






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Union[str, Any]     	=  ["""image_processor""", """tokenizer"""]
	snake_case__							:     Optional[Any]     	=  """FlavaImageProcessor"""
	snake_case__							:     List[str]     	=  ("""BertTokenizer""", """BertTokenizerFast""")





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Dict=None ,  **__lowerCamelCase				:					Tuple	):
		UpperCamelCase     :Union[str, Any]          =	None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead.""" ,  __lowerCamelCase ,  )
			UpperCamelCase     :List[Any]          =	kwargs.pop("""feature_extractor"""	)

		UpperCamelCase     :int          =	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__(__lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	self.image_processor





	def __call__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Optional[ImageInput] = None ,  __lowerCamelCase				:					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Union[bool, str, PaddingStrategy] = False ,  __lowerCamelCase				:					Union[bool, str, TruncationStrategy] = False ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					int = 0 ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Union[str, TensorType]] = None ,  **__lowerCamelCase				:					int ,  ):

		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     :List[str]          =	self.tokenizer(
			    text=__lowerCamelCase ,  add_special_tokens=__lowerCamelCase ,  padding=__lowerCamelCase ,  truncation=__lowerCamelCase ,  max_length=__lowerCamelCase ,  stride=__lowerCamelCase ,  pad_to_multiple_of=__lowerCamelCase ,  return_token_type_ids=__lowerCamelCase ,  return_attention_mask=__lowerCamelCase ,  return_overflowing_tokens=__lowerCamelCase ,  return_special_tokens_mask=__lowerCamelCase ,  return_offsets_mapping=__lowerCamelCase ,  return_length=__lowerCamelCase ,  verbose=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase ,  )
		if images is not None:
			UpperCamelCase     :Dict          =	self.image_processor(
			    __lowerCamelCase ,  return_image_mask=__lowerCamelCase ,  return_codebook_pixels=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase ,  )

		if text is not None and images is not None:
			encoding.update(__lowerCamelCase	)
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**__lowerCamelCase	) ,  tensor_type=__lowerCamelCase	)





	def 						_A (		self				:					Any ,  *__lowerCamelCase				:					int ,  **__lowerCamelCase				:					Optional[int]	):
		return self.tokenizer.batch_decode(*__lowerCamelCase ,  **__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any] ,  *__lowerCamelCase				:					Dict ,  **__lowerCamelCase				:					List[str]	):
		return self.tokenizer.decode(*__lowerCamelCase ,  **__lowerCamelCase	)





	@property
	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Optional[Any]          =	self.tokenizer.model_input_names
		UpperCamelCase     :Any          =	self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def 						_A (		self				:					Optional[Any]	):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,  __lowerCamelCase ,  )
		return self.image_processor_class





	@property
	def 						_A (		self				:					List[Any]	):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,  __lowerCamelCase ,  )
		return self.image_processor







"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)