"""simple docstring"""





# 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  _snake_case		(    __snake_case   ,       __snake_case ):







				"""simple docstring"""






				a			=						1





				@register_to_config
				def __init__(      self							:							Union[str, Any]      ,    _A							:							Optional[int]=2_0_0_0      ,    _A							:							Any=0.1      ,    _A							:							Optional[Any]=2_0      ,    _A							:							Optional[Any]=1e-3):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											_SCREAMING_SNAKE_CASE		:						int			  =    None
											_SCREAMING_SNAKE_CASE		:						Any			  =    None





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]      ,    _A							:							Union[str, torch.device] = None):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.linspace(1      ,    self.config.sampling_eps      ,    _A      ,    device=_A)





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							List[str]      ,    _A							:							List[Any]      ,    _A							:							Union[str, Any]      ,    _A							:							Any=None):



											"""simple docstring"""







											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
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    std.flatten()
											while len(std.shape) < len(score.shape):
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    std.unsqueeze(-1)
											_SCREAMING_SNAKE_CASE		:						int			  =    -score / std

											# compute
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    -1.0 / len(self.timesteps)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_SCREAMING_SNAKE_CASE		:						int			  =    beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    beta_t.unsqueeze(-1)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -0.5 * beta_t * x

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.sqrt(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    drift - diffusion**2 * score
											_SCREAMING_SNAKE_CASE		:						Any			  =    x + drift * dt

											# add noise
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    randn_tensor(x.shape      ,    layout=x.layout      ,    generator=_A      ,    device=x.device      ,    dtype=x.dtype)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean





				def __len__(      self							:							int):



											"""simple docstring"""







											return self.config.num_train_timesteps



"""simple docstring"""





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasModel(config=__SCREAMING_SNAKE_CASE  )
							else:
														raise ValueError(F"""Task {task} not supported."""  )

							print(F"""Building PyTorch model from configuration: {config}"""  )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# Save tokenizer files
							print(F"""Save tokenizer files to {pytorch_dump_path}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     ,	model_max_length=512  )
							tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

							print("""Used relative position embeddings:"""     ,	model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
			)
			parser.add_argument(
			    '''--reset_position_index_per_cell''',
			    default=False,
			    action='''store_true''',
			    help='''Whether to use relative position embeddings or not. Defaults to True.''',
			)
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--tapas_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained TAPAS 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.'''
			)
			lowerCAmelCase_     =	parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)


