


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			: List[str]				   =					"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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					)       ->		str:
    if attention_mask is None:
        _lowercase			:				Optional[int] 						=						np.where(input_ids != config.pad_token_id				,					1				,					0					)
    if decoder_attention_mask is None:
        _lowercase			:				Optional[Any] 						=						np.where(decoder_input_ids != config.pad_token_id				,					1				,					0					)
    if head_mask is None:
        _lowercase			:				Tuple 						=						np.ones((config.encoder_layers, config.encoder_attention_heads)					)
    if decoder_head_mask is None:
        _lowercase			:				Union[str, Any] 						=						np.ones((config.decoder_layers, config.decoder_attention_heads)					)
    if cross_attn_head_mask is None:
        _lowercase			:				Tuple 						=						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:
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=99, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=32, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=0.0_2, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						parent
           _lowercase			:				Union[str, Any] 						=						batch_size
           _lowercase			:				List[Any] 						=						seq_length
           _lowercase			:				Optional[int] 						=						is_training
           _lowercase			:				List[str] 						=						use_labels
           _lowercase			:				List[str] 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						hidden_size
           _lowercase			:				Dict 						=						num_hidden_layers
           _lowercase			:				Optional[Any] 						=						num_attention_heads
           _lowercase			:				str 						=						intermediate_size
           _lowercase			:				Optional[Any] 						=						hidden_act
           _lowercase			:				List[Any] 						=						hidden_dropout_prob
           _lowercase			:				Any 						=						attention_probs_dropout_prob
           _lowercase			:				Optional[int] 						=						max_position_embeddings
           _lowercase			:				Union[str, Any] 						=						eos_token_id
           _lowercase			:				int 						=						pad_token_id
           _lowercase			:				Tuple 						=						bos_token_id
           _lowercase			:				Optional[int] 						=						initializer_range
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
           _lowercase			:				str 						=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.intaa)), -1)

           _lowercase			:				Union[str, Any] 						=						shift_tokens_right(lowerCamelCase, 1, 2)

           _lowercase			:				Optional[Any] 						=						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=lowerCamelCase, )
           _lowercase			:				Any 						=						prepare_blenderbot_inputs_dict(lowerCamelCase, lowerCamelCase, lowerCamelCase)
           return config, inputs_dict
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Any 						=						self.prepare_config_and_inputs()
           return config, inputs_dict
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						20
           _lowercase			:				Dict 						=						model_class_name(lowerCamelCase)

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

           _lowercase						,							_lowercase			:				Optional[Any] 						=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _lowercase			:				Dict 						=						model.init_cache(decoder_input_ids.shape[0], lowerCamelCase, lowerCamelCase)
           _lowercase			:				int 						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype='i4')

           _lowercase			:				int 						=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), )
           _lowercase			:				Optional[Any] 						=						model.decode(
               decoder_input_ids[:, :-1], lowerCamelCase, decoder_attention_mask=lowerCamelCase, past_key_values=lowerCamelCase, decoder_position_ids=lowerCamelCase, )

           _lowercase			:				str 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype='i4')
           _lowercase			:				Optional[int] 						=						model.decode(
               decoder_input_ids[:, -1:], lowerCamelCase, decoder_attention_mask=lowerCamelCase, past_key_values=outputs_cache.past_key_values, decoder_position_ids=lowerCamelCase, )

           _lowercase			:				int 						=						model.decode(lowerCamelCase, lowerCamelCase)

           _lowercase			:				Optional[Any] 						=						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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				str 						=						20
           _lowercase			:				Tuple 						=						model_class_name(lowerCamelCase)

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

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

           _lowercase			:				Tuple 						=						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
               ], axis=-1, )

           _lowercase			:				Dict 						=						model.init_cache(decoder_input_ids.shape[0], lowerCamelCase, lowerCamelCase)
           _lowercase			:				Optional[int] 						=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), )

           _lowercase			:				List[Any] 						=						model.decode(
               decoder_input_ids[:, :-1], lowerCamelCase, decoder_attention_mask=lowerCamelCase, past_key_values=lowerCamelCase, decoder_position_ids=lowerCamelCase, )
           _lowercase			:				str 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype='i4')
           _lowercase			:				List[str] 						=						model.decode(
               decoder_input_ids[:, -1:], lowerCamelCase, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=lowerCamelCase, decoder_position_ids=lowerCamelCase, )

           _lowercase			:				Tuple 						=						model.decode(lowerCamelCase, lowerCamelCase, decoder_attention_mask=lowerCamelCase)

