

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 			lowercase    (	lowercase_     ,  lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      1




				@register_to_config
				def __init__( self				,       snake_case=2000				,       snake_case=0.1				,       snake_case=20				,       snake_case=1e-3	):
											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None




				def    a							( self				,       snake_case				,       snake_case = None	):
											snake_case_          =   torch.linspace(1				,       self.config.sampling_eps				,       snake_case				,       device=snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None	):
											if self.timesteps is None:
																		raise ValueError(
																		    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'	)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											snake_case_          =   (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											snake_case_          =   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff	)	)
											snake_case_          =   std.flatten()
											while len(std.shape	) < len(score.shape	):
																		snake_case_          =   std.unsqueeze(-1	)
											snake_case_          =   -score / std

											# compute
											snake_case_          =   -1.0 / len(self.timesteps	)

											snake_case_          =   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											snake_case_          =   beta_t.flatten()
											while len(beta_t.shape	) < len(x.shape	):
																		snake_case_          =   beta_t.unsqueeze(-1	)
											snake_case_          =   -0.5 * beta_t * x

											snake_case_          =   torch.sqrt(snake_case	)
											snake_case_          =   drift - diffusion**2 * score
											snake_case_          =   x + drift * dt

											# add noise
											snake_case_          =   randn_tensor(x.shape				,       layout=x.layout				,       generator=snake_case				,       device=x.device				,       dtype=x.dtype	)
											snake_case_          =   x_mean + diffusion * math.sqrt(-dt	) * noise

											return x, x_mean




				def __len__( self	):
											return self.config.num_train_timesteps







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     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

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

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       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(
					    """--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."""
					)
					_UpperCAmelCase		:       str						    =	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
					)




