"""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


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """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 							a					(		_lowerCAmelCase ):
    _snake_case  :	Any  				=				"owlvit_text_model"


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

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


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Tuple ,					__lowerCAmelCase						:	Union[str, Any] ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__       )

      _UpperCAmelCase			,					_UpperCAmelCase    			=   cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )

      # get the text config dict if we are loading from OwlViTConfig
      if config_dict.get("""model_type"""       ) == "owlvit":
        _UpperCAmelCase    			=   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(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )



class 							a					(		_lowerCAmelCase ):
    _snake_case  :	str  				=				"owlvit_vision_model"


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

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


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Any ,					__lowerCAmelCase						:	Union[str, Any] ,					**__lowerCAmelCase						:	Any       ):
      cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__       )

      _UpperCAmelCase			,					_UpperCAmelCase    			=   cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )

      # get the vision config dict if we are loading from OwlViTConfig
      if config_dict.get("""model_type"""       ) == "owlvit":
        _UpperCAmelCase    			=   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(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )



class 							a					(		_lowerCAmelCase ):
    _snake_case  :	Optional[Any]  				=				"owlvit"
    _snake_case  :	Dict  				=				True


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	Union[str, Any]=None ,					__lowerCAmelCase						:	Optional[Any]=None ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	List[str]=2.6_592 ,					__lowerCAmelCase						:	Tuple=True ,					**__lowerCAmelCase						:	Optional[int] ,					):
      super().__init__(**SCREAMING_SNAKE_CASE__       )

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

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

      _UpperCAmelCase    			=   OwlViTTextConfig(**SCREAMING_SNAKE_CASE__       )
      _UpperCAmelCase    			=   OwlViTVisionConfig(**SCREAMING_SNAKE_CASE__       )

      _UpperCAmelCase    			=   projection_dim
      _UpperCAmelCase    			=   logit_scale_init_value
      _UpperCAmelCase    			=   return_dict
      _UpperCAmelCase    			=   1.0


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					**__lowerCAmelCase						:	List[str]       ):
      cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__       )

      _UpperCAmelCase			,					_UpperCAmelCase    			=   cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )

      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(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )


    @classmethod
    def   lowerCAmelCase_	(       cls						:	str ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any] ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   {}
      _UpperCAmelCase    			=   text_config
      _UpperCAmelCase    			=   vision_config

      return cls.from_dict(SCREAMING_SNAKE_CASE__ ,					**SCREAMING_SNAKE_CASE__       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   copy.deepcopy(self.__dict__       )
      _UpperCAmelCase    			=   self.text_config.to_dict()
      _UpperCAmelCase    			=   self.vision_config.to_dict()
      _UpperCAmelCase    			=   self.__class__.model_type
      return output



class 							a					(		_lowerCAmelCase ):


    @property
    def   lowerCAmelCase_	(       self						:	Dict       ):
      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   lowerCAmelCase_	(       self						:	List[Any]       ):
      return OrderedDict(
          [
              ("""logits_per_image""", {0: """batch"""}),
              ("""logits_per_text""", {0: """batch"""}),
              ("""text_embeds""", {0: """batch"""}),
              ("""image_embeds""", {0: """batch"""}),
          ]       )


    @property
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      return 1e-4


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	str = -1 ,					__lowerCAmelCase						:	List[Any] = -1 ,					__lowerCAmelCase						:	Tuple = None ,					):
      _UpperCAmelCase    			=   super().generate_dummy_inputs(
          processor.tokenizer ,					batch_size=SCREAMING_SNAKE_CASE__ ,					seq_length=SCREAMING_SNAKE_CASE__ ,					framework=SCREAMING_SNAKE_CASE__       )
      _UpperCAmelCase    			=   super().generate_dummy_inputs(
          processor.image_processor ,					batch_size=SCREAMING_SNAKE_CASE__ ,					framework=SCREAMING_SNAKE_CASE__       )
      return {**text_input_dict, **image_input_dict}


    @property
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      return 14





"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	hidden_size=SCREAMING_SNAKE_CASE__						,	num_hidden_layers=SCREAMING_SNAKE_CASE__						,	num_attention_heads=SCREAMING_SNAKE_CASE__						,	intermediate_size=SCREAMING_SNAKE_CASE__						,	hidden_act=SCREAMING_SNAKE_CASE__						,	hidden_dropout_prob=SCREAMING_SNAKE_CASE__						,	attention_probs_dropout_prob=SCREAMING_SNAKE_CASE__						,	max_position_embeddings=SCREAMING_SNAKE_CASE__						,	type_vocab_size=SCREAMING_SNAKE_CASE__						,	initializer_range=SCREAMING_SNAKE_CASE__						,	layer_norm_eps=SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				# The order of inputs is different for question answering and sequence classification
				if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]		)
				else:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels"}),
					    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-5


			@property
			def   snake_case__	(      self		)      ->	int:
				return 12







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				setattr(processor.image_processor						,	"apply_ocr"						,	SCREAMING_SNAKE_CASE__		)

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch						,	num_token_to_add=0		)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				A__											=					processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
				# Generate dummy inputs according to compute batch and sequence
				A__											=					[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				A__											=					[[[48, 84, 73, 128]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