           _lowercase			:				str 						=						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  ):
       lowercase_					:      Optional[int]       					=     99
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Union[str, 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, )

           _lowercase			:				Optional[Any] 						=						input_ids.shape[0]
           _lowercase			:				int 						=						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      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase			:				Any 						=						self._get_config_and_data()
           _lowercase			:				Tuple 						=						FlaxBlenderbotForConditionalGeneration(lowerCamelCase)
           _lowercase			:				List[str] 						=						lm_model(input_ids=lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['logits'].shape, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						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, )
           _lowercase			:				str 						=						FlaxBlenderbotForConditionalGeneration(lowerCamelCase)
           _lowercase			:				List[str] 						=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.intaa)
           _lowercase			:				Any 						=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.intaa)
           _lowercase			:				Tuple 						=						lm_model(input_ids=lowerCamelCase, decoder_input_ids=lowerCamelCase)
           _lowercase			:				List[Any] 						=						(*summary.shape, config.vocab_size)
           self.assertEqual(outputs['logits'].shape, lowerCamelCase)




       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Any 						=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.intaa)
           _lowercase			:				Dict 						=						shift_tokens_right(lowerCamelCase, 1, 2)
           _lowercase			:				str 						=						np.equal(lowerCamelCase, 1).astype(np.floataa).sum()
           _lowercase			:				Union[str, Any] 						=						np.equal(lowerCamelCase, 1).astype(np.floataa).sum()
           self.assertEqual(shifted.shape, input_ids.shape)
           self.assertEqual(lowerCamelCase, n_pad_before - 1)
           self.assertTrue(np.equal(shifted[:, 0], 2).all())






@require_flax
class _lowerCamelCase(		_a,					unittest.TestCase,					_a  ):
       lowercase_					:      Any       					=     True
       lowercase_					:      Optional[int]       					=     (
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       lowercase_					:      Dict       					=     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						FlaxBlenderbotModelTester(self)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase						,							_lowercase			:				int 						=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[str] 						=						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(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   _lowercase			:				Optional[Any] 						=						self._prepare_for_class(lowerCamelCase, lowerCamelCase)
                   _lowercase			:				Any 						=						model_class(lowerCamelCase)

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

                   with self.subTest('JIT Enabled'):
                       _lowercase			:				List[str] 						=						encode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase			:				Union[str, Any] 						=						encode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase), len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase, lowerCamelCase):
                       self.assertEqual(jitted_output.shape, output.shape)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				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__):
                   _lowercase			:				int 						=						model_class(lowerCamelCase)
                   _lowercase			:				Union[str, Any] 						=						model.encode(inputs_dict['input_ids'], inputs_dict['attention_mask'])

                   _lowercase			:				Optional[int] 						=						{
                       '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(lowerCamelCase, lowerCamelCase, lowerCamelCase):
                       return model.decode(
                           decoder_input_ids=lowerCamelCase, decoder_attention_mask=lowerCamelCase, encoder_outputs=lowerCamelCase, )

                   with self.subTest('JIT Enabled'):
                       _lowercase			:				Union[str, Any] 						=						decode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase			:				Optional[int] 						=						decode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase), len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase, lowerCamelCase):
                       self.assertEqual(jitted_output.shape, output.shape)
       @slow
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           for model_class_name in self.all_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class_name.from_pretrained('facebook/blenderbot-400M-distill')
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _lowercase			:				Any 						=						np.ones((1, 1)) * model.config.eos_token_id
               _lowercase			:				Optional[Any] 						=						model(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @unittest.skipUnless(jax_device != 'cpu', '3B test too slow on CPU.')
       @slow
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
           _lowercase			:				Any 						=						{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

           _lowercase			:				Any 						=						FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B', from_pt=lowerCamelCase)
           _lowercase			:				Dict 						=						BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B')

           _lowercase			:				int 						=						['Sam']
           _lowercase			:				str 						=						tokenizer(lowerCamelCase, return_tensors='jax')

           _lowercase			:				Optional[Any] 						=						model.generate(**lowerCamelCase, **lowerCamelCase)
           _lowercase			:				Any 						=						'Sam is a great name. It means "sun" in Gaelic.'

           _lowercase			:				Tuple 						=						tokenizer.batch_decode(lowerCamelCase, **lowerCamelCase)
           assert generated_txt[0].strip() == tgt_text



'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						field(
      default='''O1''' ,				metadata={
          '''help''': (
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )
      } ,				)




  @cached_property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[1]





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

