"""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   lowerCAmelCase_	(       self						:	str       ):
      _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(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					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(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					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=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					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=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()

"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Union[str, Any]=None ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   config_class
      _UpperCAmelCase    			=   has_text_modality
      _UpperCAmelCase    			=   kwargs
      _UpperCAmelCase    			=   common_properties


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )
      _UpperCAmelCase    			=   (
          ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
        common_properties.extend(["""vocab_size"""]       )

      # Test that config has the common properties as getters
      for prop in common_properties:
        self.parent.assertTrue(hasattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					msg=f'''`{prop}` does not exist'''       )

      # Test that config has the common properties as setter
      for idx, name in enumerate(__lowerCAmelCase       ):
        try:
          setattr(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )
          self.parent.assertEqual(
              getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					__lowerCAmelCase ,					msg=f'''`{name} value {idx} expected, but was {getattr(__lowerCAmelCase ,					__lowerCAmelCase       )}'''       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(__lowerCAmelCase       ):
        try:
          _UpperCAmelCase    			=   self.config_class(**{name: idx}       )
          self.parent.assertEqual(
              getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					__lowerCAmelCase ,					msg=f'''`{name} value {idx} expected, but was {getattr(__lowerCAmelCase ,					__lowerCAmelCase       )}'''       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )
      _UpperCAmelCase    			=   json.loads(config.to_json_string()       )
      for key, value in self.inputs_dict.items():
        self.parent.assertEqual(obj[key] ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase    			=   os.path.join(__lowerCAmelCase ,					"""config.json"""       )
        config_first.to_json_file(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_json_file(__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        config_first.save_pretrained(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_pretrained(__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      _UpperCAmelCase    			=   """test"""
      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase    			=   os.path.join(__lowerCAmelCase ,					__lowerCAmelCase       )
        config_first.save_pretrained(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_pretrained(__lowerCAmelCase ,					subfolder=__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict ,					num_labels=5       )
      self.parent.assertEqual(len(config.idalabel       ) ,					5       )
      self.parent.assertEqual(len(config.labelaid       ) ,					5       )

      _UpperCAmelCase    			=   3
      self.parent.assertEqual(len(config.idalabel       ) ,					3       )
      self.parent.assertEqual(len(config.labelaid       ) ,					3       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      if self.config_class.is_composition:
        return
      _UpperCAmelCase    			=   self.config_class()
      self.parent.assertIsNotNone(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   copy.deepcopy(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.config_class(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   []
      for key, value in config_common_kwargs.items():
        if key == "torch_dtype":
          if not is_torch_available():
            continue
          else:
            import torch

            if config.torch_dtype != torch.floataa:
              wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)       )
        elif getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) != value:
          wrong_values.append((key, getattr(__lowerCAmelCase ,					__lowerCAmelCase       ), value)       )

      if len(__lowerCAmelCase       ) > 0:
        _UpperCAmelCase    			=   """\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]       )
        raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()
