
'''simple docstring'''




from sklearn.metrics import matthews_corrcoef

import datasets


lowercase			:				List[str]    		=			'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

lowercase			:				Tuple    		=			'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

lowercase			:				int    		=			'\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION						)
class  A				(							datasets.Metric						):





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         return datasets.MetricInfo(
             description=_DESCRIPTION						,			citation=_CITATION						,			inputs_description=_KWARGS_DESCRIPTION						,			features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''int32'''	),
                     '''references''': datasets.Value('''int32'''	),
                 }	)						,			reference_urls=[
                 '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
             ]						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				Tuple:






         """simple docstring"""






         return {
             "matthews_correlation": float(matthews_corrcoef(_UpperCamelCase						,			_UpperCamelCase						,			sample_weight=_UpperCamelCase	)	),
         }











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
       snake_case__					:							Tuple													=     '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 _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






      if attention_mask is None:
            __A          =				np.where(input_ids != config.pad_token_id					,				1					,				0 )
      if decoder_attention_mask is None:
            __A          =				np.where(decoder_input_ids != config.pad_token_id					,				1					,				0 )
      if head_mask is None:
            __A          =				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            __A          =				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 A_					:




   def __init__(self	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __A          =				parent
         __A          =				batch_size
         __A          =				seq_length
         __A          =				is_training
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				eos_token_id
         __A          =				pad_token_id
         __A          =				bos_token_id
         __A          =				initializer_range




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

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				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          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

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

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				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 _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

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

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				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 A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				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          =				input_ids.shape[0]
         __A          =				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 _lowerCAmelCase      (self	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:int )->					Dict:
         __A          =				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          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,   dtype=np.intaa )
         __A          =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,   dtype=np.intaa )
         __A          =				lm_model(input_ids=_UpperCamelCase    ,   decoder_input_ids=_UpperCamelCase )
         __A          =				(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				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 A_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				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 _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A   ,						__A          =				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 _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

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

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				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 _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

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

                     __A          =				{
                         '''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	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				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 _lowerCAmelCase      (self	:int )->					Optional[int]:
         for model_class_name in self.all_model_classes:
               __A          =				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __A          =				np.ones((1, 1) ) * model.config.eos_token_id
               __A          =				model(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )






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

         __A          =				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,   from_pt=_UpperCamelCase )
         __A          =				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

         __A          =				model.generate(**_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				'''Sam is a great name. It means "sun" in Gaelic.'''

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





