

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMarianModel, TFMarianMTModel



@require_tf
class    A__:
						"""simple docstring"""






						_A  :							Optional[Any]   			=	MarianConfig
						_A  :							Optional[int]   			=	{}
						_A  :							Optional[Any]   			=	'''gelu'''





						def __init__(  self       ,     _lowercase       ,     _lowercase=13       ,     _lowercase=7       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=99       ,     _lowercase=32       ,     _lowercase=2       ,     _lowercase=4       ,     _lowercase=37       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=20       ,     _lowercase=2       ,     _lowercase=1       ,     _lowercase=0       ,     )      ->				List[Any]:
									a_		:		int				    =				parent
									a_		:		Dict				    =				batch_size
									a_		:		List[Any]				    =				seq_length
									a_		:		List[str]				    =				is_training
									a_		:		Dict				    =				use_labels
									a_		:		List[str]				    =				vocab_size
									a_		:		Dict				    =				hidden_size
									a_		:		List[Any]				    =				num_hidden_layers
									a_		:		str				    =				num_attention_heads
									a_		:		List[str]				    =				intermediate_size

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





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		str				    =				ids_tensor([self.batch_size, self.seq_length - 1]       ,     self.vocab_size  )
									a_		:		str				    =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )       ,     1  )
									a_		:		Any				    =				tf.concat([input_ids, eos_tensor]       ,     axis=1  )

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

									a_		:		Union[str, 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       ,     )
									a_		:		Any				    =				prepare_marian_inputs_dict(_lowercase       ,     _lowercase       ,     _lowercase  )
									return config, inputs_dict





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				List[Any]:
									a_		:		List[Any]				    =				TFMarianModel(config=_lowercase  ).get_decoder()
									a_		:		List[Any]				    =				inputs_dict["""input_ids"""]

									a_		:		Optional[int]				    =				input_ids[:1, :]
									a_		:		List[Any]				    =				inputs_dict["""attention_mask"""][:1, :]
									a_		:		int				    =				inputs_dict["""head_mask"""]
									a_		:		Any				    =				1

									# first forward pass
									a_		:		Tuple				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     head_mask=_lowercase       ,     use_cache=_lowercase  )

									a_    ,			a_		:		Union[str, Any]				    =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									a_		:		Tuple				    =				ids_tensor((self.batch_size, 3)       ,     config.vocab_size  )
									a_		:		Optional[int]				    =				tf.cast(ids_tensor((self.batch_size, 3)       ,     2  )       ,     tf.inta  )

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

									a_		:		Optional[Any]				    =				model(_lowercase       ,     attention_mask=_lowercase  )[0]
									a_		:		int				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase  )[0]

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

									# select random slice
									a_		:		Optional[Any]				    =				int(ids_tensor((1,)       ,     output_from_past.shape[-1]  )  )
									a_		:		List[Any]				    =				output_from_no_past[:, -3:, random_slice_idx]
									a_		:		int				    =				output_from_past[:, :, random_slice_idx]

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







def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__=None       ,					a__=None       ,					a__=None       ,					a__=None       ,					a__=None       ,					):

			'''simple docstring'''



			if attention_mask is None:
						a_		:		Union[str, Any]				    =				tf.cast(tf.math.not_equal(a__       ,					config.pad_token_id)       ,					tf.inta)
			if decoder_attention_mask is None:
						a_		:		Union[str, Any]				    =				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:
						a_		:		int				    =				tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						a_		:		int				    =				tf.ones((config.decoder_layers, config.decoder_attention_heads))
			if cross_attn_head_mask is None:
						a_		:		Any				    =				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    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							Dict   			=	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						_A  :							Any   			=	(TFMarianMTModel,) if is_tf_available() else ()
						_A  :							Optional[Any]   			=	(
						    {
						        '''conversational''': TFMarianMTModel,
						        '''feature-extraction''': TFMarianModel,
						        '''summarization''': TFMarianMTModel,
						        '''text2text-generation''': TFMarianMTModel,
						        '''translation''': TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						_A  :							Dict   			=	True
						_A  :							int   			=	False
						_A  :							int   			=	False





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Dict				    =				TFMarianModelTester(self  )
									a_		:		Dict				    =				ConfigTester(self       ,     config_class=_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									self.config_tester.run_common_tests()





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		Dict				    =				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_lowercase  )







@require_tf
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						_A  :							str   			=	1_000  # show more chars for failing integration tests





