



'''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	):
    """simple docstring"""







    def __init__(    self					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     Dict=13 ,     snake_case_					:     str=7 ,     snake_case_					:     List[str]=True ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     List[Any]=True ,     snake_case_					:     Union[str, Any]=True ,     snake_case_					:     Dict=99 ,     snake_case_					:     Optional[int]=32 ,     snake_case_					:     Optional[Any]=5 ,     snake_case_					:     Dict=4 ,     snake_case_					:     Optional[Any]=37 ,     snake_case_					:     str="gelu" ,     snake_case_					:     Optional[Any]=0.1 ,     snake_case_					:     Union[str, Any]=0.1 ,     snake_case_					:     List[str]=512 ,     snake_case_					:     Union[str, Any]=16 ,     snake_case_					:     Optional[Any]=2 ,     snake_case_					:     Any=0.02 ,     snake_case_					:     Dict=4 ,     ):
          snake_case__  :    Optional[Any]    						=      parent
          snake_case__  :    str    						=      batch_size
          snake_case__  :    Any    						=      seq_length
          snake_case__  :    Dict    						=      is_training
          snake_case__  :    int    						=      use_attention_mask
          snake_case__  :    Any    						=      use_token_type_ids
          snake_case__  :    List[Any]    						=      use_labels
          snake_case__  :    List[Any]    						=      vocab_size
          snake_case__  :    int    						=      hidden_size
          snake_case__  :    List[str]    						=      num_hidden_layers
          snake_case__  :    str    						=      num_attention_heads
          snake_case__  :    List[str]    						=      intermediate_size
          snake_case__  :    List[str]    						=      hidden_act
          snake_case__  :    Any    						=      hidden_dropout_prob
          snake_case__  :    List[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    List[Any]    						=      max_position_embeddings
          snake_case__  :    Any    						=      type_vocab_size
          snake_case__  :    Dict    						=      type_sequence_label_size
          snake_case__  :    Optional[Any]    						=      initializer_range
          snake_case__  :    Tuple    						=      num_choices




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Tuple    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

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

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

          snake_case__  :    Optional[Any]    						=      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 lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      self.prepare_config_and_inputs()
          snake_case__						,				snake_case__						,				snake_case__						,				snake_case__  :    List[str]    						=      config_and_inputs
          snake_case__  :    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]						):
          snake_case__  :    Union[str, Any]    						=      self.prepare_config_and_inputs()
          snake_case__						,				snake_case__						,				snake_case__						,				snake_case__  :    Dict    						=      config_and_inputs

          snake_case__  :    List[str]    						=      True
          snake_case__  :    Union[str, Any]    						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
          snake_case__  :    Dict    						=      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	):
    """simple docstring"""







    lowercase					=		True

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




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Dict    						=      FlaxRobertaModelTester(self						)




    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          for model_class_name in self.all_model_classes:
                snake_case__  :    Any    						=      model_class_name.from_pretrained("""roberta-base""" ,     from_pt=snake_case_						)
                snake_case__  :    Optional[int]    						=      model(np.ones((1, 1)						)						)
                self.assertIsNotNone(snake_case_						)





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
