



"""simple docstring"""



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				['image_processor', 'tokenizer']
    a      :				Any     =				'OwlViTImageProcessor'
    a      :				int     =				('CLIPTokenizer', 'CLIPTokenizerFast')







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       Optional[int]=None     ,					**__UpperCamelCase			:       List[str]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""     )
        if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""     )

        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )







    def __call__(			self			:       str     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any="max_length"     ,					__UpperCamelCase			:       List[str]="np"     ,					**__UpperCamelCase			:       Dict     )		->str:






        '''simple docstring'''






        if text is None and query_images is None and images is None:
            raise ValueError(
                """You have to specify at least one text or query image or image. All three cannot be none."""     )

        if text is not None:
            if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) or (isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) and not isinstance(text[0]     ,					__UpperCamelCase     )):
                _UpperCAmelCase          =   [self.tokenizer(__UpperCamelCase     ,					padding=__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )]

            elif isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) and isinstance(text[0]     ,					__UpperCamelCase     ):
                _UpperCAmelCase          =   []

                # Maximum number of queries across batch
                _UpperCAmelCase          =   max([len(__UpperCamelCase     ) for t in text]     )

                # Pad all batch samples to max number of text queries
                for t in text:
                    if len(__UpperCamelCase     ) != max_num_queries:
                        _UpperCAmelCase          =   t + [""" """] * (max_num_queries - len(__UpperCamelCase     ))

                    _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					padding=__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )
                    encodings.append(__UpperCamelCase     )
            else:
                raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""     )

            if return_tensors == "np":
                _UpperCAmelCase          =   np.concatenate([encoding["""input_ids"""] for encoding in encodings]     ,					axis=0     )
                _UpperCAmelCase          =   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]     ,					axis=0     )

            elif return_tensors == "jax" and is_flax_available():
                import jax.numpy as jnp

                _UpperCAmelCase          =   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]     ,					axis=0     )
                _UpperCAmelCase          =   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]     ,					axis=0     )

            elif return_tensors == "pt" and is_torch_available():
                import torch

                _UpperCAmelCase          =   torch.cat([encoding["""input_ids"""] for encoding in encodings]     ,					dim=0     )
                _UpperCAmelCase          =   torch.cat([encoding["""attention_mask"""] for encoding in encodings]     ,					dim=0     )

            elif return_tensors == "tf" and is_tf_available():
                import tensorflow as tf

                _UpperCAmelCase          =   tf.stack([encoding["""input_ids"""] for encoding in encodings]     ,					axis=0     )
                _UpperCAmelCase          =   tf.stack([encoding["""attention_mask"""] for encoding in encodings]     ,					axis=0     )

            else:
                raise ValueError("""Target return tensor type could not be returned"""     )

            _UpperCAmelCase          =   BatchEncoding()
            _UpperCAmelCase          =   input_ids
            _UpperCAmelCase          =   attention_mask

        if query_images is not None:
            _UpperCAmelCase          =   BatchEncoding()
            _UpperCAmelCase          =   self.image_processor(
                __UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     ).pixel_values
            _UpperCAmelCase          =   query_pixel_values

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is not None and images is not None:
            _UpperCAmelCase          =   image_features.pixel_values
            return encoding
        elif query_images is not None and images is not None:
            _UpperCAmelCase          =   image_features.pixel_values
            return encoding
        elif text is not None or query_images is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**__UpperCamelCase     )     ,					tensor_type=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					*__UpperCamelCase			:       Dict     ,					**__UpperCamelCase			:       Optional[int]     )		->List[str]:






        '''simple docstring'''






        return self.image_processor.post_process(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Union[str, Any]     ,					**__UpperCamelCase			:       Any     )		->Optional[int]:






        '''simple docstring'''






        return self.image_processor.post_process_object_detection(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       Optional[int]     ,					**__UpperCamelCase			:       List[str]     )		->Dict:






        '''simple docstring'''






        return self.image_processor.post_process_image_guided_detection(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					*__UpperCamelCase			:       List[Any]     ,					**__UpperCamelCase			:       Union[str, Any]     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     ,					*__UpperCamelCase			:       Tuple     ,					**__UpperCamelCase			:       Dict     )		->Dict:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @property
    def      _snake_case					(			self			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







    @property
    def      _snake_case					(			self			:       Dict     )		->Any:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

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


if __name__ == "__main__":
 a					:	Dict							=		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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)