






'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  SCREAMING_SNAKE_CASE_					   =				"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )






def 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__=None							,		SCREAMING_SNAKE_CASE__=None							,		SCREAMING_SNAKE_CASE__=None							,		SCREAMING_SNAKE_CASE__=None							,		SCREAMING_SNAKE_CASE__=None							,		SCREAMING_SNAKE_CASE__=None							,		):
 if attention_mask is None:
  __a     :						List[Any]          =    np.where(input_ids != config.pad_token_id							,		1							,		0		)
 if decoder_attention_mask is None:
  __a     :						Any          =    np.where(decoder_input_ids != config.pad_token_id							,		1							,		0		)
 if head_mask is None:
  __a     :						Any          =    np.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  __a     :						Tuple          =    np.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  __a     :						List[str]          =    np.ones((config.decoder_layers, config.decoder_attention_heads)		)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }




class 				lowerCAmelCase       :







   """simple docstring"""







   def __init__(			self , _A , _A=13 , _A=7 , _A=True , _A=False , _A=99 , _A=16 , _A=2 , _A=4 , _A=4 , _A="gelu" , _A=0.1 , _A=0.1 , _A=32 , _A=2 , _A=1 , _A=0 , _A=0.02 , )     -> List[str]:
    __a     :						int          =    parent
    __a     :						str          =    batch_size
    __a     :						int          =    seq_length
    __a     :						int          =    is_training
    __a     :						int          =    use_labels
    __a     :						Optional[int]          =    vocab_size
    __a     :						List[Any]          =    hidden_size
    __a     :						Any          =    num_hidden_layers
    __a     :						Optional[Any]          =    num_attention_heads
    __a     :						Optional[Any]          =    intermediate_size
    __a     :						List[Any]          =    hidden_act
    __a     :						Any          =    hidden_dropout_prob
    __a     :						str          =    attention_probs_dropout_prob
    __a     :						List[str]          =    max_position_embeddings
    __a     :						int          =    eos_token_id
    __a     :						List[str]          =    pad_token_id
    __a     :						Dict          =    bos_token_id
    __a     :						int          =    initializer_range



   def  __magic_name__     (			self      )     -> str:
    __a     :						Optional[int]          =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size      ) , 3 , self.vocab_size      )
    __a     :						int          =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa      )) , -1      )

    __a     :						List[str]          =    shift_tokens_right(_UpperCAmelCase , 1 , 2      )

    __a     :						List[str]          =    BlenderbotConfig(
        vocab_size=self.vocab_size , d_model=self.hidden_size , 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 , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_UpperCAmelCase , )
    __a     :						Dict          =    prepare_blenderbot_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase      )
    return config, inputs_dict



   def  __magic_name__     (			self      )     -> List[str]:
    __a      ,     __a     :						Dict          =    self.prepare_config_and_inputs()
    return config, inputs_dict



   def  __magic_name__     (			self , _A , _A , _A      )     -> Optional[Any]:
    __a     :						int          =    20
    __a     :						Union[str, Any]          =    model_class_name(_UpperCAmelCase      )

    __a     :						List[Any]          =    model.encode(inputs_dict['input_ids']      )

    __a      ,     __a     :						List[Any]          =    (
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

    __a     :						Optional[Any]          =    model.init_cache(decoder_input_ids.shape[0] , _UpperCAmelCase , _UpperCAmelCase      )
    __a     :						int          =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4'      )

    __a     :						Union[str, Any]          =    jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
    __a     :						str          =    model.decode(
        decoder_input_ids[:, :-1] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )

    __a     :						Optional[Any]          =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4'      )
    __a     :						Union[str, Any]          =    model.decode(
        decoder_input_ids[:, -1:] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_UpperCAmelCase , )

    __a     :						int          =    model.decode(_UpperCAmelCase , _UpperCAmelCase      )

    __a     :						int          =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
    self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}'''      )



   def  __magic_name__     (			self , _A , _A , _A      )     -> Tuple:
    __a     :						List[str]          =    20
    __a     :						List[Any]          =    model_class_name(_UpperCAmelCase      )

    __a     :						List[Any]          =    model.encode(inputs_dict['input_ids']      )

    __a      ,     __a     :						Optional[int]          =    (
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

    __a     :						List[str]          =    jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
        ] , axis=-1 , )

    __a     :						Optional[Any]          =    model.init_cache(decoder_input_ids.shape[0] , _UpperCAmelCase , _UpperCAmelCase      )
    __a     :						Dict          =    jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )

    __a     :						Tuple          =    model.decode(
        decoder_input_ids[:, :-1] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )
    __a     :						Optional[Any]          =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4'      )
    __a     :						str          =    model.decode(
        decoder_input_ids[:, -1:] , _UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )

    __a     :						Any          =    model.decode(_UpperCAmelCase , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase      )

    __a     :						int          =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
    self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}'''      )




@require_flax
class 				lowerCAmelCase       (				unittest.TestCase ):







   """simple docstring"""







   _A							= 99



   def  __magic_name__     (			self      )     -> Union[str, Any]:
    __a     :						List[Any]          =    np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ] , dtype=np.intaa , )

