
"""simple docstring"""




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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						torch.FloatTensor
				__UpperCamelCase				:						Optional[torch.FloatTensor] 	=       None






def lowercase_  (       _snake_case       ,_snake_case=0.999       ,_snake_case="cosine"       ,):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_snake_case						):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_snake_case						):
																					return math.exp(t * -12.0						)

							else:
														raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

							SCREAMING_SNAKE_CASE__						:	str									=       []
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       i / num_diffusion_timesteps
														SCREAMING_SNAKE_CASE__						:	int									=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_snake_case						) / alpha_bar_fn(_snake_case						)       ,_snake_case						)						)
							return torch.tensor(_snake_case       ,dtype=torch.floataa						)



class      lowerCAmelCase_						(a__			, a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						List[str] 	=       [e.name for e in KarrasDiffusionSchedulers]
				__UpperCamelCase				:						Optional[int] 	=       1



				@register_to_config
				def __init__(self       ,	SCREAMING_SNAKE_CASE__ = 10_00       ,	SCREAMING_SNAKE_CASE__ = 0.0001       ,	SCREAMING_SNAKE_CASE__ = 0.02       ,	SCREAMING_SNAKE_CASE__ = "linear"       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = "epsilon"       ,	SCREAMING_SNAKE_CASE__ = "linspace"       ,	SCREAMING_SNAKE_CASE__ = 0       ,	) ->		List[Any]:



											"""simple docstring"""




											if trained_betas is not None:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   )
											elif beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE__						:	int									=       torch.linspace(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		SCREAMING_SNAKE_CASE__						:	int									=       (
																		    torch.linspace(beta_start**0.5       ,	beta_end**0.5       ,	SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		SCREAMING_SNAKE_CASE__						:	Dict									=       betas_for_alpha_bar(SCREAMING_SNAKE_CASE__   )
											else:
																		raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

											SCREAMING_SNAKE_CASE__						:	str									=       1.0 - self.betas
											SCREAMING_SNAKE_CASE__						:	Any									=       torch.cumprod(self.alphas       ,	dim=0   )

											SCREAMING_SNAKE_CASE__						:	int									=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
											SCREAMING_SNAKE_CASE__						:	int									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   )

											# setable values
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       None
											SCREAMING_SNAKE_CASE__						:	Dict									=       use_karras_sigmas
											self.set_timesteps(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
											SCREAMING_SNAKE_CASE__						:	Dict									=       False



				@property
				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											if self.config.timestep_spacing in ["linspace", "trailing"]:
																		return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		torch.FloatTensor:



											"""simple docstring"""




											if isinstance(SCREAMING_SNAKE_CASE__       ,	torch.Tensor   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       timestep.to(self.timesteps.device   )
											SCREAMING_SNAKE_CASE__						:	str									=       (self.timesteps == timestep).nonzero().item()
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.sigmas[step_index]
											SCREAMING_SNAKE_CASE__						:	List[str]									=       sample / ((sigma**2 + 1) ** 0.5)
											SCREAMING_SNAKE_CASE__						:	Tuple									=       True
											return sample



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											def lms_derivative(SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	int									=       1.0
																		for k in range(SCREAMING_SNAKE_CASE__   ):
																									if current_order == k:
																																continue
																									prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																		return prod

											SCREAMING_SNAKE_CASE__						:	Dict									=       integrate.quad(SCREAMING_SNAKE_CASE__       ,	self.sigmas[t]       ,	self.sigmas[t + 1]       ,	epsrel=1E-4   )[0]

											return integrated_coeff



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       np.linspace(0       ,	self.config.num_train_timesteps - 1       ,	SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )[
																		    ::-1
																		].copy()
											elif self.config.timestep_spacing == "leading":
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.config.num_train_timesteps // self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       (np.arange(0       ,	SCREAMING_SNAKE_CASE__   ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE__   )
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		SCREAMING_SNAKE_CASE__						:	Any									=       self.config.num_train_timesteps / self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       (np.arange(self.config.num_train_timesteps       ,	0       ,	-step_ratio   )).round().copy().astype(SCREAMING_SNAKE_CASE__   )
																		timesteps -= 1
											else:
																		raise ValueError(
																		    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       np.log(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       np.interp(SCREAMING_SNAKE_CASE__       ,	np.arange(0       ,	len(SCREAMING_SNAKE_CASE__   )   )       ,	SCREAMING_SNAKE_CASE__   )

											if self.use_karras_sigmas:
																		SCREAMING_SNAKE_CASE__						:	str									=       self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Any									=       np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) for sigma in sigmas]   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(device=SCREAMING_SNAKE_CASE__   )
											if str(SCREAMING_SNAKE_CASE__   ).startswith("""mps"""   ):
																		# mps does not support float64
																		SCREAMING_SNAKE_CASE__						:	Any									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   )
											else:
																		SCREAMING_SNAKE_CASE__						:	str									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(device=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       np.log(SCREAMING_SNAKE_CASE__   )

