





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
     _snake_case  :    Tuple						    =  'platform'

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			List[Any]=None,	lowerCAmelCase_      :			Any=None,	lowerCAmelCase_      :			Dict=None,	lowerCAmelCase_      :			Union[str, Any]=None,	lowerCAmelCase_      :			Tuple=None,	lowerCAmelCase_      :			int=None,	):
   if attention_mask is None:
      __lowerCAmelCase								=	np.where(input_ids != config.pad_token_id,	1,	0	)
   if decoder_attention_mask is None:
      __lowerCAmelCase								=	np.where(decoder_input_ids != config.pad_token_id,	1,	0	)
   if head_mask is None:
      __lowerCAmelCase								=	np.ones((config.encoder_layers, config.encoder_attention_heads)	)
   if decoder_head_mask is None:
      __lowerCAmelCase								=	np.ones((config.decoder_layers, config.decoder_attention_heads)	)
   if cross_attn_head_mask is None:
      __lowerCAmelCase								=	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 				_UpperCAmelCase    :

  """simple docstring"""


  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Optional[Any]=1_3					,  lowerCAmelCase_ :		Optional[int]=7					,  lowerCAmelCase_ :		Optional[int]=True					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		Union[str, Any]=9_9					,  lowerCAmelCase_ :		List[Any]=1_6					,  lowerCAmelCase_ :		Union[str, Any]=2					,  lowerCAmelCase_ :		List[str]=4					,  lowerCAmelCase_ :		str=4					,  lowerCAmelCase_ :		int="gelu"					,  lowerCAmelCase_ :		Optional[int]=0.1					,  lowerCAmelCase_ :		str=0.1					,  lowerCAmelCase_ :		int=3_2					,  lowerCAmelCase_ :		List[str]=2					,  lowerCAmelCase_ :		Optional[Any]=1					,  lowerCAmelCase_ :		int=0					,  lowerCAmelCase_ :		int=0.02					,  )							->						str:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	seq_length
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	eos_token_id
     __lowerCAmelCase								=	pad_token_id
     __lowerCAmelCase								=	bos_token_id
     __lowerCAmelCase								=	initializer_range






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

     __lowerCAmelCase								=	shift_tokens_right(lowerCAmelCase_					,  1					,  2     )

     __lowerCAmelCase								=	BlenderbotSmallConfig(
         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_					,  )
     __lowerCAmelCase								=	prepare_blenderbot_inputs_dict(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )
     return config, inputs_dict






  def 				lowercase		(				self :		List[Any]     )							->						Dict:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.prepare_config_and_inputs()
     return config, inputs_dict






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Any     )							->						Optional[int]:
     __lowerCAmelCase								=	2_0
     __lowerCAmelCase								=	model_class_name(lowerCAmelCase_     )

     __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     __lowerCAmelCase								=	model.init_cache(decoder_input_ids.shape[0]					,  lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,  dtype='i4'     )

     __lowerCAmelCase								=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, :-1]					,  lowerCAmelCase_					,  decoder_attention_mask=lowerCAmelCase_					,  past_key_values=lowerCAmelCase_					,  decoder_position_ids=lowerCAmelCase_					,  )

     __lowerCAmelCase								=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype='i4'     )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, -1:]					,  lowerCAmelCase_					,  decoder_attention_mask=lowerCAmelCase_					,  past_key_values=outputs_cache.past_key_values					,  decoder_position_ids=lowerCAmelCase_					,  )

     __lowerCAmelCase								=	model.decode(lowerCAmelCase_					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	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 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Union[str, Any]     )							->						Any:
     __lowerCAmelCase								=	2_0
     __lowerCAmelCase								=	model_class_name(lowerCAmelCase_     )

     __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

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

     __lowerCAmelCase								=	model.init_cache(decoder_input_ids.shape[0]					,  lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )

     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, :-1]					,  lowerCAmelCase_					,  decoder_attention_mask=lowerCAmelCase_					,  past_key_values=lowerCAmelCase_					,  decoder_position_ids=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype='i4'     )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, -1:]					,  lowerCAmelCase_					,  past_key_values=outputs_cache.past_key_values					,  decoder_attention_mask=lowerCAmelCase_					,  decoder_position_ids=lowerCAmelCase_					,  )

     __lowerCAmelCase								=	model.decode(lowerCAmelCase_					,  lowerCAmelCase_					,  decoder_attention_mask=lowerCAmelCase_     )

