


"""simple docstring"""
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'''

							__lowercase:		List[Any]				   =     MarianConfig
							__lowercase:		Dict				   =     {}
							__lowercase:		Any				   =     """gelu"""



							def __init__(					self				:   Optional[Any]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   List[str]=13		,							UpperCAmelCase_				:   Union[str, Any]=7		,							UpperCAmelCase_				:   int=True		,							UpperCAmelCase_				:   int=False		,							UpperCAmelCase_				:   List[Any]=99		,							UpperCAmelCase_				:   Dict=32		,							UpperCAmelCase_				:   Tuple=2		,							UpperCAmelCase_				:   Tuple=4		,							UpperCAmelCase_				:   str=37		,							UpperCAmelCase_				:   Dict=0.1		,							UpperCAmelCase_				:   Optional[int]=0.1		,							UpperCAmelCase_				:   List[str]=20		,							UpperCAmelCase_				:   Optional[Any]=2		,							UpperCAmelCase_				:   Union[str, Any]=1		,							UpperCAmelCase_				:   Dict=0		,							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		parent
														snake_case_     							=		batch_size
														snake_case_     							=		seq_length
														snake_case_     							=		is_training
														snake_case_     							=		use_labels
														snake_case_     							=		vocab_size
														snake_case_     							=		hidden_size
														snake_case_     							=		num_hidden_layers
														snake_case_     							=		num_attention_heads
														snake_case_     							=		intermediate_size

														snake_case_     							=		hidden_dropout_prob
														snake_case_     							=		attention_probs_dropout_prob
														snake_case_     							=		max_position_embeddings
														snake_case_     							=		eos_token_id
														snake_case_     							=		pad_token_id
														snake_case_     							=		bos_token_id



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		ids_tensor([self.batch_size, self.seq_length - 1]		,							self.vocab_size							)
														snake_case_     							=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		,							1							)
														snake_case_     							=		tf.concat([input_ids, eos_tensor]		,							axis=1							)

														snake_case_     							=		ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size							)

														snake_case_     							=		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		,							)
														snake_case_     							=		prepare_marian_inputs_dict(__lowerCamelCase		,							__lowerCamelCase		,							__lowerCamelCase							)
														return config, inputs_dict



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Tuple							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		TFMarianModel(config=__lowerCamelCase							).get_decoder()
														snake_case_     							=		inputs_dict["input_ids"]

														snake_case_     							=		input_ids[:1, :]
														snake_case_     							=		inputs_dict["attention_mask"][:1, :]
														snake_case_     							=		inputs_dict["head_mask"]
														snake_case_     							=		1

														# first forward pass
														snake_case_     							=		model(__lowerCamelCase		,							attention_mask=__lowerCamelCase		,							head_mask=__lowerCamelCase		,							use_cache=__lowerCamelCase							)

														snake_case_     							=		outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														snake_case_     							=		ids_tensor((self.batch_size, 3)		,							config.vocab_size							)
														snake_case_     							=		tf.cast(ids_tensor((self.batch_size, 3)		,							2							)		,							tf.inta							)

														# append to next input_ids and
														snake_case_     							=		tf.concat([input_ids, next_tokens]		,							axis=-1							)
														snake_case_     							=		tf.concat([attention_mask, next_attn_mask]		,							axis=-1							)

														snake_case_     							=		model(__lowerCamelCase		,							attention_mask=__lowerCamelCase							)[0]
														snake_case_     							=		model(__lowerCamelCase		,							attention_mask=__lowerCamelCase		,							past_key_values=__lowerCamelCase							)[0]

