

'''simple docstring'''


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

import torch

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




@dataclass
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42




class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    1







						@register_to_config
						def __init__(							self					:      str			,			snake_case_					:      int = 2_0_0_0			,			snake_case_					:      float = 0.1_5			,			snake_case_					:      float = 0.0_1			,			snake_case_					:      float = 1_3_4_8.0			,			snake_case_					:      float = 1e-5			,			snake_case_					:      int = 1			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  sigma_max

								# setable values
								snake_case__						:       Union[str, Any]       						=  None

								self.set_sigmas(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      str			,			snake_case_					:      int			,			snake_case_					:      float = None			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

								snake_case__						:       List[str]       						=  torch.linspace(1			,			snake_case_			,			snake_case_			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      float = None			,			snake_case_					:      float = None			,			snake_case_					:      float = None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  sigma_min if sigma_min is not None else self.config.sigma_min
								snake_case__						:       int       						=  sigma_max if sigma_max is not None else self.config.sigma_max
								snake_case__						:       Optional[int]       						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
										self.set_timesteps(snake_case_			,			snake_case_  )

								snake_case__						:       Dict       						=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								snake_case__						:       Optional[int]       						=  torch.exp(torch.linspace(math.log(snake_case_  )			,			math.log(snake_case_  )			,			snake_case_  )  )
								snake_case__						:       List[Any]       						=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,			torch.zeros_like(t.to(timesteps.device  )  )			,			self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,			)







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      int			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[torch.Generator] = None			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



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

								snake_case__						:       Optional[int]       						=  timestep * torch.ones(
								    sample.shape[0]			,			device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								snake_case__						:       int       						=  (timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								snake_case__						:       Union[str, Any]       						=  timesteps.to(self.discrete_sigmas.device  )

								snake_case__						:       Optional[Any]       						=  self.discrete_sigmas[timesteps].to(sample.device  )
								snake_case__						:       int       						=  self.get_adjacent_sigma(snake_case_			,			snake_case_  ).to(sample.device  )
								snake_case__						:       Optional[Any]       						=  torch.zeros_like(snake_case_  )
								snake_case__						:       Any       						=  (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								snake_case__						:       Optional[Any]       						=  diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
										snake_case__						:       Optional[Any]       						=  diffusion.unsqueeze(-1  )
								snake_case__						:       Tuple       						=  drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								snake_case__						:       Union[str, Any]       						=  randn_tensor(
								    sample.shape			,			layout=sample.layout			,			generator=snake_case_			,			device=sample.device			,			dtype=sample.dtype  )
								snake_case__						:       Union[str, Any]       						=  sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								snake_case__						:       Tuple       						=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
										return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=snake_case_			,			prev_sample_mean=snake_case_  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[torch.Generator] = None			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



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

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								snake_case__						:       Optional[Any]       						=  randn_tensor(sample.shape			,			layout=sample.layout			,			generator=snake_case_  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								snake_case__						:       List[str]       						=  torch.norm(model_output.reshape(model_output.shape[0]			,			-1  )			,			dim=-1  ).mean()
								snake_case__						:       int       						=  torch.norm(noise.reshape(noise.shape[0]			,			-1  )			,			dim=-1  ).mean()
								snake_case__						:       List[Any]       						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								snake_case__						:       Union[str, Any]       						=  step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								snake_case__						:       int       						=  step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
										snake_case__						:       int       						=  step_size.unsqueeze(-1  )
								snake_case__						:       Dict       						=  sample + step_size * model_output
								snake_case__						:       Any       						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
										return (prev_sample,)

								return SchedulerOutput(prev_sample=snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			):



								'''simple docstring'''



								snake_case__						:       int       						=  timesteps.to(original_samples.device  )
								snake_case__						:       Union[str, Any]       						=  self.discrete_sigmas.to(original_samples.device  )[timesteps]
								snake_case__						:       Union[str, Any]       						=  (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(snake_case_  ) * sigmas[:, None, None, None]
								)
								snake_case__						:       List[Any]       						=  noise + original_samples
								return noisy_samples







						def __len__(							self					:      Any  ):



								'''simple docstring'''



								return self.config.num_train_timesteps






'''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 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  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__						:       Union[str, Any]										=						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__						:       Optional[int]										=						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
					)



