

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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






@flax.struct.dataclass
class      lowerCamelCase_      :
	_lowercase		:   CommonSchedulerState

	# setable values
	_lowercase		:   jnp.ndarray
	_lowercase		:   jnp.ndarray
	_lowercase		:   Optional[int]     					=				None




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Any					,      __A						:     CommonSchedulerState					,      __A						:     jnp.ndarray					,      __A						:     jnp.ndarray							):
		return cls(common=__A					,      init_noise_sigma=__A					,      timesteps=__A							)






@dataclass
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   DDPMSchedulerState






class      lowerCamelCase_      (	_lowercase						,      _lowercase	):
	_lowercase		:   Optional[int]     					=				[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:   jnp.dtype




	@property
	def        lowerCAmelCase_       (     self						:     Tuple							):
		return True




	@register_to_config
	def __init__(     self						:     str					,      __A						:     int = 1000					,      __A						:     float = 0.0_0_0_1					,      __A						:     float = 0.0_2					,      __A						:     str = "linear"					,      __A						:     Optional[jnp.ndarray] = None					,      __A						:     str = "fixed_small"					,      __A						:     bool = True					,      __A						:     str = "epsilon"					,      __A						:     jnp.dtype = jnp.floataa					,      ):
		__A :			Dict		    =			dtype




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Optional[CommonSchedulerState] = None							):
		if common is None:
			__A :			List[str]		    =			CommonSchedulerState.create(self							)

		# standard deviation of the initial noise distribution
		__A :			Any		    =			jnp.array(1.0					,      dtype=self.dtype							)

		__A :			Any		    =			jnp.arange(0					,      self.config.num_train_timesteps							).round()[::-1]

		return DDPMSchedulerState.create(
		    common=__A					,      init_noise_sigma=__A					,      timesteps=__A					,      )




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     DDPMSchedulerState					,      __A						:     jnp.ndarray					,      __A						:     Optional[int] = None							):
		return sample




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     DDPMSchedulerState					,      __A						:     int					,      __A						:     Tuple = ()							):
		__A :			Dict		    =			self.config.num_train_timesteps // num_inference_steps
		# creates integer timesteps by multiplying by ratio
		# rounding to avoid issues when num_inference_step is power of 3
		__A :			Dict		    =			(jnp.arange(0					,      __A							) * step_ratio).round()[::-1]

		return state.replace(
		    num_inference_steps=__A					,      timesteps=__A					,      )




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     DDPMSchedulerState					,      __A						:     List[str]					,      __A						:     Union[str, Any]=None					,      __A						:     List[str]=None							):
		__A :			Dict		    =			state.common.alphas_cumprod[t]
		__A :			Tuple		    =			jnp.where(t > 0					,      state.common.alphas_cumprod[t - 1]					,      jnp.array(1.0					,      dtype=self.dtype							)							)

		# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
		# and sample from it to get previous sample
		# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
		__A :			List[str]		    =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

		if variance_type is None:
			__A :			List[str]		    =			self.config.variance_type

		# hacks - were probably added for training stability
		if variance_type == "fixed_small":
			__A :			int		    =			jnp.clip(__A					,      a_min=1e-2_0							)
		# for rl-diffuser https://arxiv.org/abs/2205.09991
		elif variance_type == "fixed_small_log":
			__A :			Union[str, Any]		    =			jnp.log(jnp.clip(__A					,      a_min=1e-2_0							)							)
		elif variance_type == "fixed_large":
			__A :			List[Any]		    =			state.common.betas[t]
		elif variance_type == "fixed_large_log":
			# Glide max_log
			__A :			Tuple		    =			jnp.log(state.common.betas[t]							)
		elif variance_type == "learned":
			return predicted_variance
		elif variance_type == "learned_range":
			__A :			Union[str, Any]		    =			variance
			__A :			str		    =			state.common.betas[t]
			__A :			Optional[int]		    =			(predicted_variance + 1) / 2
			__A :			List[str]		    =			frac * max_log + (1 - frac) * min_log

		return variance




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     DDPMSchedulerState					,      __A						:     jnp.ndarray					,      __A						:     int					,      __A						:     jnp.ndarray					,      __A						:     Optional[jax.random.KeyArray] = None					,      __A						:     bool = True					,      ):
		__A :			Union[str, Any]		    =			timestep

		if key is None:
			__A :			str		    =			jax.random.PRNGKey(0							)

		if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
			__A    ,				__A :			Dict		    =			jnp.split(__A					,      sample.shape[1]					,      axis=1							)
		else:
			__A :			Optional[int]		    =			None

		# 1. compute alphas, betas
		__A :			Union[str, Any]		    =			state.common.alphas_cumprod[t]
		__A :			str		    =			jnp.where(t > 0					,      state.common.alphas_cumprod[t - 1]					,      jnp.array(1.0					,      dtype=self.dtype							)							)
		__A :			str		    =			1 - alpha_prod_t
		__A :			str		    =			1 - alpha_prod_t_prev

		# 2. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
		if self.config.prediction_type == "epsilon":
			__A :			Any		    =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif self.config.prediction_type == "sample":
			__A :			Union[str, Any]		    =			model_output
		elif self.config.prediction_type == "v_prediction":
			__A :			List[str]		    =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
			    """ for the FlaxDDPMScheduler."""							)

		# 3. Clip "predicted x_0"
		if self.config.clip_sample:
			__A :			Any		    =			jnp.clip(__A					,      -1					,      1							)

		# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		__A :			Tuple		    =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
		__A :			List[Any]		    =			state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

		# 5. Compute predicted previous sample µ_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		__A :			str		    =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		def random_variance():
			__A :			Dict		    =			jax.random.split(__A					,      num=1							)
			__A :			Dict		    =			jax.random.normal(__A					,      shape=model_output.shape					,      dtype=self.dtype							)
			return (self._get_variance(__A					,      __A					,      predicted_variance=__A							) ** 0.5) * noise

		__A :			Union[str, Any]		    =			jnp.where(t > 0					,      random_variance()					,      jnp.zeros(model_output.shape					,      dtype=self.dtype							)							)

		__A :			Any		    =			pred_prev_sample + variance

		if not return_dict:
			return (pred_prev_sample, state)

		return FlaxDDPMSchedulerOutput(prev_sample=__A					,      state=__A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     DDPMSchedulerState					,      __A						:     jnp.ndarray					,      __A						:     jnp.ndarray					,      __A						:     jnp.ndarray					,      ):
		return add_noise_common(state.common					,      __A					,      __A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     DDPMSchedulerState					,      __A						:     jnp.ndarray					,      __A						:     jnp.ndarray					,      __A						:     jnp.ndarray					,      ):
		return get_velocity_common(state.common					,      __A					,      __A					,      __A							)




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








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					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(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT 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.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





