






import numpy as np

import datasets


a_		:    str   							=     '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

a_		:    str   							=     '\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

a_		:    Tuple   							=     '\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return datasets.MetricInfo(
						    description=_DESCRIPTION		,       citation=_CITATION		,       inputs_description=_KWARGS_DESCRIPTION		,       features=datasets.Features(
						        {
						            'X': datasets.Sequence(datasets.Value('float'		,       id='sequence')		,       id='X'),
						        })		,       )







			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							str:
						# convert to numpy arrays
						SCREAMING_SNAKE_CASE							=			np.array(a)
						SCREAMING_SNAKE_CASE							=			np.array(a)

						# Assert that arrays are 2D
						if len(X.shape) != 2:
									raise ValueError('Expected `X` to be a 2D vector')
						if len(reference_distribution.shape) != 2:
									raise ValueError('Expected `reference_distribution` to be a 2D vector')
						if reference_distribution.shape[0] < 2:
									raise ValueError(
									    'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension')

						# Get mahalanobis distance for each prediction
						SCREAMING_SNAKE_CASE							=			X - np.mean(a)
						SCREAMING_SNAKE_CASE							=			np.cov(reference_distribution.T)
						try:
									SCREAMING_SNAKE_CASE							=			np.linalg.inv(a)
						except np.linalg.LinAlgError:
									SCREAMING_SNAKE_CASE							=			np.linalg.pinv(a)
						SCREAMING_SNAKE_CASE							=			np.dot(a		,       a)
						SCREAMING_SNAKE_CASE							=			np.dot(a		,       X_minus_mu.T).diagonal()

						return {"mahalanobis": mahal_dist}








from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''gelu'''


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=False							,					A_=99							,					A_=32							,					A_=2							,					A_=4							,					A_=37							,					A_=0.1							,					A_=0.1							,					A_=20							,					A_=2							,					A_=1							,					A_=0							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

       _UpperCAmelCase		:							Union[str, Any]							=     hidden_dropout_prob
       _UpperCAmelCase		:							str							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[Any]							=     eos_token_id
       _UpperCAmelCase		:							Optional[Any]							=     pad_token_id
       _UpperCAmelCase		:							Tuple							=     bos_token_id


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    )
       _UpperCAmelCase		:							Tuple							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,					1    )
       _UpperCAmelCase		:							List[Any]							=     tf.concat([input_ids, eos_tensor]							,					axis=1    )

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[Any]							=     self.config_cls(
           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_ids=[2]							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.pad_token_id							,					**self.config_updates							,					)
       _UpperCAmelCase		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _UpperCAmelCase		:							List[Any]							=     ids_tensor((self.batch_size, 3)							,					config.vocab_size    )
       _UpperCAmelCase		:							List[str]							=     tf.cast(ids_tensor((self.batch_size, 3)							,					2    )							,					tf.inta    )

       # append to next input_ids and
       _UpperCAmelCase		:							Dict							=     tf.concat([input_ids, next_tokens]							,					axis=-1    )
       _UpperCAmelCase		:							Optional[Any]							=     tf.concat([attention_mask, next_attn_mask]							,					axis=-1    )

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     model(A_							,					attention_mask=A_							,					past_key_values=A_    )[0]

       self.parent.assertEqual(next_tokens.shape[1]							,					output_from_past.shape[1]    )

       # select random slice
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(A_							,					A_							,					rtol=1e-3    )




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							str							=     tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape						,   dtype=tf.inta     ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,   config.pad_token_id     )						,   tf.inta     ),
        ]						,   axis=-1						,   )
  if head_mask is None:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     tf.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": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }


@require_tf
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=A_    )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )





