
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class 				UpperCAmelCase						( _UpperCamelCase       ):
					a__:			torch.FloatTensor







class 				UpperCAmelCase						( _UpperCamelCase    ,			_UpperCamelCase       ):

					@register_to_config
					def __init__(      self   :       Optional[Any]			,   lowerCAmelCase   :       int = 3			,   lowerCAmelCase   :       int = 3			,   lowerCAmelCase   :       Tuple[str] = ("DownEncoderBlock2D",)			,   lowerCAmelCase   :       Tuple[str] = ("UpDecoderBlock2D",)			,   lowerCAmelCase   :       Tuple[int] = (64,)			,   lowerCAmelCase   :       int = 1			,   lowerCAmelCase   :       str = "silu"			,   lowerCAmelCase   :       int = 3			,   lowerCAmelCase   :       int = 32			,   lowerCAmelCase   :       int = 256			,   lowerCAmelCase   :       int = 32			,   lowerCAmelCase   :       Optional[int] = None			,   lowerCAmelCase   :       float = 0.1_82_15			,   lowerCAmelCase   :       str = "group"			,   ):
											super().__init__()

											# pass init params to Encoder
											lowercase							:     Tuple									=	Encoder(
											    in_channels=_UpperCAmelCase			,   out_channels=_UpperCAmelCase			,   down_block_types=_UpperCAmelCase			,   block_out_channels=_UpperCAmelCase			,   layers_per_block=_UpperCAmelCase			,   act_fn=_UpperCAmelCase			,   norm_num_groups=_UpperCAmelCase			,   double_z=_UpperCAmelCase			,   )

											lowercase							:     Tuple									=	vq_embed_dim if vq_embed_dim is not None else latent_channels

											lowercase							:     List[str]									=	nn.Convad(_UpperCAmelCase			,   _UpperCAmelCase			,   1       )
											lowercase							:     List[Any]									=	VectorQuantizer(_UpperCAmelCase			,   _UpperCAmelCase			,   beta=0.25			,   remap=_UpperCAmelCase			,   sane_index_shape=_UpperCAmelCase       )
											lowercase							:     str									=	nn.Convad(_UpperCAmelCase			,   _UpperCAmelCase			,   1       )

											# pass init params to Decoder
											lowercase							:     Optional[Any]									=	Decoder(
											    in_channels=_UpperCAmelCase			,   out_channels=_UpperCAmelCase			,   up_block_types=_UpperCAmelCase			,   block_out_channels=_UpperCAmelCase			,   layers_per_block=_UpperCAmelCase			,   act_fn=_UpperCAmelCase			,   norm_num_groups=_UpperCAmelCase			,   norm_type=_UpperCAmelCase			,   )

					@apply_forward_hook
					def 							_lowerCAmelCase						(      self   :       Union[str, Any]			,   lowerCAmelCase   :       torch.FloatTensor			,   lowerCAmelCase   :       bool = True       ):
											lowercase							:     List[str]									=	self.encoder(_UpperCAmelCase       )
											lowercase							:     str									=	self.quant_conv(_UpperCAmelCase       )

											if not return_dict:
																	return (h,)

											return VQEncoderOutput(latents=_UpperCAmelCase       )

					@apply_forward_hook
					def 							_lowerCAmelCase						(      self   :       Dict			,   lowerCAmelCase   :       torch.FloatTensor			,   lowerCAmelCase   :       bool = False			,   lowerCAmelCase   :       bool = True       ):
											# also go through quantization layer
											if not force_not_quantize:
																	lowercase							:     str									=	self.quantize(_UpperCAmelCase       )
											else:
																	lowercase							:     Dict									=	h
											lowercase							:     Union[str, Any]									=	self.post_quant_conv(_UpperCAmelCase       )
											lowercase							:     List[Any]									=	self.decoder(_UpperCAmelCase			,   quant if self.config.norm_type == '''spatial''' else None       )

											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=_UpperCAmelCase       )




					def 							_lowerCAmelCase						(      self   :       int			,   lowerCAmelCase   :       torch.FloatTensor			,   lowerCAmelCase   :       bool = True       ):
											lowercase							:     Dict									=	sample
											lowercase							:     Union[str, Any]									=	self.encode(_UpperCAmelCase       ).latents
											lowercase							:     int									=	self.decode(_UpperCAmelCase       ).sample

											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=_UpperCAmelCase       )







'''simple docstring'''

import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				BigBirdForPreTraining(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(lowerCAmelCase_		,     lowerCAmelCase_		,     is_trivia_qa=lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  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(
							    '''--big_bird_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.'''
							)
							parser.add_argument(
							    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
							)




