



"""simple docstring"""



import math

import flax.linen as nn
import jax.numpy as jnp


def  _UpperCamelCase	(						_A					,      _A					,      _A = 1					,      _A = 1					,      _A = 1.0e4					,      _A = False					,      _A = 1.0					,      )    ->  jnp.ndarray:





    """simple docstring"""

    assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
    assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
    _UpperCAmelCase          =   float(embedding_dim // 2      )
    _UpperCAmelCase          =   math.log(max_timescale / min_timescale      ) / (num_timescales - freq_shift)
    _UpperCAmelCase          =   min_timescale * jnp.exp(jnp.arange(_A					,      dtype=jnp.floataa      ) * -log_timescale_increment      )
    _UpperCAmelCase          =   jnp.expand_dims(_A					,      1      ) * jnp.expand_dims(_A					,      0      )

    # scale embeddings
    _UpperCAmelCase          =   scale * emb

    if flip_sin_to_cos:
        _UpperCAmelCase          =   jnp.concatenate([jnp.cos(_A      ), jnp.sin(_A      )]					,      axis=1      )
    else:
        _UpperCAmelCase          =   jnp.concatenate([jnp.sin(_A      ), jnp.cos(_A      )]					,      axis=1      )
    _UpperCAmelCase          =   jnp.reshape(_A					,      [jnp.shape(_A      )[0], embedding_dim]      )
    return signal




class 		a_						( nn.Module	):
    a      :				int     =				32
    a      :				jnp.dtype     =				jnp.floataa







    @nn.compact
    def __call__(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   nn.Dense(self.time_embed_dim     ,					dtype=self.dtype     ,					name="""linear_1"""     )(__UpperCamelCase     )
        _UpperCAmelCase          =   nn.silu(__UpperCamelCase     )
        _UpperCAmelCase          =   nn.Dense(self.time_embed_dim     ,					dtype=self.dtype     ,					name="""linear_2"""     )(__UpperCamelCase     )
        return temb







class 		a_						( nn.Module	):
    a      :				int     =				32
    a      :				bool     =				False
    a      :				float     =				1







    @nn.compact
    def __call__(			self			:       Dict     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[str]:






        '''simple docstring'''






        return get_sinusoidal_embeddings(
            __UpperCamelCase     ,					embedding_dim=self.dim     ,					flip_sin_to_cos=self.flip_sin_to_cos     ,					freq_shift=self.freq_shift     )

"""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)