"""simple docstring"""







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.electra.modeling_flax_electra import (
	    FlaxElectraForCausalLM,
	    FlaxElectraForMaskedLM,
	    FlaxElectraForMultipleChoice,
	    FlaxElectraForPreTraining,
	    FlaxElectraForQuestionAnswering,
	    FlaxElectraForSequenceClassification,
	    FlaxElectraForTokenClassification,
	    FlaxElectraModel,
	)

class 						UpperCamelCase     (       unittest.TestCase		):



				def __init__(self							: List[str]      ,    _A							: Optional[int]      ,    _A							: List[Any]=13      ,    _A							: int=7      ,    _A							: List[str]=True      ,    _A							: Union[str, Any]=True      ,    _A							: List[Any]=True      ,    _A							: Union[str, Any]=True      ,    _A							: Dict=99      ,    _A							: Dict=24      ,    _A							: str=32      ,    _A							: Optional[int]=5      ,    _A							: Dict=4      ,    _A							: Union[str, Any]=37      ,    _A							: Tuple="gelu"      ,    _A							: Union[str, Any]=0.1      ,    _A							: Optional[int]=0.1      ,    _A							: str=5_12      ,    _A							: Any=16      ,    _A							: Optional[Any]=2      ,    _A							: Union[str, Any]=0.02      ,    _A							: str=4      ,    )       ->      Any:
					__snake_case     :				Any 						=					parent
					__snake_case     :				Any 						=					batch_size
					__snake_case     :				Tuple 						=					seq_length
					__snake_case     :				Any 						=					is_training
					__snake_case     :				Any 						=					use_attention_mask
					__snake_case     :				Dict 						=					use_token_type_ids
					__snake_case     :				Any 						=					use_labels
					__snake_case     :				Tuple 						=					vocab_size
					__snake_case     :				Optional[Any] 						=					hidden_size
					__snake_case     :				str 						=					embedding_size
					__snake_case     :				Tuple 						=					num_hidden_layers
					__snake_case     :				Tuple 						=					num_attention_heads
					__snake_case     :				Optional[int] 						=					intermediate_size
					__snake_case     :				Optional[Any] 						=					hidden_act
					__snake_case     :				str 						=					hidden_dropout_prob
					__snake_case     :				str 						=					attention_probs_dropout_prob
					__snake_case     :				Union[str, Any] 						=					max_position_embeddings
					__snake_case     :				Dict 						=					type_vocab_size
					__snake_case     :				Union[str, Any] 						=					type_sequence_label_size
					__snake_case     :				str 						=					initializer_range
					__snake_case     :				Dict 						=					num_choices



				def    _lowercase	(self							: Optional[Any])       ->      Optional[int]:
					__snake_case     :				Optional[Any] 						=					ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

					__snake_case     :				Any 						=					None
					if self.use_attention_mask:
						__snake_case     :				Optional[Any] 						=					random_attention_mask([self.batch_size, self.seq_length])

					__snake_case     :				Optional[Any] 						=					None
					if self.use_token_type_ids:
						__snake_case     :				Optional[int] 						=					ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size)

					__snake_case     :				Any 						=					ElectraConfig(
					    vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    embedding_size=self.embedding_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      ,    initializer_range=self.initializer_range      ,    )

					return config, input_ids, token_type_ids, attention_mask






				def    _lowercase	(self							: List[str])       ->      List[str]:
					__snake_case     :				List[Any] 						=					self.prepare_config_and_inputs()
					__snake_case						,							__snake_case						,							__snake_case						,							__snake_case     :				Optional[Any] 						=					config_and_inputs
					__snake_case     :				Tuple 						=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
					return config, inputs_dict







@require_flax
class 						UpperCamelCase     (       lowercase ,      unittest.TestCase		):
				UpperCAmelCase							: Tuple       =						True

				UpperCAmelCase							: Any       =						(
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)



				def    _lowercase	(self							: Dict)       ->      Dict:
					__snake_case     :				Tuple 						=					FlaxElectraModelTester(self)






				@slow
				def    _lowercase	(self							: Optional[int])       ->      int:
					for model_class_name in self.all_model_classes:
						if model_class_name == FlaxElectraForMaskedLM:
							__snake_case     :				Tuple 						=					model_class_name.from_pretrained('google/electra-small-generator')
						else:
							__snake_case     :				str 						=					model_class_name.from_pretrained('google/electra-small-discriminator')
						__snake_case     :				List[Any] 						=					model(np.ones((1, 1)))
						self.assertIsNotNone(_A)






"""simple docstring"""







from typing import TYPE_CHECKING

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


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





