
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     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""BlipImageProcessor"""
			__UpperCamelCase :  Union[str, Any]    =	"""AutoTokenizer"""

			def __init__(    self	:    int  ,		snake_case_	:    str  ,		snake_case_	:    Optional[Any] ):
							UpperCamelCase_:							Optional[Any]		=  False
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							Dict		=  self.image_processor

			def __call__(    self	:    Tuple  ,		snake_case_	:    ImageInput = None  ,		snake_case_	:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,		snake_case_	:    bool = True  ,		snake_case_	:    Union[bool, str, PaddingStrategy] = False  ,		snake_case_	:    Union[bool, str, TruncationStrategy] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    int = 0  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    bool = False  ,		snake_case_	:    bool = False  ,		snake_case_	:    bool = False  ,		snake_case_	:    bool = False  ,		snake_case_	:    bool = False  ,		snake_case_	:    bool = True  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		**snake_case_	:    List[Any]  ,		):
							if images is None and text is None:
											raise ValueError("""You have to specify either images or text.""" )

							# Get only text
							if images is None:
											UpperCamelCase_:							int		=  self.tokenizer
											UpperCamelCase_:							Union[str, Any]		=  self.tokenizer(
											    text=snake_case_  ,		add_special_tokens=snake_case_  ,		padding=snake_case_  ,		truncation=snake_case_  ,		max_length=snake_case_  ,		stride=snake_case_  ,		pad_to_multiple_of=snake_case_  ,		return_attention_mask=snake_case_  ,		return_overflowing_tokens=snake_case_  ,		return_special_tokens_mask=snake_case_  ,		return_offsets_mapping=snake_case_  ,		return_token_type_ids=snake_case_  ,		return_length=snake_case_  ,		verbose=snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_  ,		)
											return text_encoding

							# add pixel_values
							UpperCamelCase_:							Dict		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_ )

							if text is not None:
											UpperCamelCase_:							Dict		=  self.tokenizer(
											    text=snake_case_  ,		add_special_tokens=snake_case_  ,		padding=snake_case_  ,		truncation=snake_case_  ,		max_length=snake_case_  ,		stride=snake_case_  ,		pad_to_multiple_of=snake_case_  ,		return_attention_mask=snake_case_  ,		return_overflowing_tokens=snake_case_  ,		return_special_tokens_mask=snake_case_  ,		return_offsets_mapping=snake_case_  ,		return_token_type_ids=snake_case_  ,		return_length=snake_case_  ,		verbose=snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_  ,		)
							else:
											UpperCamelCase_:							Optional[int]		=  None

							if text_encoding is not None:
											encoding_image_processor.update(snake_case_ )

							return encoding_image_processor

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Optional[int] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		*snake_case_	:    Optional[int]  ,		**snake_case_	:    List[Any] ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							str		=  self.tokenizer.model_input_names
							UpperCamelCase_:							Dict		=  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )








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


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Optional[int] 				=					{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""ctrl"""
			__UpperCamelCase :  Dict    =	["""past_key_values"""]
			__UpperCamelCase :  List[str]    =	{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}

			def __init__(    self	:    Dict  ,		snake_case_	:    Any=24_6534  ,		snake_case_	:    Dict=256  ,		snake_case_	:    str=1280  ,		snake_case_	:    Optional[int]=8192  ,		snake_case_	:    Union[str, Any]=48  ,		snake_case_	:    Any=16  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    Optional[Any]=0.02  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  vocab_size
							UpperCamelCase_:							Union[str, Any]		=  n_positions
							UpperCamelCase_:							Optional[int]		=  n_embd
							UpperCamelCase_:							int		=  n_layer
							UpperCamelCase_:							str		=  n_head
							UpperCamelCase_:							Optional[int]		=  dff
							UpperCamelCase_:							Optional[Any]		=  resid_pdrop
							UpperCamelCase_:							Union[str, Any]		=  embd_pdrop
							UpperCamelCase_:							List[str]		=  layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]		=  initializer_range

							UpperCamelCase_:							Optional[Any]		=  use_cache

							super().__init__(**snake_case_ )






