






import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class     _SCREAMING_SNAKE_CASE       :
	snake_case__							:     Optional[int]     	=  None





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[str]          =	self.feature_extraction_class(**self.feat_extract_dict	)
		UpperCamelCase     :List[str]          =	json.loads(feat_extract.to_json_string()	)
		for key, value in self.feat_extract_dict.items():
			self.assertEqual(obj[key] ,  __lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Optional[Any]          =	self.feature_extraction_class(**self.feat_extract_dict	)

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCamelCase     :Optional[Any]          =	os.path.join(__lowerCamelCase ,  """feat_extract.json"""	)
			feat_extract_first.to_json_file(__lowerCamelCase	)
			UpperCamelCase     :int          =	self.feature_extraction_class.from_json_file(__lowerCamelCase	)

		self.assertEqual(feat_extract_second.to_dict() ,  feat_extract_first.to_dict()	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[Any]          =	self.feature_extraction_class(**self.feat_extract_dict	)

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCamelCase     :int          =	feat_extract_first.save_pretrained(__lowerCamelCase	)[0]
			check_json_file_has_correct_format(__lowerCamelCase	)
			UpperCamelCase     :str          =	self.feature_extraction_class.from_pretrained(__lowerCamelCase	)

		self.assertEqual(feat_extract_second.to_dict() ,  feat_extract_first.to_dict()	)





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.feature_extraction_class()
		self.assertIsNotNone(__lowerCamelCase	)








from typing import TYPE_CHECKING

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


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
