List[str]							=  model.create_network_inputs(**a_					)
             a_ :		Union[str, Any]							=  model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]					)

             a_ :		int							=  torch.cat(
                 (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])					,							dim=-1					,							)
             a_ :		Optional[int]							=  encoder(inputs_embeds=a_					)[0]
             self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3					)

             a_ :		List[Any]							=  (
                 torch.mean(transformer_inputs[:, : config.context_length, ...]					,							dim=1					)
                 .unsqueeze(1					)
                 .repeat(1					,							config.prediction_length					,							1					)
             )
             a_ :		int							=  torch.zeros(
                 [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]					,							device=enc_input.device					,							)

             a_ :		Tuple							=  torch.cat(
                 (
                     torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)					,							dim=1					),
                     feature[:, config.context_length - config.label_length :, ...],
                 )					,							dim=-1					,							)
             a_ :		List[str]							=  torch.cat(
                 (
                     torch.cat((trend_input[:, -config.label_length :, ...], mean)					,							dim=1					),
                     feature[:, config.context_length - config.label_length :, ...],
                 )					,							dim=-1					,							)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    a_ :		Dict							=  model.get_decoder()
                    decoder.save_pretrained(a_					)
                    a_ :		List[str]							=  AutoformerDecoder.from_pretrained(a_					).to(a_					)

             a_ :		Optional[int]							=  decoder(
                 trend=a_					,							inputs_embeds=a_					,							encoder_hidden_states=a_					,							)[0]

             self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3					)
@require_torch
class 			_a  (    __snake_case						,   __snake_case						,   unittest.TestCase      ):
      snake_case__    :					List[Any]					       =						(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      snake_case__    :					int					       =						(AutoformerForPrediction,) if is_torch_available() else ()
      snake_case__    :					Dict					       =						{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
      snake_case__    :					Dict					       =						False
      snake_case__    :					List[str]					       =						False
      snake_case__    :					Optional[int]					       =						False
      snake_case__    :					List[str]					       =						False
      snake_case__    :					int					       =						False
      snake_case__    :					List[Any]					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Optional[Any]:
             a_ :		Dict							=  AutoformerModelTester(self					)
             a_ :		Dict							=  ConfigTester(self					,							config_class=a_					,							has_text_modality=a_					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[int]:
             self.config_tester.run_common_tests()
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Any:
             a_ :		Dict							=  self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    a_ :		Dict							=  model_class(a_					)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(a_					)
                           a_ :		List[str]							=  model_class.from_pretrained(a_					,							output_loading_info=a_					)
                    self.assertEqual(info['missing_keys']					,							[]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[Any]:
             a_ :		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_encoder_decoder_model_standalone(*a_					)
      @unittest.skip(reason='Model has no tokens embeddings'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[Any]:
             pass
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Optional[int]							=  inspect.signature(getattr(a_					,							'forward'					)					)
             # The main input is the name of the argument after `self`
             a_ :		Optional[int]							=  list(model_signature.parameters.keys()					)[1]
             self.assertEqual(AutoformerModel.main_input_name					,							a_					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Tuple:
             a_ :		List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_ :		Dict							=  model_class(a_					)
                    a_ :		List[str]							=  inspect.signature(model.forward					)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    a_ :		str							=  [*signature.parameters.keys()]

                    a_ :		str							=  [
                        '''past_values''',
                        '''past_time_features''',
                        '''past_observed_mask''',
                        '''static_categorical_features''',
                        '''static_real_features''',
                        '''future_values''',
                        '''future_time_features''',
                    ]

                    if model.__class__.__name__ in ["AutoformerForPrediction"]:
                           expected_arg_names.append('future_observed_mask'					)

                    expected_arg_names.extend(
                        [
                            'decoder_attention_mask',
                            'head_mask',
                            'decoder_head_mask',
                            'cross_attn_head_mask',
                            'encoder_outputs',
                            'past_key_values',
                            'output_hidden_states',
                            'output_attentions',
                            'use_cache',
                            'return_dict',
                        ]					)

                    self.assertListEqual(arg_names[: len(a_					)]					,							a_					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             a_ :		Any							=  self.model_tester.prepare_config_and_inputs_for_common()
             a_ :		List[str]							=  True

             a_ :		Optional[int]							=  getattr(self.model_tester					,							'seq_length'					,							a_					)
             a_ :		Optional[int]							=  getattr(self.model_tester					,							'decoder_seq_length'					,							a_					)
             a_ :		List[Any]							=  getattr(self.model_tester					,							'encoder_seq_length'					,							a_					)
             a_ :		Any							=  getattr(self.model_tester					,							'd_model'					,							a_					)
             a_ :		int							=  getattr(self.model_tester					,							'num_attention_heads'					,							a_					)
             a_ :		Optional[int]							=  d_model // num_attention_heads

             for model_class in self.all_model_classes:
                    a_ :		List[str]							=  True
                    a_ :		List[str]							=  False
                    a_ :		Tuple							=  True
                    a_ :		List[Any]							=  model_class(a_					)
                    model.to(a_					)
                    model.eval()
                    with torch.no_grad():
                           a_ :		List[str]							=  model(**self._prepare_for_class(a_					,							a_					)					)
                    a_ :		Optional[Any]							=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                    self.assertEqual(len(a_					)					,							self.model_tester.num_hidden_layers					)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    a_ :		Any							=  True
                    a_ :		List[Any]							=  model_class(a_					)
                    model.to(a_					)
                    model.eval()
                    with torch.no_grad():
                           a_ :		Dict							=  model(**self._prepare_for_class(a_					,							a_					)					)
                    a_ :		Union[str, Any]							=  outputs.encoder_attentions
                    self.assertEqual(len(a_					)					,							self.model_tester.num_hidden_layers					)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]					)					,							[self.model_tester.num_attention_heads, encoder_seq_length, dim]					,							)
                    a_ :		List[str]							=  len(a_					)

