




'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a  (	__a ):
       __a				:     Optional[int]   				=					["""image_processor""", """tokenizer"""]
       __a				:     str   				=					"""CLIPImageProcessor"""
       __a				:     Optional[Any]   				=					("""CLIPTokenizer""", """CLIPTokenizerFast""")
       def __init__(	self						:	Optional[int]   ,				lowercase						:	int=None   ,				lowercase						:	Tuple=None   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''   ,				lowercase   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

           super().__init__(lowercase   ,				lowercase				)
       def __call__(	self						:	int   ,				lowercase						:	List[str]=None   ,				lowercase						:	Dict=None   ,				lowercase						:	Tuple=None   ,				**lowercase						:	int				):



           '''simple docstring'''

           if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Any   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	Any   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				lowercase   ,				)
           return self.image_processor_class







       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,				lowercase   ,				)
           return self.image_processor








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






