






from typing import TYPE_CHECKING

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


__lowerCamelCase :      Optional[Any]										=				{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Union[str, Any]										=				["""DebertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[str]										=				[
	    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """DebertaForMaskedLM""",
	    """DebertaForQuestionAnswering""",
	    """DebertaForSequenceClassification""",
	    """DebertaForTokenClassification""",
	    """DebertaModel""",
	    """DebertaPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Union[str, Any]										=				[
	    """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFDebertaForMaskedLM""",
	    """TFDebertaForQuestionAnswering""",
	    """TFDebertaForSequenceClassification""",
	    """TFDebertaForTokenClassification""",
	    """TFDebertaModel""",
	    """TFDebertaPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
	from .tokenization_deberta import DebertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_fast import DebertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta import (
		    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		    DebertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta import (
		    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		    TFDebertaPreTrainedModel,
		)


else:
	import sys

	__lowerCamelCase :      Dict										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  SMALL_MODEL_IDENTIFIER
						snake_case__			:				Any       =  "pt"
						snake_case__			:				Any       =  "tf"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				int       =  AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__A	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  TFAutoModel.from_pretrained(self.test_model							,		from_pt=__A	)
						model_tf.save_pretrained(__A	)



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  "mock_framework"

						# Framework provided - return whatever the user provides
						snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model							,		__A	)
						self.assertEqual(__A							,		__A	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				Optional[int]       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				int       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)



					def 	_lowercase					( self					:  Dict	):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				List[str]       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				Tuple       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
							with self.assertRaises(__A	):
								snake_case__			:				int       =  FeaturesManager.determine_framework(__A	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	):
							snake_case__			:				List[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						snake_case__			:				Tuple       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				int       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Both in environment -> use PyTorch
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[int]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# Both not in environment -> raise error
						snake_case__			:				List[str]       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[Any]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							with self.assertRaises(__A	):
								snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)