    __a     :						str          =    input_ids.shape[0]
    __a     :						List[Any]          =    BlenderbotConfig(
        vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
    return config, input_ids, batch_size



   def  __magic_name__     (			self      )     -> Optional[int]:
    __a      ,     __a      ,     __a     :						List[Any]          =    self._get_config_and_data()
    __a     :						Optional[int]          =    FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase      )
    __a     :						Tuple          =    lm_model(input_ids=_UpperCAmelCase      )
    __a     :						Any          =    (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['logits'].shape , _UpperCAmelCase      )



   def  __magic_name__     (			self      )     -> Optional[Any]:
    __a     :						Any          =    BlenderbotConfig(
        vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
    __a     :						Any          =    FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase      )
    __a     :						Optional[int]          =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa      )
    __a     :						Any          =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa      )
    __a     :						int          =    lm_model(input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase      )
    __a     :						Dict          =    (*summary.shape, config.vocab_size)
    self.assertEqual(outputs['logits'].shape , _UpperCAmelCase      )



   def  __magic_name__     (			self      )     -> Optional[Any]:
    __a     :						List[Any]          =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa      )
    __a     :						Tuple          =    shift_tokens_right(_UpperCAmelCase , 1 , 2      )
    __a     :						Optional[Any]          =    np.equal(_UpperCAmelCase , 1      ).astype(np.floataa      ).sum()
    __a     :						Tuple          =    np.equal(_UpperCAmelCase , 1      ).astype(np.floataa      ).sum()
    self.assertEqual(shifted.shape , input_ids.shape      )
    self.assertEqual(_UpperCAmelCase , n_pad_before - 1      )
    self.assertTrue(np.equal(shifted[:, 0] , 2      ).all()      )




@require_flax
class 				lowerCAmelCase       (				UpperCAmelCase__				,   unittest.TestCase				,   UpperCAmelCase__ ):







   """simple docstring"""







   _A							= True
   _A							= (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   _A							= (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def  __magic_name__     (			self      )     -> Dict:
    __a     :						Any          =    FlaxBlenderbotModelTester(self      )



   def  __magic_name__     (			self      )     -> str:
    __a      ,     __a     :						List[str]          =    self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase      )



   def  __magic_name__     (			self      )     -> Tuple:
    __a      ,     __a     :						int          =    self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase      )



   def  __magic_name__     (			self      )     -> List[Any]:
    __a      ,     __a     :						List[str]          =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__      ):
      __a     :						List[str]          =    self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase      )
      __a     :						Any          =    model_class(_UpperCAmelCase      )

      @jax.jit
      def encode_jitted(_A , _A=None , **_A      ):
       return model.encode(input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase      )

      with self.subTest('JIT Enabled'      ):
       __a     :						Dict          =    encode_jitted(**_UpperCAmelCase      ).to_tuple()

      with self.subTest('JIT Disabled'      ):
       with jax.disable_jit():
        __a     :						Optional[int]          =    encode_jitted(**_UpperCAmelCase      ).to_tuple()

      self.assertEqual(len(_UpperCAmelCase      ) , len(_UpperCAmelCase      )      )
      for jitted_output, output in zip(_UpperCAmelCase , _UpperCAmelCase      ):
       self.assertEqual(jitted_output.shape , output.shape      )



   def  __magic_name__     (			self      )     -> Tuple:
    __a      ,     __a     :						List[Any]          =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__      ):
      __a     :						Optional[int]          =    model_class(_UpperCAmelCase      )
      __a     :						Union[str, Any]          =    model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask']      )

      __a     :						Tuple          =    {
          'decoder_input_ids': inputs_dict['decoder_input_ids'],
          'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
          'encoder_outputs': encoder_outputs,
      }

      @jax.jit
      def decode_jitted(_A , _A , _A      ):
       return model.decode(
           decoder_input_ids=_UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , encoder_outputs=_UpperCAmelCase , )

      with self.subTest('JIT Enabled'      ):
       __a     :						Optional[Any]          =    decode_jitted(**_UpperCAmelCase      ).to_tuple()

      with self.subTest('JIT Disabled'      ):
       with jax.disable_jit():
        __a     :						str          =    decode_jitted(**_UpperCAmelCase      ).to_tuple()

      self.assertEqual(len(_UpperCAmelCase      ) , len(_UpperCAmelCase      )      )
      for jitted_output, output in zip(_UpperCAmelCase , _UpperCAmelCase      ):
       self.assertEqual(jitted_output.shape , output.shape      )



   @slow
   def  __magic_name__     (			self      )     -> List[str]:
    for model_class_name in self.all_model_classes:
     __a     :						Any          =    model_class_name.from_pretrained('facebook/blenderbot-400M-distill'      )
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     __a     :						int          =    np.ones((1, 1)      ) * model.config.eos_token_id
     __a     :						List[Any]          =    model(_UpperCAmelCase      )
     self.assertIsNotNone(_UpperCAmelCase      )



   @unittest.skipUnless(jax_device != 'cpu' , '3B test too slow on CPU.'      )
   @slow
   def  __magic_name__     (			self      )     -> List[Any]:
    __a     :						Optional[Any]          =    {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
    __a     :						str          =    {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

    __a     :						Union[str, Any]          =    FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' , from_pt=_UpperCAmelCase      )
    __a     :						Tuple          =    BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'      )

    __a     :						Optional[Any]          =    ['Sam']
    __a     :						Optional[int]          =    tokenizer(_UpperCAmelCase , return_tensors='jax'      )

    __a     :						int          =    model.generate(**_UpperCAmelCase , **_UpperCAmelCase      )
    __a     :						str          =    'Sam is a great name. It means "sun" in Gaelic.'

    __a     :						List[str]          =    tokenizer.batch_decode(_UpperCAmelCase , **_UpperCAmelCase      )
    assert generated_txt[0].strip() == tgt_text












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  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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			):
        # 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			):
        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()