						@classmethod
						def 	UpperCamelCase__			(  cls  )      ->				None:
									a_		:		Dict				    =				F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
									return cls





						@cached_property
						def 	UpperCamelCase__			(  self  )      ->				MarianTokenizer:
									return AutoTokenizer.from_pretrained(self.model_name  )





						@property
						def 	UpperCamelCase__			(  self  )      ->				int:
									return self.tokenizer.eos_token_id





						@cached_property
						def 	UpperCamelCase__			(  self  )      ->				str:
									warnings.simplefilter("""error"""  )
									a_		:		TFMarianMTModel				    =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									assert isinstance(_lowercase       ,     _lowercase  )
									a_		:		Tuple				    =				model.config
									self.assertListEqual(c.bad_words_ids       ,     [[c.pad_token_id]]  )
									self.assertEqual(c.max_length       ,     512  )
									self.assertEqual(c.decoder_start_token_id       ,     c.pad_token_id  )
									return model





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				Union[str, Any]:
									a_		:		List[Any]				    =				self.translate_src_text(**_lowercase  )
									self.assertListEqual(self.expected_text       ,     _lowercase  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				Any:
									a_		:		str				    =				self.tokenizer(self.src_text       ,     **_lowercase       ,     padding=_lowercase       ,     return_tensors="""tf"""  )
									a_		:		Union[str, Any]				    =				self.model.generate(
									    model_inputs.input_ids       ,     attention_mask=model_inputs.attention_mask       ,     num_beams=2       ,     max_length=128  )
									a_		:		Dict				    =				self.tokenizer.batch_decode(generated_ids.numpy()       ,     skip_special_tokens=_lowercase  )
									return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class    A__(a_    ):
						"""simple docstring"""






						_A  :							List[Any]   			=	'''mt'''
						_A  :							Optional[int]   			=	'''en'''
						_A  :							int   			=	['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
						_A  :							Optional[Any]   			=	['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']





						@unittest.skip("""Skipping until #12647 is resolved."""  )
						@slow
						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class    A__(a_    ):
						"""simple docstring"""






						_A  :							List[Any]   			=	'''en'''
						_A  :							List[str]   			=	'''zh'''
						_A  :							Any   			=	['''My name is Wolfgang and I live in Berlin''']
						_A  :							Union[str, Any]   			=	['''我叫沃尔夫冈 我住在柏林''']





						@unittest.skip("""Skipping until #12647 is resolved."""  )
						@slow
						def 	UpperCamelCase__			(  self  )      ->				str:
									self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class    A__(a_    ):
						"""simple docstring"""






						_A  :							str   			=	'''en'''
						_A  :							Optional[int]   			=	'''ROMANCE'''
						_A  :							List[Any]   			=	[
						    '''>>fr<< Don\'t spend so much time watching TV.''',
						    '''>>pt<< Your message has been sent.''',
						    '''>>es<< He\'s two years older than me.''',
						]
						_A  :							List[Any]   			=	[
						    '''Ne passez pas autant de temps à regarder la télé.''',
						    '''A sua mensagem foi enviada.''',
						    '''Es dos años más viejo que yo.''',
						]





						@unittest.skip("""Skipping until #12647 is resolved."""  )
						@slow
						def 	UpperCamelCase__			(  self  )      ->				str:
									self._assert_generated_batch_equal_expected()





						@unittest.skip("""Skipping until #12647 is resolved."""  )
						@slow
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		List[Any]				    =				TranslationPipeline(self.model       ,     self.tokenizer       ,     framework="""tf"""  )
									a_		:		Tuple				    =				pipeline(self.src_text  )
									self.assertEqual(self.expected_text       ,     [x["""translation_text"""] for x in output]  )





from sklearn.metrics import mean_absolute_error

import datasets


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

__snake_case							:     List[Any] 						=	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__snake_case							:     str 						=	"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,       _KWARGS_DESCRIPTION    )
class    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(self._get_feature_types()  )       ,     reference_urls=[
									        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
									    ]       ,     )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									if self.config_name == "multilist":
												return {
												    "predictions": datasets.Sequence(datasets.Value("""float"""  )  ),
												    "references": datasets.Sequence(datasets.Value("""float"""  )  ),
												}
									else:
												return {
												    "predictions": datasets.Value("""float"""  ),
												    "references": datasets.Value("""float"""  ),
												}





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


