
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						:      int									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    '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'''




	_A			=  'owlvit_text_model'







	def __init__(      self	:List[Any]  ,     a	:Any=4_9_4_0_8  ,     a	:Any=5_1_2  ,     a	:Optional[int]=2_0_4_8  ,     a	:int=1_2  ,     a	:List[Any]=8  ,     a	:str=1_6  ,     a	:Any="quick_gelu"  ,     a	:Dict=1E-5  ,     a	:Tuple=0.0  ,     a	:Union[str, Any]=0.02  ,     a	:Any=1.0  ,     a	:Dict=0  ,     a	:Optional[int]=4_9_4_0_6  ,     a	:Dict=4_9_4_0_7  ,     **a	:List[Any]  ,     )	->				Optional[Any]:
								super().__init__(pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     **a		)

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







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

								__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      cls.get_config_dict(a  ,     **a		)

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get("model_type"		) == "owlvit":
															__UpperCamelCase			:     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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'owlvit_vision_model'







	def __init__(      self	:List[str]  ,     a	:Any=7_6_8  ,     a	:Any=3_0_7_2  ,     a	:Any=1_2  ,     a	:int=1_2  ,     a	:Optional[Any]=3  ,     a	:str=7_6_8  ,     a	:Dict=3_2  ,     a	:Union[str, Any]="quick_gelu"  ,     a	:str=1E-5  ,     a	:List[str]=0.0  ,     a	:Any=0.02  ,     a	:Union[str, Any]=1.0  ,     **a	:List[str]  ,     )	->				Union[str, Any]:
								super().__init__(**a		)

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







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

								__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      cls.get_config_dict(a  ,     **a		)

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




	_A			=  'owlvit'
	_A			=  True







	def __init__(      self	:str  ,     a	:Optional[int]=None  ,     a	:List[str]=None  ,     a	:Tuple=5_1_2  ,     a	:Union[str, Any]=2.6592  ,     a	:str=True  ,     **a	:int  ,     )	->				List[Any]:
								super().__init__(**a		)

								if text_config is None:
															__UpperCamelCase			:     Union[str, Any]         =      {}
															logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."		)

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

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

								__UpperCamelCase			:     Tuple         =      projection_dim
								__UpperCamelCase			:     int         =      logit_scale_init_value
								__UpperCamelCase			:     List[str]         =      return_dict
								__UpperCamelCase			:     Optional[int]         =      1.0







	@classmethod
	def      _lowerCamelCase       (      cls	:Optional[Any]  ,     a	:Union[str, os.PathLike]  ,     **a	:List[Any]		)	->				"PretrainedConfig":
								cls._set_token_in_kwargs(a		)

								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[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      _lowerCamelCase       (      cls	:Union[str, Any]  ,     a	:Dict  ,     a	:Dict  ,     **a	:List[Any]		)	->				str:
								__UpperCamelCase			:     List[str]         =      {}
								__UpperCamelCase			:     List[str]         =      text_config
								__UpperCamelCase			:     str         =      vision_config

								return cls.from_dict(a  ,     **a		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[Any]         =      copy.deepcopy(self.__dict__		)
								__UpperCamelCase			:     List[Any]         =      self.text_config.to_dict()
								__UpperCamelCase			:     str         =      self.vision_config.to_dict()
								__UpperCamelCase			:     Tuple         =      self.__class__.model_type
								return output




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	@property
	def      _lowerCamelCase       (      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      _lowerCamelCase       (      self	:List[str]		)	->				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      _lowerCamelCase       (      self	:int		)	->				float:
								return 1E-4







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







	@property
	def      _lowerCamelCase       (      self	:str		)	->				int:
								return 1_4

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)