


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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """trajectory_transformer"""
      __snake_case 	=       ["""past_key_values"""]
      __snake_case 	=       {
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }




      def __init__(    self:	int						,				a:	Dict=100						,				a:	int=5						,				a:	Union[str, Any]=1						,				a:	Optional[int]=1						,				a:	int=249						,				a:	Optional[int]=6						,				a:	str=17						,				a:	Optional[Any]=25						,				a:	int=4						,				a:	Union[str, Any]=4						,				a:	Tuple=128						,				a:	List[str]=0.1						,				a:	List[Any]=0.1						,				a:	Optional[Any]=0.1						,				a:	Any=0.0_0_0_6						,				a:	Optional[int]=512						,				a:	Any=0.0_2						,				a:	Tuple=1e-12						,				a:	Optional[int]=1						,				a:	List[str]=True						,				a:	Any=1						,				a:	Optional[Any]=5_0256						,				a:	Tuple=5_0256						,				**a:	str						,				):
       __lowerCamelCase		:							Tuple		 =						vocab_size
       __lowerCamelCase		:							Optional[int]		 =						action_weight
       __lowerCamelCase		:							Optional[Any]		 =						reward_weight
       __lowerCamelCase		:							Dict		 =						value_weight
       __lowerCamelCase		:							List[str]		 =						max_position_embeddings
       __lowerCamelCase		:							str		 =						block_size
       __lowerCamelCase		:							str		 =						action_dim
       __lowerCamelCase		:							Any		 =						observation_dim
       __lowerCamelCase		:							str		 =						transition_dim
       __lowerCamelCase		:							Optional[int]		 =						learning_rate
       __lowerCamelCase		:							Any		 =						n_layer
       __lowerCamelCase		:							Any		 =						n_head
       __lowerCamelCase		:							Tuple		 =						n_embd
       __lowerCamelCase		:							List[str]		 =						embd_pdrop
       __lowerCamelCase		:							List[Any]		 =						attn_pdrop
       __lowerCamelCase		:							Any		 =						resid_pdrop
       __lowerCamelCase		:							List[str]		 =						initializer_range
       __lowerCamelCase		:							Dict		 =						layer_norm_eps
       __lowerCamelCase		:							Dict		 =						kaiming_initializer_range
       __lowerCamelCase		:							List[Any]		 =						use_cache
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )





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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_text_model"""




      def __init__(    self:	Optional[int]						,				a:	Dict=4_9408						,				a:	Optional[Any]=512						,				a:	Dict=2048						,				a:	Optional[Any]=12						,				a:	Tuple=8						,				a:	Union[str, Any]=16						,				a:	str="quick_gelu"						,				a:	List[Any]=1e-5						,				a:	Dict=0.0						,				a:	Optional[int]=0.0_2						,				a:	Dict=1.0						,				a:	Any=0						,				a:	Union[str, Any]=4_9406						,				a:	Any=4_9407						,				**a:	Dict						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

       __lowerCamelCase		:							List[Any]		 =						vocab_size
       __lowerCamelCase		:							int		 =						hidden_size
       __lowerCamelCase		:							Optional[Any]		 =						intermediate_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						layer_norm_eps
       __lowerCamelCase		:							Tuple		 =						attention_dropout
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Dict						,				a:	Union[str, os.PathLike]						,				**a:	Optional[int]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the text config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Optional[Any]		 =						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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_vision_model"""




      def __init__(    self:	int						,				a:	Tuple=768						,				a:	int=3072						,				a:	List[str]=12						,				a:	Optional[Any]=12						,				a:	Optional[int]=3						,				a:	Optional[int]=768						,				a:	Optional[Any]=32						,				a:	Optional[int]="quick_gelu"						,				a:	Union[str, Any]=1e-5						,				a:	Union[str, Any]=0.0						,				a:	Union[str, Any]=0.0_2						,				a:	int=1.0						,				**a:	Union[str, Any]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							str		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Optional[Any]		 =						num_attention_heads
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						image_size
       __lowerCamelCase		:							Tuple		 =						patch_size
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Tuple		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						attention_dropout
       __lowerCamelCase		:							Tuple		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Optional[int]						,				a:	Union[str, os.PathLike]						,				**a:	int    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Any		 =						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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit"""
      __snake_case 	=       True




      def __init__(    self:	Dict						,				a:	int=None						,				a:	str=None						,				a:	Tuple=512						,				a:	Tuple=2.6_5_9_2						,				a:	int=True						,				**a:	int						,				):
       super().__init__(**a    )

       if text_config is None:
        __lowerCamelCase		:							List[str]		 =						{}
        logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'    )

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

       __lowerCamelCase		:							List[Any]		 =						OwlViTTextConfig(**a    )
       __lowerCamelCase		:							str		 =						OwlViTVisionConfig(**a    )

       __lowerCamelCase		:							Union[str, Any]		 =						projection_dim
       __lowerCamelCase		:							Tuple		 =						logit_scale_init_value
       __lowerCamelCase		:							Dict		 =						return_dict
       __lowerCamelCase		:							Tuple		 =						1.0




      @classmethod
      def 					_snake_case     (    cls:	str						,				a:	Union[str, os.PathLike]						,				**a:	List[Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						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 					_snake_case     (    cls:	Tuple						,				a:	Dict						,				a:	Dict						,				**a:	str    ):
       __lowerCamelCase		:							List[str]		 =						{}
       __lowerCamelCase		:							List[str]		 =						text_config
       __lowerCamelCase		:							Optional[int]		 =						vision_config

       return cls.from_dict(a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Optional[Any]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							List[Any]		 =						self.text_config.to_dict()
       __lowerCamelCase		:							List[str]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							Optional[Any]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	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 					_snake_case     (    self:	Dict    ):
       return OrderedDict(
           [
               ('logits_per_image', {0: 'batch'}),
               ('logits_per_text', {0: 'batch'}),
               ('text_embeds', {0: 'batch'}),
               ('image_embeds', {0: 'batch'}),
           ]    )




      @property
      def 					_snake_case     (    self:	int    ):
       return 1e-4




      def 					_snake_case     (    self:	Any						,				a:	"ProcessorMixin"						,				a:	int = -1						,				a:	int = -1						,				a:	Optional["TensorType"] = None						,				):
       __lowerCamelCase		:							List[str]		 =						super().generate_dummy_inputs(
           processor.tokenizer						,				batch_size=a						,				seq_length=a						,				framework=a    )
       __lowerCamelCase		:							int		 =						super().generate_dummy_inputs(
           processor.image_processor						,				batch_size=a						,				framework=a    )
       return {**text_input_dict, **image_input_dict}




      @property
      def 					_snake_case     (    self:	int    ):
       return 14

