





import warnings

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

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		['''image_processor''', '''tokenizer''']
	__UpperCamelCase   =		'''ViTImageProcessor'''
	__UpperCamelCase   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')




	def __init__(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Any=None					,      __lowerCamelCase							:     Dict=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     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					,      )
					__lowercase											=     kwargs.pop('feature_extractor'   )

			__lowercase											=     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   )




	def __call__(						self							:     List[Any]					,      __lowerCamelCase							:     Dict=None					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     Dict=None					,      **__lowerCamelCase							:     Tuple   )       -> Dict:

			'''simple docstring'''




			if text is None and visual_prompt is None and images is None:
					raise ValueError('You have to specify either text, visual prompt or images.'   )

			if text is not None and visual_prompt is not None:
					raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'   )

			if text is not None:
					__lowercase											=     self.tokenizer(__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase   )

			if visual_prompt is not None:
					__lowercase											=     self.image_processor(__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase   )

			if images is not None:
					__lowercase											=     self.image_processor(__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase   )

			if visual_prompt is not None and images is not None:
					__lowercase											=     {
					    'pixel_values': image_features.pixel_values,
					    'conditional_pixel_values': prompt_features.pixel_values,
					}
					return encoding
			elif text is not None and images is not None:
					__lowercase											=     image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			elif visual_prompt is not None:
					__lowercase											=     {
					    'conditional_pixel_values': prompt_features.pixel_values,
					}
					return encoding
			else:
					return BatchEncoding(data=dict(**__lowerCamelCase   )					,      tensor_type=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     List[Any]					,      *__lowerCamelCase							:     Dict					,      **__lowerCamelCase							:     Tuple   )       -> Optional[Any]:

			'''simple docstring'''




			return self.tokenizer.batch_decode(*__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict					,      *__lowerCamelCase							:     Any					,      **__lowerCamelCase							:     int   )       -> List[Any]:

			'''simple docstring'''




			return self.tokenizer.decode(*__lowerCamelCase					,      **__lowerCamelCase   )




	@property
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[int]:

			'''simple docstring'''




			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    UpperCAmelCase			(						self							:     str   )       -> Optional[Any]:

			'''simple docstring'''




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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''rwkv'''
	__UpperCamelCase   =		{'''max_position_embeddings''': '''context_length'''}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Tuple=50_277					,      __lowerCamelCase							:     List[str]=1_024					,      __lowerCamelCase							:     List[Any]=4_096					,      __lowerCamelCase							:     Tuple=32					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[Any]=1E-5					,      __lowerCamelCase							:     Tuple=0					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Dict=6					,      __lowerCamelCase							:     List[Any]=False					,      __lowerCamelCase							:     Tuple=True					,      **__lowerCamelCase							:     Optional[int]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     context_length
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     attention_hidden_size if attention_hidden_size is not None else hidden_size
			__lowercase											=     intermediate_size if intermediate_size is not None else 4 * hidden_size
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     rescale_every
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

			super().__init__(
			    tie_word_embeddings=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase   )