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

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

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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			)     ->					List[Any]:




							if attention_mask is None:
														snake_case_     							=		tf.cast(tf.math.not_equal(_A					,			config.pad_token_id					)					,			tf.inta					)
							if decoder_attention_mask is None:
														snake_case_     							=		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:
														snake_case_     							=		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														snake_case_     							=		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														snake_case_     							=		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  (lowercase__    ,		lowercase__    ,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		int				   =     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							__lowercase:		Dict				   =     (TFMarianMTModel,) if is_tf_available() else ()
							__lowercase:		List[Any]				   =     (
							    {
							        """conversational""": TFMarianMTModel,
							        """feature-extraction""": TFMarianModel,
							        """summarization""": TFMarianMTModel,
							        """text2text-generation""": TFMarianMTModel,
							        """translation""": TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							__lowercase:		int				   =     True
							__lowercase:		Tuple				   =     False
							__lowercase:		str				   =     False



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Optional[Any]:



														"""simple docstring"""



														snake_case_     							=		TFMarianModelTester(self							)
														snake_case_     							=		ConfigTester(self		,							config_class=__lowerCamelCase							)



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



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase							(					self				:   int							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase							)





@require_tf
class   __A  (unittest.TestCase):





							'''simple docstring'''

							__lowercase:		int				   =     10_00  # show more chars for failing integration tests



							@classmethod
							def 		lowerCAmelCase							(					cls				:   Optional[Any]							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
														return cls



							@cached_property
							def 		lowerCAmelCase							(					self				:   int							)       ->int:



														"""simple docstring"""



														return AutoTokenizer.from_pretrained(self.model_name							)



							@property
							def 		lowerCAmelCase							(					self				:   Dict							)       ->Tuple:



														"""simple docstring"""



														return self.tokenizer.eos_token_id



							@cached_property
							def 		lowerCAmelCase							(					self				:   str							)       ->Union[str, Any]:



														"""simple docstring"""



														warnings.simplefilter("""error"""							)
														snake_case_     							=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
														assert isinstance(__lowerCamelCase		,							__lowerCamelCase							)
														snake_case_     							=		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 		lowerCAmelCase							(					self				:   Optional[int]		,							**UpperCAmelCase_				:   Optional[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		self.translate_src_text(**__lowerCamelCase							)
														self.assertListEqual(self.expected_text		,							__lowerCamelCase							)



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							**UpperCAmelCase_				:   List[Any]							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		self.tokenizer(self.src_text		,							**__lowerCamelCase		,							padding=__lowerCamelCase		,							return_tensors="""tf"""							)
														snake_case_     							=		self.model.generate(
														    model_inputs.input_ids		,							attention_mask=model_inputs.attention_mask		,							num_beams=2		,							max_length=128							)
														snake_case_     							=		self.tokenizer.batch_decode(generated_ids.numpy()		,							skip_special_tokens=__lowerCamelCase							)
														return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class   __A  (lowercase__):





							'''simple docstring'''

							__lowercase:		List[Any]				   =     """mt"""
							__lowercase:		Dict				   =     """en"""
							__lowercase:		Tuple				   =     ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
							__lowercase:		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 		lowerCAmelCase							(					self				:   str							)       ->int:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class   __A  (lowercase__):





							'''simple docstring'''

							__lowercase:		List[Any]				   =     """en"""
							__lowercase:		str				   =     """zh"""
							__lowercase:		Optional[int]				   =     ["""My name is Wolfgang and I live in Berlin"""]
							__lowercase:		int				   =     ["""我叫沃尔夫冈 我住在柏林"""]



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



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class   __A  (lowercase__):





							'''simple docstring'''

							__lowercase:		Any				   =     """en"""
							__lowercase:		Tuple				   =     """ROMANCE"""
							__lowercase:		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.""",
							]
							__lowercase:		List[str]				   =     [
							    """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 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()



							@unittest.skip("""Skipping until #12647 is resolved."""							)
							@slow
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		TranslationPipeline(self.model		,							self.tokenizer		,							framework="""tf"""							)
														snake_case_     							=		pipeline(self.src_text							)
														self.assertEqual(self.expected_text		,							[x["""translation_text"""] for x in output]							)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       Tuple						=   [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


