"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
     _SCREAMING_SNAKE_CASE	:		Any   =   '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	List[Any]      , lowercase_     :	Optional[int]=None      , lowercase_     :	Tuple=None      , lowercase_     :	List[str]=None      , lowercase_     :	Tuple=None      , lowercase_     :	str=None      , lowercase_     :	Any=None      , )  ->							Optional[Any]:
   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      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				):


      '''simple docstring'''

      __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    _A				(	self	):


      '''simple docstring'''

      __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(_SCREAMING_SNAKE_CASE ,				1 ,				2	)

      __lowerCamelCase    		=					MarianConfig(
          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=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					prepare_marian_inputs_dict(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      return config, inputs_dict







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.prepare_config_and_inputs()
      return config, inputs_dict







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					20
      __lowerCamelCase    		=					model_class_name(_SCREAMING_SNAKE_CASE	)

      __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] ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      __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] ,				_SCREAMING_SNAKE_CASE ,				decoder_attention_mask=_SCREAMING_SNAKE_CASE ,				past_key_values=_SCREAMING_SNAKE_CASE ,				decoder_position_ids=_SCREAMING_SNAKE_CASE ,				)

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

      __lowerCamelCase    		=					model.decode(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      __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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					20
      __lowerCamelCase    		=					model_class_name(_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					model.decode(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				decoder_attention_mask=_SCREAMING_SNAKE_CASE	)

      __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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				unittest.TestCase    ,				UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    True
   __UpperCAmelCase	:							List[Any]    =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   __UpperCAmelCase	:							List[Any]    =    (FlaxMarianMTModel,) if is_flax_available() else ()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					FlaxMarianModelTester(self	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __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(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __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(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)

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

            with self.subTest('JIT Enabled'	):
               __lowerCamelCase    		=					encode_jitted(**_SCREAMING_SNAKE_CASE	).to_tuple()

            with self.subTest('JIT Disabled'	):
               with jax.disable_jit():
                  __lowerCamelCase    		=					encode_jitted(**_SCREAMING_SNAKE_CASE	).to_tuple()

            self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				len(_SCREAMING_SNAKE_CASE	)	)
            for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
               self.assertEqual(jitted_output.shape ,				output.shape	)







   def    _A				(	self	):


      '''simple docstring'''

      __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(_SCREAMING_SNAKE_CASE	)
            __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(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
               return model.decode(
                   decoder_input_ids=_SCREAMING_SNAKE_CASE ,				decoder_attention_mask=_SCREAMING_SNAKE_CASE ,				encoder_outputs=_SCREAMING_SNAKE_CASE ,				)

            with self.subTest('JIT Enabled'	):
               __lowerCamelCase    		=					decode_jitted(**_SCREAMING_SNAKE_CASE	).to_tuple()

            with self.subTest('JIT Disabled'	):
               with jax.disable_jit():
                  __lowerCamelCase    		=					decode_jitted(**_SCREAMING_SNAKE_CASE	).to_tuple()

            self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				len(_SCREAMING_SNAKE_CASE	)	)
            for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
               self.assertEqual(jitted_output.shape ,				output.shape	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __lowerCamelCase    		=					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
         # FlaxMarianForSequenceClassification expects eos token in input_ids
         __lowerCamelCase    		=					np.ones((1, 1)	) * model.config.eos_token_id
         __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    None
   __UpperCAmelCase	:							Tuple    =    None







   @classmethod
   def    _A				(	cls	):


      '''simple docstring'''

      __lowerCamelCase    		=					F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls







   @cached_property
   def    _A				(	self	):


      '''simple docstring'''

      return MarianTokenizer.from_pretrained(self.model_name	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.tokenizer.eos_token_id







   @cached_property
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					FlaxMarianMTModel.from_pretrained(self.model_name	)
      self.assertEqual(model.config.decoder_start_token_id ,				model.config.pad_token_id	)
      return model







   def    _A				(	self ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   def    _A				(	self ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.tokenizer(self.src_text ,				padding=_SCREAMING_SNAKE_CASE ,				return_tensors='np' ,				**_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.model.generate(
          model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				num_beams=2 ,				max_length=128 ,				).sequences
      __lowerCamelCase    		=					self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,				skip_special_tokens=_SCREAMING_SNAKE_CASE	)
      return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    "en"
   __UpperCAmelCase	:							Optional[Any]    =    "fr"
   __UpperCAmelCase	:							Dict    =    [
       "I am a small frog.",
       "Now I can forget the 100 words of german that I know.",
   ]
   __UpperCAmelCase	:							Any    =    [
       "Je suis une petite grenouille.",
       "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
   ]







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    "fr"
   __UpperCAmelCase	:							List[str]    =    "en"
   __UpperCAmelCase	:							str    =    [
       "Donnez moi le micro.",
       "Tom et Mary étaient assis à une table.",  # Accents
   ]
   __UpperCAmelCase	:							Optional[int]    =    [
       "Give me the microphone.",
       "Tom and Mary were sitting at a table.",
   ]







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



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







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    "en"
   __UpperCAmelCase	:							str    =    "de"
   __UpperCAmelCase	:							List[str]    =    [
       "I am a small frog.",
       "Now I can forget the 100 words of german that I know.",
       "Tom asked his teacher for advice.",
       "That's how I would do it.",
       "Tom really admired Mary's courage.",
       "Turn around and close your eyes.",
   ]
   __UpperCAmelCase	:							List[Any]    =    [
       "Ich bin ein kleiner Frosch.",
       "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
       "Tom bat seinen Lehrer um Rat.",
       "So würde ich das machen.",
       "Tom bewunderte Marias Mut wirklich.",
       "Drehen Sie sich um und schließen Sie die Augen.",
   ]







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    "en"
   __UpperCAmelCase	:							Tuple    =    "zh"
   __UpperCAmelCase	:							Dict    =    ["My name is Wolfgang and I live in Berlin"]
   __UpperCAmelCase	:							List[Any]    =    ["我叫沃尔夫冈 我住在柏林"]







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[Any]    =    "ru"
   __UpperCAmelCase	:							Any    =    "fr"
   __UpperCAmelCase	:							List[str]    =    ["Он показал мне рукопись своей новой пьесы."]
   __UpperCAmelCase	:							Optional[int]    =    ["Il m'a montré le manuscrit de sa nouvelle pièce."]







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



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







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self._assert_generated_batch_equal_expected()










from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__SCREAMING_SNAKE_CASE	:							str           =  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Any           =  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							str           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