											# get distribution
											SCREAMING_SNAKE_CASE__						:	int									=       log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											SCREAMING_SNAKE_CASE__						:	str									=       np.cumsum((dists >= 0)       ,	axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
											SCREAMING_SNAKE_CASE__						:	str									=       low_idx + 1

											SCREAMING_SNAKE_CASE__						:	List[str]									=       log_sigmas[low_idx]
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       log_sigmas[high_idx]

											# interpolate sigmas
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       (low - log_sigma) / (low - high)
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       np.clip(SCREAMING_SNAKE_CASE__       ,	0       ,	1   )

											# transform interpolation to time range
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       (1 - w) * low_idx + w * high_idx
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       t.reshape(sigma.shape   )
											return t



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		torch.FloatTensor:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	float									=       in_sigmas[-1].item()
											SCREAMING_SNAKE_CASE__						:	float									=       in_sigmas[0].item()

											SCREAMING_SNAKE_CASE__						:	Tuple									=       7.0  # 7.0 is the value used in the paper
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       np.linspace(0       ,	1       ,	self.num_inference_steps   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       sigma_min ** (1 / rho)
											SCREAMING_SNAKE_CASE__						:	int									=       sigma_max ** (1 / rho)
											SCREAMING_SNAKE_CASE__						:	str									=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = 4       ,	SCREAMING_SNAKE_CASE__ = True       ,	) ->		Union[LMSDiscreteSchedulerOutput, Tuple]:



											"""simple docstring"""




											if not self.is_scale_input_called:
																		warnings.warn(
																		    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																		    """See `StableDiffusionPipeline` for a usage example."""   )

											if isinstance(SCREAMING_SNAKE_CASE__       ,	torch.Tensor   ):
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       timestep.to(self.timesteps.device   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       (self.timesteps == timestep).nonzero().item()
											SCREAMING_SNAKE_CASE__						:	Any									=       self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       model_output
											else:
																		raise ValueError(
																		    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

											# 2. Convert to an ODE derivative
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       (sample - pred_original_sample) / sigma
											self.derivatives.append(SCREAMING_SNAKE_CASE__   )
											if len(self.derivatives   ) > order:
																		self.derivatives.pop(0   )

											# 3. Compute linear multistep coefficients
											SCREAMING_SNAKE_CASE__						:	int									=       min(step_index + 1       ,	SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       [self.get_lms_coefficient(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) for curr_order in range(SCREAMING_SNAKE_CASE__   )]

											# 4. Compute previous sample based on the derivatives path
											SCREAMING_SNAKE_CASE__						:	Tuple									=       sample + sum(
											    coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE__       ,	reversed(self.derivatives   )   )   )

											if not return_dict:
																		return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__       ,	pred_original_sample=SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	) ->		torch.FloatTensor:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.sigmas.to(device=original_samples.device       ,	dtype=original_samples.dtype   )
											if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE__   ):
																		# mps does not support float64
																		SCREAMING_SNAKE_CASE__						:	str									=       self.timesteps.to(original_samples.device       ,	dtype=torch.floataa   )
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       timesteps.to(original_samples.device       ,	dtype=torch.floataa   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.timesteps.to(original_samples.device   )
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       timesteps.to(original_samples.device   )

											SCREAMING_SNAKE_CASE__						:	Tuple									=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       sigmas[step_indices].flatten()
											while len(sigma.shape   ) < len(original_samples.shape   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       sigma.unsqueeze(-1   )

											SCREAMING_SNAKE_CASE__						:	Tuple									=       original_samples + noise * sigma
											return noisy_samples



				def __len__(self   ) ->		Optional[int]:



											"""simple docstring"""




											return self.config.num_train_timesteps






"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

							# Encoder Layers
							for layer in range(config.num_hidden_layers						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--fnet_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained FNet model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




