
"""simple docstring"""


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     '''owlvit_text_model'''


						def __init__(			self  :   Optional[Any]	,A_  :   List[str]=4_9408	,A_  :   Optional[int]=512	,A_  :   Dict=2048	,A_  :   List[str]=12	,A_  :   Optional[Any]=8	,A_  :   List[Any]=16	,A_  :   List[str]="quick_gelu"	,A_  :   int=1e-5	,A_  :   int=0.0	,A_  :   List[str]=0.02	,A_  :   Tuple=1.0	,A_  :   Union[str, Any]=0	,A_  :   Tuple=4_9406	,A_  :   Optional[int]=4_9407	,**A_  :   int	,)  -> Union[str, Any]:
							super().__init__(pad_token_id=A_	,bos_token_id=A_	,eos_token_id=A_	,**A_   )

							A     = vocab_size
							A     = hidden_size
							A     = intermediate_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = max_position_embeddings
							A     = hidden_act
							A     = layer_norm_eps
							A     = attention_dropout
							A     = initializer_range
							A     = initializer_factor




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Dict	,A_  :   Union[str, os.PathLike]	,**A_  :   List[Any]   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							# get the text config dict if we are loading from OwlViTConfig
							if config_dict.get('model_type'   ) == "owlvit":
								A     = config_dict['text_config']

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[Any]   =     '''owlvit_vision_model'''


						def __init__(			self  :   int	,A_  :   List[str]=768	,A_  :   Dict=3072	,A_  :   List[str]=12	,A_  :   int=12	,A_  :   Optional[Any]=3	,A_  :   Optional[int]=768	,A_  :   List[str]=32	,A_  :   Union[str, Any]="quick_gelu"	,A_  :   Optional[int]=1e-5	,A_  :   Optional[Any]=0.0	,A_  :   Tuple=0.02	,A_  :   Optional[int]=1.0	,**A_  :   List[Any]	,)  -> Optional[int]:
							super().__init__(**A_   )

							A     = hidden_size
							A     = intermediate_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = num_channels
							A     = image_size
							A     = patch_size
							A     = hidden_act
							A     = layer_norm_eps
							A     = attention_dropout
							A     = initializer_range
							A     = initializer_factor




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   str	,A_  :   Union[str, os.PathLike]	,**A_  :   Any   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							# get the vision config dict if we are loading from OwlViTConfig
							if config_dict.get('model_type'   ) == "owlvit":
								A     = config_dict['vision_config']

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''owlvit'''
						_lowerCamelCase:     List[str]   =     True


						def __init__(			self  :   List[str]	,A_  :   int=None	,A_  :   str=None	,A_  :   Tuple=512	,A_  :   int=2.65_92	,A_  :   Optional[Any]=True	,**A_  :   Any	,)  -> Any:
							super().__init__(**A_   )

							if text_config is None:
								A     = {}
								logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'   )

							if vision_config is None:
								A     = {}
								logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'   )

							A     = OwlViTTextConfig(**A_   )
							A     = OwlViTVisionConfig(**A_   )

							A     = projection_dim
							A     = logit_scale_init_value
							A     = return_dict
							A     = 1.0


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Union[str, Any]	,A_  :   Union[str, os.PathLike]	,**A_  :   str   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   Dict	,A_  :   Dict	,**A_  :   List[Any]   )  -> Optional[int]:
							A     = {}
							A     = text_config
							A     = vision_config

							return cls.from_dict(A_	,**A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = copy.deepcopy(self.__dict__   )
							A     = self.text_config.to_dict()
							A     = self.vision_config.to_dict()
							A     = self.__class__.model_type
							return output







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'sequence'}),
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('attention_mask', {0: 'batch', 1: 'sequence'}),
							    ]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('logits_per_image', {0: 'batch'}),
							        ('logits_per_text', {0: 'batch'}),
							        ('text_embeds', {0: 'batch'}),
							        ('image_embeds', {0: 'batch'}),
							    ]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> float:
							return 1e-4


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   "ProcessorMixin"	,A_  :   int = -1	,A_  :   int = -1	,A_  :   Optional["TensorType"] = None	,)  -> Mapping[str, Any]:
							A     = super().generate_dummy_inputs(
							    processor.tokenizer	,batch_size=A_	,seq_length=A_	,framework=A_   )
							A     = super().generate_dummy_inputs(
							    processor.image_processor	,batch_size=A_	,framework=A_   )
							return {**text_input_dict, **image_input_dict}




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> int:
							return 14

"""simple docstring"""


from typing import List, Optional, Union

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







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							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 and not self.image_processor.is_vqa:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

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

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




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