     __lowerCAmelCase								=	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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  a_        = 99






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     __lowerCAmelCase								=	np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]					,  dtype=np.intaa					,  )

     __lowerCAmelCase								=	input_ids.shape[0]
     __lowerCAmelCase								=	BlenderbotSmallConfig(
         vocab_size=self.vocab_size					,  d_model=2_4					,  encoder_layers=2					,  decoder_layers=2					,  encoder_attention_heads=2					,  decoder_attention_heads=2					,  encoder_ffn_dim=3_2					,  decoder_ffn_dim=3_2					,  max_position_embeddings=4_8					,  eos_token_id=2					,  pad_token_id=1					,  bos_token_id=0					,  )
     return config, input_ids, batch_size






  def 				lowercase		(				self :		Optional[int]     )							->						Tuple:
     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	self._get_config_and_data()
     __lowerCAmelCase								=	FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase_     )
     __lowerCAmelCase								=	lm_model(input_ids=lowerCAmelCase_     )
     __lowerCAmelCase								=	(batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['logits'].shape					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						List[str]:
     __lowerCAmelCase								=	BlenderbotSmallConfig(
         vocab_size=self.vocab_size					,  d_model=1_4					,  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=4_8					,  )
     __lowerCAmelCase								=	FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase_     )
     __lowerCAmelCase								=	np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,  dtype=np.intaa     )
     __lowerCAmelCase								=	np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,  dtype=np.intaa     )
     __lowerCAmelCase								=	lm_model(input_ids=lowerCAmelCase_					,  decoder_input_ids=lowerCAmelCase_     )
     __lowerCAmelCase								=	(*summary.shape, config.vocab_size)
     self.assertEqual(outputs['logits'].shape					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,  dtype=np.intaa     )
     __lowerCAmelCase								=	shift_tokens_right(lowerCAmelCase_					,  1					,  2     )
     __lowerCAmelCase								=	np.equal(lowerCAmelCase_					,  1     ).astype(np.floataa     ).sum()
     __lowerCAmelCase								=	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 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase		,    _UpperCamelCase    ):

  """simple docstring"""


  a_        = True
  a_        = (
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a_        = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






  def 				lowercase		(				self :		Optional[int]     )							->						List[Any]:
     __lowerCAmelCase								=	FlaxBlenderbotSmallModelTester(self     )






  def 				lowercase		(				self :		Tuple     )							->						Tuple:
     __lowerCAmelCase	,	__lowerCAmelCase								=	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 				lowercase		(				self :		Union[str, Any]     )							->						List[str]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	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 				lowercase		(				self :		Tuple     )							->						Tuple:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__     ):
           __lowerCAmelCase								=	self._prepare_for_class(lowerCAmelCase_					,  lowerCAmelCase_     )
           __lowerCAmelCase								=	model_class(lowerCAmelCase_     )

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

           with self.subTest('JIT Enabled'     ):
              __lowerCAmelCase								=	encode_jitted(**lowerCAmelCase_     ).to_tuple()

           with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                 __lowerCAmelCase								=	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 				lowercase		(				self :		int     )							->						Any:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__     ):
           __lowerCAmelCase								=	model_class(lowerCAmelCase_     )
           __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']					,  inputs_dict['attention_mask']     )

           __lowerCAmelCase								=	{
               '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_ :		int					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		int     ):
              return model.decode(
                  decoder_input_ids=lowerCAmelCase_					,  decoder_attention_mask=lowerCAmelCase_					,  encoder_outputs=lowerCAmelCase_					,  )

           with self.subTest('JIT Enabled'     ):
              __lowerCAmelCase								=	decode_jitted(**lowerCAmelCase_     ).to_tuple()

           with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                 __lowerCAmelCase								=	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 				lowercase		(				self :		List[str]     )							->						List[Any]:
     for model_class_name in self.all_model_classes:
        __lowerCAmelCase								=	model_class_name.from_pretrained('facebook/blenderbot_small-90M'     )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        __lowerCAmelCase								=	np.ones((1, 1)     ) * model.config.eos_token_id
        __lowerCAmelCase								=	model(lowerCAmelCase_     )
        self.assertIsNotNone(lowerCAmelCase_     )








import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

if NLTK_AVAILABLE:
     with FileLock('.lock') as lock:
          nltk.download('punkt', quiet=True)

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowerCAmelCase_	)	)

