




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_       :   List[Any]   =       {
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_       :   Optional[int]   =       [
     'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
     'M2M100ForConditionalGeneration',
     'M2M100Model',
     'M2M100PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
 from .tokenization_mam_aaa import MaMaaaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mam_aaa import (
      M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaMaaaForConditionalGeneration,
      MaMaaaModel,
      MaMaaaPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_       :   List[Any]   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2     )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )