





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
      lowerCAmelCase_						   =    """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_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	int=None    ,							lowerCAmelCase:	List[str]=None    ,							lowerCAmelCase:	str=None    ,							lowerCAmelCase:	Union[str, Any]=None    ,							lowerCAmelCase:	Union[str, Any]=None    ,							lowerCAmelCase:	Dict=None    ,							)->		Dict:
  if attention_mask is None:
    _snake_case   :				str														=	np.where(input_ids != config.pad_token_id    ,							1    ,							0				)
  if decoder_attention_mask is None:
    _snake_case   :				Any														=	np.where(decoder_input_ids != config.pad_token_id    ,							1    ,							0				)
  if head_mask is None:
    _snake_case   :				Any														=	np.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    _snake_case   :				Optional[int]														=	np.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    _snake_case   :				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    :


  '''simple docstring'''







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					int=13      ,				UpperCamelCase      :					List[Any]=7      ,				UpperCamelCase      :					Union[str, Any]=True      ,				UpperCamelCase      :					Optional[Any]=False      ,				UpperCamelCase      :					Optional[int]=99      ,				UpperCamelCase      :					Tuple=16      ,				UpperCamelCase      :					Union[str, Any]=2      ,				UpperCamelCase      :					int=4      ,				UpperCamelCase      :					List[str]=4      ,				UpperCamelCase      :					Optional[int]="gelu"      ,				UpperCamelCase      :					Optional[Any]=0.1      ,				UpperCamelCase      :					str=0.1      ,				UpperCamelCase      :					List[Any]=32      ,				UpperCamelCase      :					str=2      ,				UpperCamelCase      :					Union[str, Any]=1      ,				UpperCamelCase      :					str=0      ,				UpperCamelCase      :					List[str]=0.02      ,				):


    '''simple docstring'''





    _snake_case   :				List[str]														=	parent
    _snake_case   :				Optional[int]														=	batch_size
    _snake_case   :				List[str]														=	seq_length
    _snake_case   :				str														=	is_training
    _snake_case   :				Dict														=	use_labels
    _snake_case   :				Any														=	vocab_size
    _snake_case   :				Dict														=	hidden_size
    _snake_case   :				Tuple														=	num_hidden_layers
    _snake_case   :				Optional[Any]														=	num_attention_heads
    _snake_case   :				Optional[int]														=	intermediate_size
    _snake_case   :				Optional[int]														=	hidden_act
    _snake_case   :				List[Any]														=	hidden_dropout_prob
    _snake_case   :				Dict														=	attention_probs_dropout_prob
    _snake_case   :				Optional[int]														=	max_position_embeddings
    _snake_case   :				List[str]														=	eos_token_id
    _snake_case   :				int														=	pad_token_id
    _snake_case   :				Dict														=	bos_token_id
    _snake_case   :				Tuple														=	initializer_range







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





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

    _snake_case   :				Any														=	shift_tokens_right(UpperCamelCase      ,				1      ,				2							)

    _snake_case   :				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      ,				)
    _snake_case   :				Optional[int]														=	prepare_blenderbot_inputs_dict(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)
    return config, inputs_dict







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case							,					_snake_case   :				List[Any]														=	self.prepare_config_and_inputs()
    return config, inputs_dict







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	20
    _snake_case   :				Optional[Any]														=	model_class_name(UpperCamelCase							)

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

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

    _snake_case   :				Tuple														=	model.init_cache(decoder_input_ids.shape[0]      ,				UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				Dict														=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,				dtype='i4'							)

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

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

    _snake_case   :				str														=	model.decode(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				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 UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	20
    _snake_case   :				Optional[Any]														=	model_class_name(UpperCamelCase							)

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

    _snake_case							,					_snake_case   :				Union[str, Any]														=	(
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

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

    _snake_case   :				List[Any]														=	model.init_cache(decoder_input_ids.shape[0]      ,				UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)

