
"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def        __lowerCAmelCase							(_UpperCamelCase						):
	if "model" in orig_key:
		__lowerCAmelCase     :		Tuple		     = orig_key.replace('model.'			,    ''						)
	if "norm1" in orig_key:
		__lowerCAmelCase     :		List[Any]		     = orig_key.replace('norm1'			,    'attention.output.LayerNorm'						)
	if "norm2" in orig_key:
		__lowerCAmelCase     :		str		     = orig_key.replace('norm2'			,    'output.LayerNorm'						)
	if "norm" in orig_key:
		__lowerCAmelCase     :		Optional[int]		     = orig_key.replace('norm'			,    'LayerNorm'						)
	if "transformer" in orig_key:
		__lowerCAmelCase     :		Tuple		     = orig_key.split('.'						)[0].split('_'						)[-1]
		__lowerCAmelCase     :		Any		     = orig_key.replace(F"transformer_{layer_num}"			,    F"encoder.layer.{layer_num}"						)
	if "mha.attn" in orig_key:
		__lowerCAmelCase     :		Tuple		     = orig_key.replace('mha.attn'			,    'attention.self'						)
	if "mha" in orig_key:
		__lowerCAmelCase     :		List[str]		     = orig_key.replace('mha'			,    'attention'						)
	if "W_q" in orig_key:
		__lowerCAmelCase     :		Optional[Any]		     = orig_key.replace('W_q'			,    'self.query'						)
	if "W_k" in orig_key:
		__lowerCAmelCase     :		Dict		     = orig_key.replace('W_k'			,    'self.key'						)
	if "W_v" in orig_key:
		__lowerCAmelCase     :		List[Any]		     = orig_key.replace('W_v'			,    'self.value'						)
	if "ff.0" in orig_key:
		__lowerCAmelCase     :		Dict		     = orig_key.replace('ff.0'			,    'intermediate.dense'						)
	if "ff.2" in orig_key:
		__lowerCAmelCase     :		Tuple		     = orig_key.replace('ff.2'			,    'output.dense'						)
	if "ff" in orig_key:
		__lowerCAmelCase     :		Tuple		     = orig_key.replace('ff'			,    'output.dense'						)
	if "mlm_class" in orig_key:
		__lowerCAmelCase     :		Optional[Any]		     = orig_key.replace('mlm.mlm_class'			,    'cls.predictions.decoder'						)
	if "mlm" in orig_key:
		__lowerCAmelCase     :		str		     = orig_key.replace('mlm'			,    'cls.predictions.transform'						)
	if "backbone.backbone.encoders" in orig_key:
		__lowerCAmelCase     :		Optional[int]		     = orig_key.replace('backbone.backbone.encoders'			,    'encoder.layer'						)
	if "cls" not in orig_key:
		__lowerCAmelCase     :		Tuple		     = 'mra.' + orig_key

	return orig_key
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase     :		Tuple		     = orig_state_dict.pop(_lowerCAmelCase						)

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			__lowerCAmelCase     :		Any		     = val

	__lowerCAmelCase     :		Optional[Any]		     = orig_state_dict['cls.predictions.decoder.bias']
	__lowerCAmelCase     :		Dict		     = torch.arange(_lowerCAmelCase						).expand((1, -1)						) + 2

	return orig_state_dict
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		str		     = torch.load(_lowerCAmelCase			,    map_location='cpu'						)['model_state_dict']
	__lowerCAmelCase     :		int		     = MraConfig.from_json_file(_lowerCAmelCase						)
	__lowerCAmelCase     :		Any		     = MraForMaskedLM(_lowerCAmelCase						)

	__lowerCAmelCase     :		int		     = convert_checkpoint_helper(config.max_position_embeddings			,    _lowerCAmelCase						)

	print(model.load_state_dict(_lowerCAmelCase						)						)
	model.eval()
	model.save_pretrained(_lowerCAmelCase						)

	print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"						)


if __name__ == "__main__":
							lowerCamelCase__     =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Mra model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase__     =						parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 					a					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

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



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






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

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      if common is None:
          __a			      =						CommonSchedulerState.create(self     )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __a			      =						(
          jnp.array(1.0						,  dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

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

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

      return DDIMSchedulerState.create(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    Optional[int] = None     )      -> jnp.ndarray:
      return sample



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __a			      =						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			      =						(jnp.arange(0						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

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



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						state.common.alphas_cumprod[timestep]
      __a			      =						jnp.where(
          prev_timestep >= 0						,  state.common.alphas_cumprod[prev_timestep]						,  state.final_alpha_cumprod     )
      __a			      =						1 - alpha_prod_t
      __a			      =						1 - alpha_prod_t_prev

      __a			      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    bool = True						,  )      -> Union[FlaxDDIMSchedulerOutput, 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"""     )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __a			      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

      __a			      =						state.common.alphas_cumprod
      __a			      =						state.final_alpha_cumprod

      # 2. compute alphas, betas
      __a			      =						alphas_cumprod[timestep]
      __a			      =						jnp.where(prev_timestep >= 0						,  alphas_cumprod[prev_timestep]						,  lowerCamelCase_     )

      __a			      =						1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          __a			      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          __a			      =						model_output
      elif self.config.prediction_type == "sample":
          __a			      =						model_output
          __a			      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          __a			      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          __a			      =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              """ `v_prediction`"""     )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __a			      =						self._get_variance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps
