






"""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	(						_UpperCAmelCase					):




    """simple docstring"""




    @slow
    @require_torch
    def  lowercase					(		self							:     Any    )    ->       Any:
         __snake_case				=							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''	,      '''prajjwal1/bert-tiny'''    )
         __snake_case				=							BertTokenizer.from_pretrained('''bert-base-uncased'''    )

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

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

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

         __snake_case				=							4

         def _map_to_encoder_decoder_inputs(A_							:     Dict    ):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              __snake_case				=							tokenizer(batch['''article''']	,      padding='''max_length'''	,      truncation=UpperCamelCase_	,      max_length=512    )
              __snake_case				=							tokenizer(batch['''highlights''']	,      padding='''max_length'''	,      truncation=UpperCamelCase_	,      max_length=128    )
              __snake_case				=							inputs.input_ids
              __snake_case				=							inputs.attention_mask

              __snake_case				=							outputs.input_ids
              __snake_case				=							outputs.input_ids.copy()
              __snake_case				=							[
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              __snake_case				=							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(A_							:     Union[str, Any]    ):
              __snake_case				=							pred.label_ids
              __snake_case				=							pred.predictions

              # all unnecessary tokens are removed
              __snake_case				=							tokenizer.batch_decode(UpperCamelCase_	,      skip_special_tokens=UpperCamelCase_    )
              __snake_case				=							tokenizer.batch_decode(UpperCamelCase_	,      skip_special_tokens=UpperCamelCase_    )

              __snake_case				=							sum([int(pred_str[i] == label_str[i]    ) for i in range(len(UpperCamelCase_    )    )]    ) / len(UpperCamelCase_    )

              return {"accuracy": accuracy}

         # map train dataset
         __snake_case				=							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
         __snake_case				=							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''']	,      )

         __snake_case				=							self.get_auto_remove_tmp_dir()

         __snake_case				=							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
         __snake_case				=							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

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     List[Any]=13	,      A_							:     Dict=7	,      A_							:     Optional[int]=True	,      A_							:     Optional[int]=True	,      A_							:     List[Any]=True	,      A_							:     Union[str, Any]=True	,      A_							:     str=99	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=5	,      A_							:     Union[str, Any]=4	,      A_							:     Dict=37	,      A_							:     Any="gelu"	,      A_							:     Tuple=0.1	,      A_							:     str=0.1	,      A_							:     List[str]=512	,      A_							:     List[str]=16	,      A_							:     Optional[int]=2	,      A_							:     Optional[Any]=0.02	,      A_							:     str=4	,      )    ->       Any:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_attention_mask
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_choices
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							None
         if self.use_attention_mask:
              __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

         __snake_case				=							None
         if self.use_token_type_ids:
              __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size    )

         __snake_case				=							RoFormerConfig(
             vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      is_decoder=A_	,      initializer_range=self.initializer_range	,      )

         return config, input_ids, token_type_ids, attention_mask







    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							config_and_inputs
         __snake_case				=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					True

    UpperCamelCase_	:					List[str]							=					(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerModelTester(self    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''	,      from_pt=A_    )
              __snake_case				=							model(np.ones((1, 1)    )    )
              self.assertIsNotNone(A_    )






@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         __snake_case				=							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         __snake_case				=							model(A_    )[0]

         __snake_case				=							50_000

         __snake_case				=							(1, 6, vocab_size)
         self.assertEqual(output.shape	,      A_    )

         __snake_case				=							jnp.array(
             [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]    )

         self.assertTrue(jnp.allclose(output[:, :3, :3]	,      A_	,      atol=1E-4    )    )