


from typing import TYPE_CHECKING

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


_lowerCAmelCase:						List[str]   				=       {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Any   				=       [
	    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RobertaForCausalLM',
	    'RobertaForMaskedLM',
	    'RobertaForMultipleChoice',
	    'RobertaForQuestionAnswering',
	    'RobertaForSequenceClassification',
	    'RobertaForTokenClassification',
	    'RobertaModel',
	    'RobertaPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Optional[int]   				=       [
	    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFRobertaForCausalLM',
	    'TFRobertaForMaskedLM',
	    'TFRobertaForMultipleChoice',
	    'TFRobertaForQuestionAnswering',
	    'TFRobertaForSequenceClassification',
	    'TFRobertaForTokenClassification',
	    'TFRobertaMainLayer',
	    'TFRobertaModel',
	    'TFRobertaPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Union[str, Any]   				=       [
	    'FlaxRobertaForCausalLM',
	    'FlaxRobertaForMaskedLM',
	    'FlaxRobertaForMultipleChoice',
	    'FlaxRobertaForQuestionAnswering',
	    'FlaxRobertaForSequenceClassification',
	    'FlaxRobertaForTokenClassification',
	    'FlaxRobertaModel',
	    'FlaxRobertaPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						Dict   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''


from unittest.mock import Mock, patch

from file_transfer.send_file import send_file



@patch("""socket.socket"""				)
@patch("""builtins.open"""				)
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict ,    UpperCamelCase__    :					Tuple				):




     """simple docstring"""


     a_     :							Dict     						=  Mock()
     a_     :							List[str]     						=  conn, Mock()
     a_     :							Optional[int]     						=  iter([1, None]				)
     a_     :							List[str]     						=  lambda UpperCamelCase__				: next(UpperCamelCase__				)

     # ===== invoke =====
     send_file(filename="""mytext.txt""" ,    testing=UpperCamelCase__				)

     # ===== ensurance =====
     sock.assert_called_once()
     sock.return_value.bind.assert_called_once()
     sock.return_value.listen.assert_called_once()
     sock.return_value.accept.assert_called_once()
     conn.recv.assert_called_once()

     file.return_value.__enter__.assert_called_once()
     file.return_value.__enter__.return_value.read.assert_called()

     conn.send.assert_called_once()
     conn.close.assert_called_once()
     sock.return_value.shutdown.assert_called_once()
     sock.return_value.close.assert_called_once()
