



'''simple docstring'''






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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class 				lowercase			:


 """simple docstring"""


 UpperCAmelCase		      =    42

 # setable values
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    None

 # running values
 UpperCAmelCase		      =    None







 @classmethod
 def _snake_case					(				cls    ,a_    ,a_    ,a_    ,a_				)						->  str:
        return cls(common=a_    ,init_noise_sigma=a_    ,timesteps=a_    ,sigmas=a_				)







@dataclass
class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    42







class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase		      =    42







 @property
 def _snake_case					(				self				)						->  Dict:
        return True







 @register_to_config
 def __init__(				self    ,a_ = 1_000    ,a_ = 0.0001    ,a_ = 0.02    ,a_ = "linear"    ,a_ = None    ,a_ = "epsilon"    ,a_ = jnp.floataa    ,)						->  Union[str, Any]:
        _UpperCAmelCase    :		Optional[int]     	=					dtype







 def _snake_case					(				self    ,a_ = None				)						->  LMSDiscreteSchedulerState:
        if common is None:
               _UpperCAmelCase    :		str     	=					CommonSchedulerState.create(self				)

        _UpperCAmelCase    :		Tuple     	=					jnp.arange(0    ,self.config.num_train_timesteps				).round()[::-1]
        _UpperCAmelCase    :		int     	=					((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        _UpperCAmelCase    :		Optional[Any]     	=					sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=a_    ,init_noise_sigma=a_    ,timesteps=a_    ,sigmas=a_    ,)







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  jnp.ndarray:
        ((_UpperCAmelCase)	,)    :		str     	=					jnp.where(state.timesteps == timestep    ,size=1				)
        _UpperCAmelCase    :		Union[str, Any]     	=					step_index[0]

        _UpperCAmelCase    :		int     	=					state.sigmas[step_index]
        _UpperCAmelCase    :		Optional[int]     	=					sample / ((sigma**2 + 1) ** 0.5)
        return sample







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_				)						->  Tuple:

        def lms_derivative(a_				):
               _UpperCAmelCase    :		Tuple     	=					1.0
               for k in range(a_				):
                      if current_order == k:
                             continue
                      prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
               return prod

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

        return integrated_coeff







 def _snake_case					(				self    ,a_    ,a_    ,a_ = ()				)						->  LMSDiscreteSchedulerState:
        _UpperCAmelCase    :		Optional[int]     	=					jnp.linspace(self.config.num_train_timesteps - 1    ,0    ,a_    ,dtype=self.dtype				)

        _UpperCAmelCase    :		str     	=					jnp.floor(a_				).astype(jnp.intaa				)
        _UpperCAmelCase    :		int     	=					jnp.ceil(a_				).astype(jnp.intaa				)

        _UpperCAmelCase    :		Optional[Any]     	=					jnp.mod(a_    ,1.0				)

        _UpperCAmelCase    :		Tuple     	=					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        _UpperCAmelCase    :		List[Any]     	=					(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        _UpperCAmelCase    :		int     	=					jnp.concatenate([sigmas, jnp.array([0.0]    ,dtype=self.dtype				)]				)

        _UpperCAmelCase    :		Union[str, Any]     	=					timesteps.astype(jnp.intaa				)

        # initial running values
        _UpperCAmelCase    :		List[Any]     	=					jnp.zeros((0,) + shape    ,dtype=self.dtype				)

        return state.replace(
            timesteps=a_    ,sigmas=a_    ,num_inference_steps=a_    ,derivatives=a_    ,)







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_    ,a_ = 4    ,a_ = True    ,)						->  Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
               raise ValueError(
                   """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""				)

        _UpperCAmelCase    :		Dict     	=					state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               _UpperCAmelCase    :		int     	=					sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _UpperCAmelCase    :		Any     	=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        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
        _UpperCAmelCase    :		Optional[Any]     	=					(sample - pred_original_sample) / sigma
        _UpperCAmelCase    :		Optional[Any]     	=					state.replace(derivatives=jnp.append(state.derivatives    ,a_				)				)
        if len(state.derivatives				) > order:
               _UpperCAmelCase    :		List[Any]     	=					state.replace(derivatives=jnp.delete(state.derivatives    ,0				)				)

        # 3. Compute linear multistep coefficients
        _UpperCAmelCase    :		Tuple     	=					min(timestep + 1    ,a_				)
        _UpperCAmelCase    :		Union[str, Any]     	=					[self.get_lms_coefficient(a_    ,a_    ,a_    ,a_				) for curr_order in range(a_				)]

        # 4. Compute previous sample based on the derivatives path
        _UpperCAmelCase    :		Any     	=					sample + sum(
            coeff * derivative for coeff, derivative in zip(a_    ,reversed(state.derivatives				)				)				)

        if not return_dict:
               return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=a_    ,state=a_				)







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_    ,)						->  jnp.ndarray:
        _UpperCAmelCase    :		Any     	=					state.sigmas[timesteps].flatten()
        _UpperCAmelCase    :		Dict     	=					broadcast_to_shape_from_left(a_    ,noise.shape				)

        _UpperCAmelCase    :		Optional[int]     	=					original_samples + noise * sigma

        return noisy_samples







 def __len__(				self				)						->  List[str]:
        return self.config.num_train_timesteps










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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      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.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