                    a_ :		Optional[int]							=  7

                    if "last_hidden_state" in outputs:
                           correct_outlen += 1

                    if "trend" in outputs:
                           correct_outlen += 1

                    if "past_key_values" in outputs:
                           correct_outlen += 1  # past_key_values have been returned

                    if "loss" in outputs:
                           correct_outlen += 1

                    if "params" in outputs:
                           correct_outlen += 1

                    self.assertEqual(a_					,							a_					)

                    # decoder attentions
                    a_ :		Optional[Any]							=  outputs.decoder_attentions
                    self.assertIsInstance(a_					,							(list, tuple)					)
                    self.assertEqual(len(a_					)					,							self.model_tester.num_hidden_layers					)
                    self.assertListEqual(
                        list(decoder_attentions[0].shape[-3:]					)					,							[self.model_tester.num_attention_heads, decoder_seq_length, dim]					,							)

                    # cross attentions
                    a_ :		Optional[int]							=  outputs.cross_attentions
                    self.assertIsInstance(a_					,							(list, tuple)					)
                    self.assertEqual(len(a_					)					,							self.model_tester.num_hidden_layers					)
                    self.assertListEqual(
                        list(cross_attentions[0].shape[-3:]					)					,							[self.model_tester.num_attention_heads, decoder_seq_length, dim]					,							)

             # Check attention is always last and order is fine
             a_ :		Any							=  True
             a_ :		Optional[Any]							=  True
             a_ :		Dict							=  model_class(a_					)
             model.to(a_					)
             model.eval()
             with torch.no_grad():
                    a_ :		List[Any]							=  model(**self._prepare_for_class(a_					,							a_					)					)

             self.assertEqual(out_len + 2					,							len(a_					)					)

             a_ :		List[Any]							=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

             self.assertEqual(len(a_					)					,							self.model_tester.num_hidden_layers					)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]					)					,							[self.model_tester.num_attention_heads, encoder_seq_length, dim]					,							)

      @is_flaky()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Dict:
             super().test_retain_grad_hidden_states_attentions()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]="train-batch.pt"    )     ->		Dict:

       """simple docstring"""

       a_ :		Tuple							=  hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'  ,   filename=_snake_case  ,   repo_type='dataset'    )
       a_ :		int							=  torch.load(_snake_case  ,   map_location=_snake_case    )
       return batch
@require_torch
@slow
class 			_a  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             a_ :		int							=  AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(a_					)
             a_ :		Union[str, Any]							=  prepare_batch()

             with torch.no_grad():
                    a_ :		int							=  model(
                        past_values=batch['past_values']					,							past_time_features=batch['past_time_features']					,							past_observed_mask=batch['past_observed_mask']					,							static_categorical_features=batch['static_categorical_features']					,							future_values=batch['future_values']					,							future_time_features=batch['future_time_features']					,							)[0]

             a_ :		Any							=  torch.Size(
                 (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)					)
             self.assertEqual(output.shape					,							a_					)

             a_ :		Optional[Any]							=  torch.tensor(
                 [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]					,							device=a_					)
             self.assertTrue(torch.allclose(output[0, :3, :3]					,							a_					,							atol=a_					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Any:
             a_ :		Dict							=  AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(a_					)
             a_ :		Tuple							=  prepare_batch('val-batch.pt'					)
             with torch.no_grad():
                    a_ :		Any							=  model(
                        past_values=batch['past_values']					,							past_time_features=batch['past_time_features']					,							past_observed_mask=batch['past_observed_mask']					,							static_categorical_features=batch['static_categorical_features']					,							).encoder_last_hidden_state
             a_ :		List[Any]							=  torch.Size((6_4, model.config.context_length, model.config.d_model)					)
             self.assertEqual(output.shape					,							a_					)

             a_ :		Union[str, Any]							=  torch.tensor(
                 [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]					,							device=a_					)
             self.assertTrue(torch.allclose(output[0, :3, :3]					,							a_					,							atol=a_					)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             a_ :		int							=  AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(a_					)
             a_ :		Optional[int]							=  prepare_batch('val-batch.pt'					)
             with torch.no_grad():
                    a_ :		List[str]							=  model.generate(
                        static_categorical_features=batch['static_categorical_features']					,							past_time_features=batch['past_time_features']					,							past_values=batch['past_values']					,							future_time_features=batch['future_time_features']					,							past_observed_mask=batch['past_observed_mask']					,							)
             a_ :		Optional[Any]							=  torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)					)
             self.assertEqual(outputs.sequences.shape					,							a_					)

             a_ :		Optional[int]							=  torch.tensor([3130.6763, 4056.5293, 7053.0786]					,							device=a_					)
             a_ :		List[str]							=  outputs.sequences.mean(dim=1					)
             self.assertTrue(torch.allclose(mean_prediction[0, -3:]					,							a_					,							rtol=1E-1					)					)






import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


