
"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class   __a  (      _lowerCAmelCase	):






       @slow
       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"       ,      "prajjwal1/bert-tiny"			)
              UpperCamelCase					 =		BertTokenizer.from_pretrained("bert-base-uncased"			)

              UpperCamelCase					 =		bertabert.config.encoder.vocab_size
              UpperCamelCase					 =		tokenizer.sep_token_id
              UpperCamelCase					 =		tokenizer.cls_token_id
              UpperCamelCase					 =		128

              UpperCamelCase					 =		datasets.load_dataset("cnn_dailymail"       ,      "3.0.0"       ,      split="train[:1%]"			)
              UpperCamelCase					 =		datasets.load_dataset("cnn_dailymail"       ,      "3.0.0"       ,      split="validation[:1%]"			)

              UpperCamelCase					 =		train_dataset.select(range(32			)			)
              UpperCamelCase					 =		val_dataset.select(range(16			)			)

              UpperCamelCase					 =		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_ :							Optional[Any]			):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase					 =		tokenizer(batch["article"]       ,      padding="max_length"       ,      truncation=UpperCAmelCase_       ,      max_length=512			)
                     UpperCamelCase					 =		tokenizer(batch["highlights"]       ,      padding="max_length"       ,      truncation=UpperCAmelCase_       ,      max_length=128			)
                     UpperCamelCase					 =		inputs.input_ids
                     UpperCamelCase					 =		inputs.attention_mask

                     UpperCamelCase					 =		outputs.input_ids
                     UpperCamelCase					 =		outputs.input_ids.copy()
                     UpperCamelCase					 =		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase					 =		outputs.attention_mask

                     assert all(len(UpperCAmelCase_			) == 512 for x in inputs.input_ids			)
                     assert all(len(UpperCAmelCase_			) == 128 for x in outputs.input_ids			)

                     return batch

              def _compute_metrics(UpperCAmelCase_ :							Dict			):
                     UpperCamelCase					 =		pred.label_ids
                     UpperCamelCase					 =		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase					 =		tokenizer.batch_decode(UpperCAmelCase_       ,      skip_special_tokens=UpperCAmelCase_			)
                     UpperCamelCase					 =		tokenizer.batch_decode(UpperCAmelCase_       ,      skip_special_tokens=UpperCAmelCase_			)

                     UpperCamelCase					 =		sum([int(pred_str[i] == label_str[i]			) for i in range(len(UpperCAmelCase_			)			)]			) / len(UpperCAmelCase_			)

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase					 =		train_dataset.map(
                  _map_to_encoder_decoder_inputs       ,      batched=UpperCAmelCase_       ,      batch_size=UpperCAmelCase_       ,      remove_columns=["article", "highlights"]       ,      )
              train_dataset.set_format(
                  type="torch"       ,      columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]       ,      )

              # same for validation dataset
              UpperCamelCase					 =		val_dataset.map(
                  _map_to_encoder_decoder_inputs       ,      batched=UpperCAmelCase_       ,      batch_size=UpperCAmelCase_       ,      remove_columns=["article", "highlights"]       ,      )
              val_dataset.set_format(
                  type="torch"       ,      columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]       ,      )

              UpperCamelCase					 =		self.get_auto_remove_tmp_dir()

              UpperCamelCase					 =		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_       ,      per_device_train_batch_size=UpperCAmelCase_       ,      per_device_eval_batch_size=UpperCAmelCase_       ,      predict_with_generate=UpperCAmelCase_       ,      evaluation_strategy="steps"       ,      do_train=UpperCAmelCase_       ,      do_eval=UpperCAmelCase_       ,      warmup_steps=0       ,      eval_steps=2       ,      logging_steps=2       ,      )

              # instantiate trainer
              UpperCamelCase					 =		SeqaSeqTrainer(
                  model=UpperCAmelCase_       ,      args=UpperCAmelCase_       ,      compute_metrics=_compute_metrics       ,      train_dataset=UpperCAmelCase_       ,      eval_dataset=UpperCAmelCase_       ,      tokenizer=UpperCAmelCase_       ,      )

              # start training
              trainer.train()







"""simple docstring"""


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["a", "b", "c"]

              # Defaults to last layer if both are None
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [2]			)

              # Out indices set to match out features
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(["a", "c"]       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features set to match out indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [0, 2]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features selected from negative indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [-3, -1]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [-3, -1]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # Stage names must be set
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      UpperCAmelCase_			)

              # Out features must be a list
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(("a", "b")       ,      (0, 1)       ,      ["a", "b"]			)

              # Out features must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      ["a"]			)

              # Out indices must be a list or tuple
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      0       ,      ["a", "b"]			)

              # Out indices must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      (0, 1)       ,      ["a"]			)

              # Out features and out indices must be the same length
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0,)       ,      ["a", "b", "c"]			)

              # Out features should match out indices
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 2)       ,      ["a", "b", "c"]			)

              # Out features and out indices should be in order
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["b", "a"]       ,      (0, 1)       ,      ["a", "b"]			)

              # Check passes with valid inputs
              verify_out_features_out_indices(["a", "b", "d"]       ,      (0, 1, -1)       ,      ["a", "b", "c", "d"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BackboneMixin()

              UpperCamelCase					 =		["a", "b", "c"]
              UpperCamelCase					 =		["a", "c"]
              UpperCamelCase					 =		[0, 2]

              # Check that the output features and indices are set correctly
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 2]			)

              # Check out features and indices are updated correctly
              UpperCamelCase					 =		["a", "b"]
              self.assertEqual(backbone.out_features       ,      ["a", "b"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 1]			)

              UpperCamelCase					 =		[-3, -1]
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [-3, -1]			)





