




"""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						:	int     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	int=13     ,			snake_case_						:	Optional[Any]=7     ,			snake_case_						:	int=True     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	Optional[int]=99     ,			snake_case_						:	Union[str, Any]=32     ,			snake_case_						:	List[Any]=5     ,			snake_case_						:	Optional[int]=4     ,			snake_case_						:	Optional[int]=37     ,			snake_case_						:	Tuple="gelu"     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	Tuple=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=16     ,			snake_case_						:	Dict=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Tuple=4     ,			)				->							Optional[int]:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    seq_length
     A__	=    is_training
     A__	=    use_attention_mask
     A__	=    use_token_type_ids
     A__	=    use_labels
     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    num_choices




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[str]:




     '''simple docstring'''

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

     A__	=    None
     if self.use_attention_mask:
      A__	=    random_attention_mask([self.batch_size, self.seq_length]				)

     A__	=    None
     if self.use_token_type_ids:
      A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size				)

     A__	=    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=snake_case_     ,			initializer_range=self.initializer_range     ,			)

     return config, input_ids, token_type_ids, attention_mask




    def 							__magic_name__							(   self						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     A__,				A__,				A__,				A__	=    config_and_inputs
     A__	=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
     return config, inputs_dict




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     A__,				A__,				A__,				A__	=    config_and_inputs

     A__	=    True
     A__	=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     A__	=    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							):
    lowercase__						 =				True

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




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




     '''simple docstring'''

     A__	=    FlaxRobertaModelTester(self				)




    @slow
    def 							__magic_name__							(   self						:	str				)				->							Optional[Any]:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__	=    model_class_name.from_pretrained("roberta-base"     ,			from_pt=snake_case_				)
      A__	=    model(np.ones((1, 1)				)				)
      self.assertIsNotNone(snake_case_				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





