
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__           =	{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase__           =	["""LxmertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase__           =	[
           """LxmertEncoder""",
           """LxmertForPreTraining""",
           """LxmertForQuestionAnswering""",
           """LxmertModel""",
           """LxmertPreTrainedModel""",
           """LxmertVisualFeatureEncoder""",
           """LxmertXLayer""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase__           =	[
           """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLxmertForPreTraining""",
           """TFLxmertMainLayer""",
           """TFLxmertModel""",
           """TFLxmertPreTrainedModel""",
           """TFLxmertVisualFeatureEncoder""",
       ]


if TYPE_CHECKING:
       from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
       from .tokenization_lxmert import LxmertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_lxmert_fast import LxmertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lxmert import (
                  LxmertEncoder,
                  LxmertForPreTraining,
                  LxmertForQuestionAnswering,
                  LxmertModel,
                  LxmertPreTrainedModel,
                  LxmertVisualFeatureEncoder,
                  LxmertXLayer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_lxmert import (
                  TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLxmertForPreTraining,
                  TFLxmertMainLayer,
                  TFLxmertModel,
                  TFLxmertPreTrainedModel,
                  TFLxmertVisualFeatureEncoder,
              )

else:
       import sys

       lowerCamelCase__           =	_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 				A__ (			unittest.TestCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=4 ,	):
			__lowerCAmelCase    :		List[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		List[str]		    =   seq_length
			__lowerCAmelCase    :		str		    =   is_training
			__lowerCAmelCase    :		Optional[Any]		    =   use_attention_mask
			__lowerCAmelCase    :		Union[str, Any]		    =   use_token_type_ids
			__lowerCAmelCase    :		Dict		    =   use_labels
			__lowerCAmelCase    :		Union[str, Any]		    =   vocab_size
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   num_hidden_layers
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   intermediate_size
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Union[str, Any]		    =   max_position_embeddings
			__lowerCAmelCase    :		Any		    =   type_vocab_size
			__lowerCAmelCase    :		Tuple		    =   type_sequence_label_size
			__lowerCAmelCase    :		Union[str, Any]		    =   initializer_range
			__lowerCAmelCase    :		int		    =   num_choices

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_attention_mask:
				__lowerCAmelCase    :		str		    =   random_attention_mask([self.batch_size, self.seq_length]				)

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

			__lowerCAmelCase    :		str		    =   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=_SCREAMING_SNAKE_CASE ,	initializer_range=self.initializer_range ,	)

			return config, input_ids, token_type_ids, attention_mask

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   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				):
			__lowerCAmelCase    :		Optional[int]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs

			__lowerCAmelCase    :		int		    =   True
			__lowerCAmelCase    :		Any		    =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
			__lowerCAmelCase    :		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 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[Any]            =			True

		A_		: Optional[Any]            =			(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   FlaxRobertaModelTester(self				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		str		    =   model_class_name.from_pretrained('roberta-base' ,	from_pt=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)