


"""simple docstring"""




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

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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			torch.FloatTensor
		__UpperCAmelCase		:			torch.FloatTensor







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Dict										=				1

		@register_to_config
		def __init__(  self							, _a = 2_000							, _a = 0.15							, _a = 0.01							, _a = 1348.0							, _a = 1E-5							, _a = 1							, ):
				# standard deviation of the initial noise distribution
				__a           =			sigma_max

				# setable values
				__a           =			None

				self.set_sigmas(_a							, _a							, _a							, _a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				return sample

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = None      ):
				__a           =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

				__a           =			torch.linspace(1							, _a							, _a							, device=_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = None							, _a = None      ):
				__a           =			sigma_min if sigma_min is not None else self.config.sigma_min
				__a           =			sigma_max if sigma_max is not None else self.config.sigma_max
				__a           =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if self.timesteps is None:
						self.set_timesteps(_a							, _a      )

				__a           =			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
				__a           =			torch.exp(torch.linspace(math.log(_a      )							, math.log(_a      )							, _a      )      )
				__a           =			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )

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

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

				__a           =			timestep * torch.ones(
				    sample.shape[0]							, device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
				__a           =			(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
				__a           =			timesteps.to(self.discrete_sigmas.device      )

				__a           =			self.discrete_sigmas[timesteps].to(sample.device      )
				__a           =			self.get_adjacent_sigma(_a							, _a      ).to(sample.device      )
				__a           =			torch.zeros_like(_a      )
				__a           =			(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
				__a           =			diffusion.flatten()
				while len(diffusion.shape      ) < len(sample.shape      ):
						__a           =			diffusion.unsqueeze(-1      )
				__a           =			drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				__a           =			randn_tensor(
				    sample.shape							, layout=sample.layout							, generator=_a							, device=sample.device							, dtype=sample.dtype      )
				__a           =			sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				__a           =			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=_a							, prev_sample_mean=_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, _a = True							, ):
				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
				__a           =			randn_tensor(sample.shape							, layout=sample.layout							, generator=_a      ).to(sample.device      )

				# compute step size from the model_output, the noise, and the snr
				__a           =			torch.norm(model_output.reshape(model_output.shape[0]							, -1      )							, dim=-1      ).mean()
				__a           =			torch.norm(noise.reshape(noise.shape[0]							, -1      )							, dim=-1      ).mean()
				__a           =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
				__a           =			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
				__a           =			step_size.flatten()
				while len(step_size.shape      ) < len(sample.shape      ):
						__a           =			step_size.unsqueeze(-1      )
				__a           =			sample + step_size * model_output
				__a           =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

				if not return_dict:
						return (prev_sample,)

				return SchedulerOutput(prev_sample=_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, ):
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				__a           =			timesteps.to(original_samples.device      )
				__a           =			self.discrete_sigmas.to(original_samples.device      )[timesteps]
				__a           =			(
				    noise * sigmas[:, None, None, None]
				    if noise is not None
				    else torch.randn_like(_a      ) * sigmas[:, None, None, None]
				)
				__a           =			noise + original_samples
				return noisy_samples





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










"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			parser.add_parser(
				    '''convert'''							, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							, )
				train_parser.add_argument('''--model_type'''							, type=_a							, required=_a							, help='''Model\'s type.'''      )
				train_parser.add_argument(
				    '''--tf_checkpoint'''							, type=_a							, required=_a							, help='''TensorFlow checkpoint path or folder.'''      )
				train_parser.add_argument(
				    '''--pytorch_dump_output'''							, type=_a							, required=_a							, help='''Path to the PyTorch saved model output.'''      )
				train_parser.add_argument('''--config'''							, type=_a							, default=''''''							, help='''Configuration file path or folder.'''      )
				train_parser.add_argument(
				    '''--finetuning_task_name'''							, type=_a							, default=_a							, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							, )
				train_parser.set_defaults(func=_a      )

		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

				self._logger.info(f'''Loading model {model_type}'''      )
				__a           =			model_type
				__a           =			tf_checkpoint
				__a           =			pytorch_dump_output
				__a           =			config
				__a           =			finetuning_task_name





		def 			__UpperCAmelCase       (  self      ):
				if self._model_type == "albert":
						try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "bert":
						try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "funnel":
						try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "t5":
						try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "transfo_xl":
						try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						if "ckpt" in self._tf_checkpoint.lower():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _a							, self._config							, self._pytorch_dump_output							, _a      )
				elif self._model_type == "gpt2":
						try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "xlnet":
						try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint							, self._config							, self._pytorch_dump_output							, self._finetuning_task_name      )
				elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				else:
						raise ValueError(
						    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''      )






