



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  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Optional[Any]=7				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     List[str]=True				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     Any=True				,		UpperCamelCase__      :     Any=99				,		UpperCamelCase__      :     Dict=32				,		UpperCamelCase__      :     Union[str, Any]=5				,		UpperCamelCase__      :     Any=4				,		UpperCamelCase__      :     Dict=37				,		UpperCamelCase__      :     Optional[int]="gelu"				,		UpperCamelCase__      :     List[str]=0.1				,		UpperCamelCase__      :     Tuple=0.1				,		UpperCamelCase__      :     Tuple=5_12				,		UpperCamelCase__      :     Dict=16				,		UpperCamelCase__      :     int=2				,		UpperCamelCase__      :     str=0.02				,		UpperCamelCase__      :     Dict=4				,		)				->					Optional[int]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







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

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

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

								UpperCAmelCase         =     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=UpperCamelCase__				,		initializer_range=self.initializer_range				,		)

								return config, input_ids, token_type_ids, attention_mask






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Any:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







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

								UpperCAmelCase         =     True
								UpperCAmelCase         =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
								UpperCAmelCase         =     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  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       List[str]          =True

		lowercase      :       List[str]          =(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxRobertaModelTester(self      )







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







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








from argparse import ArgumentParser

from .env import EnvironmentCommand

def    lowerCamelCase_() ->       Any:
						UpperCAmelCase         =     ArgumentParser("Diffusers CLI tool"    ,   usage="diffusers-cli <command> [<args>]"    )
						UpperCAmelCase         =     parser.add_subparsers(help="diffusers-cli command helpers"    )

						# Register commands
						EnvironmentCommand.register_subcommand(lowerCamelCase_    )

						# Let's go
						UpperCAmelCase         =     parser.parse_args()

						if not hasattr(lowerCamelCase_    ,   "func"    ):
												parser.print_help()
												exit(1    )

						# Run
						UpperCAmelCase         =     args.func(lowerCamelCase_    )
						service.run()


if __name__ == "__main__":
		main()



