


"""simple docstring"""







import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return x + 2




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				"x = 3"
						__SCREAMING_SNAKE_CASE						   =				{}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						assert result == 3
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3}   )

						__SCREAMING_SNAKE_CASE						   =				"x = y"
						__SCREAMING_SNAKE_CASE						   =				{"y": 5}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 5, "y": 5}   )




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				"y = add_two(x)"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"add_two": add_two}			,			state=UpperCAmelCase__   )
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "y": 5}   )

						# Won't work without the tool
						with CaptureStdout() as out:
											__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						assert result is None
						assert "tried to execute add_two" in out.out




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				"x = 3"
						__SCREAMING_SNAKE_CASE						   =				{}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						assert result == 3
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3}   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				"test_dict = {'x': x, 'y': add_two(x)}"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"add_two": add_two}			,			state=UpperCAmelCase__   )
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "y": 5}   )
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "test_dict": {"x": 3, "y": 5}}   )




	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				"x = 3\ny = 5"
						__SCREAMING_SNAKE_CASE						   =				{}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "y": 5}   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				"text = f'This is x: {x}.'"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						# evaluate returns the value of the last assignment.
						assert result == "This is x: 3."
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "text": "This is x: 3."}   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				"if x <= 3:\n    y = 2\nelse:\n    y = 5"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						# evaluate returns the value of the last assignment.
						assert result == 2
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "y": 2}   )

						__SCREAMING_SNAKE_CASE						   =				{"x": 8}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 8, "y": 5}   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				"test_list = [x, add_two(x)]"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"add_two": add_two}			,			state=UpperCAmelCase__   )
						self.assertListEqual(UpperCAmelCase__			,			[3, 5]   )
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "test_list": [3, 5]}   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				"y = x"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{}			,			state=UpperCAmelCase__   )
						assert result == 3
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "y": 3}   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				"test_list = [x, add_two(x)]\ntest_list[1]"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"add_two": add_two}			,			state=UpperCAmelCase__   )
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "test_list": [3, 5]}   )

						__SCREAMING_SNAKE_CASE						   =				"test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
						__SCREAMING_SNAKE_CASE						   =				{"x": 3}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"add_two": add_two}			,			state=UpperCAmelCase__   )
						assert result == 5
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 3, "test_dict": {"x": 3, "y": 5}}   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				"x = 0\nfor i in range(3):\n    x = i"
						__SCREAMING_SNAKE_CASE						   =				{}
						__SCREAMING_SNAKE_CASE						   =				evaluate(UpperCAmelCase__			,			{"range": range}			,			state=UpperCAmelCase__   )
						assert result == 2
						self.assertDictEqual(UpperCAmelCase__			,			{"x": 2, "i": 2}   )




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
