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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class 							lowercase						:
							# setable values
							lowercase_			:			Optional[jnp.ndarray]			   =None
							lowercase_			:			Optional[jnp.ndarray]			   =None
							lowercase_			:			Optional[jnp.ndarray]			   =None




							@classmethod
							def 					A__ (							cls):
												return cls()







@dataclass
class 							lowercase						(   _UpperCAmelCase						):
							lowercase_			:			ScoreSdeVeSchedulerState
							lowercase_			:			jnp.ndarray
							lowercase_			:			Optional[jnp.ndarray]			   =None







class 							lowercase						(   _UpperCAmelCase			,    _UpperCAmelCase						):




							@property
							def 					A__ (							self):
												return True




							@register_to_config
							def __init__(							self  ,A__ = 2_0_0_0  ,A__ = 0.15  ,A__ = 0.01  ,A__ = 1348.0  ,A__ = 1E-5  ,A__ = 1  ,):
												pass




							def 					A__ (							self):
												lowercase		      =		ScoreSdeVeSchedulerState.create()
												return self.set_sigmas(
												    SCREAMING_SNAKE_CASE_  ,self.config.num_train_timesteps  ,self.config.sigma_min  ,self.config.sigma_max  ,self.config.sampling_eps  ,)




							def 					A__ (							self  ,A__  ,A__  ,A__ = ()  ,A__ = None):
												lowercase		      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

												lowercase		      =		jnp.linspace(1  ,SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_)
												return state.replace(timesteps=SCREAMING_SNAKE_CASE_)




							def 					A__ (							self  ,A__  ,A__  ,A__ = None  ,A__ = None  ,A__ = None  ,):
												lowercase		      =		sigma_min if sigma_min is not None else self.config.sigma_min
												lowercase		      =		sigma_max if sigma_max is not None else self.config.sigma_max
												lowercase		      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if state.timesteps is None:
																	lowercase		      =		self.set_timesteps(SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_)

												lowercase		      =		jnp.exp(jnp.linspace(jnp.log(SCREAMING_SNAKE_CASE_)  ,jnp.log(SCREAMING_SNAKE_CASE_)  ,SCREAMING_SNAKE_CASE_))
												lowercase		      =		jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

												return state.replace(discrete_sigmas=SCREAMING_SNAKE_CASE_  ,sigmas=SCREAMING_SNAKE_CASE_)




							def 					A__ (							self  ,A__  ,A__  ,A__):
												return jnp.where(timesteps == 0  ,jnp.zeros_like(SCREAMING_SNAKE_CASE_)  ,state.discrete_sigmas[timesteps - 1])




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

												lowercase		      =		timestep * jnp.ones(
												    sample.shape[0]  ,)
												lowercase		      =		(timestep * (len(state.timesteps) - 1)).long()

												lowercase		      =		state.discrete_sigmas[timesteps]
												lowercase		      =		self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_)
												lowercase		      =		jnp.zeros_like(SCREAMING_SNAKE_CASE_)
												lowercase		      =		(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
												lowercase		      =		diffusion.flatten()
												lowercase		      =		broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_  ,sample.shape)
												lowercase		      =		drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												lowercase		      =		random.split(SCREAMING_SNAKE_CASE_  ,num=1)
												lowercase		      =		random.normal(key=SCREAMING_SNAKE_CASE_  ,shape=sample.shape)
												lowercase		      =		sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												lowercase		      =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

												return FlaxSdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,prev_sample_mean=SCREAMING_SNAKE_CASE_  ,state=SCREAMING_SNAKE_CASE_)




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__ = True  ,):
												if state.timesteps is None:
																	raise ValueError(
																	    '''`state.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
												lowercase		      =		random.split(SCREAMING_SNAKE_CASE_  ,num=1)
												lowercase		      =		random.normal(key=SCREAMING_SNAKE_CASE_  ,shape=sample.shape)

												# compute step size from the model_output, the noise, and the snr
												lowercase		      =		jnp.linalg.norm(SCREAMING_SNAKE_CASE_)
												lowercase		      =		jnp.linalg.norm(SCREAMING_SNAKE_CASE_)
												lowercase		      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												lowercase		      =		step_size * jnp.ones(sample.shape[0])

												# compute corrected sample: model_output term and noise term
												lowercase		      =		step_size.flatten()
												lowercase		      =		broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_  ,sample.shape)
												lowercase		      =		sample + step_size * model_output
												lowercase		      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																	return (prev_sample, state)

												return FlaxSdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,state=SCREAMING_SNAKE_CASE_)





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








import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