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

    _snake_case   :				Optional[Any]														=	model.decode(UpperCamelCase      ,				UpperCamelCase      ,				decoder_attention_mask=UpperCamelCase							)

    _snake_case   :				List[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}"""							)




@require_flax
class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''
  a_							:       Optional[int]      					=99







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				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      ,				)

    _snake_case   :				int														=	input_ids.shape[0]
    _snake_case   :				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 UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case							,					_snake_case							,					_snake_case   :				Optional[int]														=	self._get_config_and_data()
    _snake_case   :				int														=	FlaxBlenderbotForConditionalGeneration(UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	lm_model(input_ids=UpperCamelCase							)
    _snake_case   :				List[str]														=	(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['logits'].shape      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,				dtype=np.intaa							)
    _snake_case   :				Dict														=	shift_tokens_right(UpperCamelCase      ,				1      ,				2							)
    _snake_case   :				Union[str, Any]														=	np.equal(UpperCamelCase      ,				1							).astype(np.floataa							).sum()
    _snake_case   :				Dict														=	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_							:       Tuple      					=True
  a_							:       Optional[int]      					=(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a_							:       List[Any]      					=(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	FlaxBlenderbotModelTester(self							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case							,					_snake_case   :				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 UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case							,					_snake_case   :				Optional[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 UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case							,					_snake_case   :				int														=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__							):
        _snake_case   :				Dict														=	self._prepare_for_class(UpperCamelCase      ,				UpperCamelCase							)
        _snake_case   :				List[Any]														=	model_class(UpperCamelCase							)

        @jax.jit
        def encode_jitted(UpperCamelCase      :					str      ,				UpperCamelCase      :					Dict=None      ,				**UpperCamelCase      :					Union[str, Any]							):
          return model.encode(input_ids=UpperCamelCase      ,				attention_mask=UpperCamelCase							)

        with self.subTest('JIT Enabled'							):
          _snake_case   :				int														=	encode_jitted(**UpperCamelCase							).to_tuple()

        with self.subTest('JIT Disabled'							):
          with jax.disable_jit():
            _snake_case   :				List[str]														=	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 UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case							,					_snake_case   :				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__							):
        _snake_case   :				Optional[int]														=	model_class(UpperCamelCase							)
        _snake_case   :				Optional[int]														=	model.encode(inputs_dict['input_ids']      ,				inputs_dict['attention_mask']							)

        _snake_case   :				Optional[Any]														=	{
            '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(UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					str							):
          return model.decode(
              decoder_input_ids=UpperCamelCase      ,				decoder_attention_mask=UpperCamelCase      ,				encoder_outputs=UpperCamelCase      ,				)

        with self.subTest('JIT Enabled'							):
          _snake_case   :				List[str]														=	decode_jitted(**UpperCamelCase							).to_tuple()

        with self.subTest('JIT Disabled'							):
          with jax.disable_jit():
            _snake_case   :				int														=	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 UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





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







  @unittest.skipUnless(jax_device != 'cpu'      ,				'3B test too slow on CPU.'							)
  @slow
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
    _snake_case   :				List[Any]														=	{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

    _snake_case   :				List[str]														=	FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'      ,				from_pt=UpperCamelCase							)
    _snake_case   :				Optional[int]														=	BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'							)

    _snake_case   :				Dict														=	['Sam']
    _snake_case   :				Union[str, Any]														=	tokenizer(UpperCamelCase      ,				return_tensors='jax'							)

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

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











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		int:

  if not isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Union[str, Any]														=	F"""Input value of [number={number}] must be an integer"""
    raise TypeError(lowerCAmelCase				)

  if number < 1:
    _snake_case   :				int														=	F"""Input value of [number={number}] must be > 0"""
    raise ValueError(lowerCAmelCase				)

  _snake_case   :				int														=	1

  for i in range(1    ,							lowerCAmelCase				):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
      import doctest

      doctest.testmod()




