


# 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    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		CommonSchedulerState

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






    @classmethod
    def _a (      cls ,       A_ ,       A_ ,       A_   )  ->	Dict:
         return cls(common=A_ ,       init_noise_sigma=A_ ,       timesteps=A_   )






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		DDPMSchedulerState






class    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  [e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCAmelCase__						:		jnp.dtype






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






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = 0.0001 ,       A_ = 0.02 ,       A_ = "linear" ,       A_ = None ,       A_ = "fixed_small" ,       A_ = True ,       A_ = "epsilon" ,       A_ = jnp.floataa ,       )  ->	List[Any]:
         __UpperCamelCase   							=dtype






    def _a (      self ,       A_ = None   )  ->	DDPMSchedulerState:
         if common is None:
              __UpperCamelCase   							=CommonSchedulerState.create(self   )

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

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

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






    def _a (      self ,       A_ ,       A_ ,       A_ = None   )  ->	jnp.ndarray:
         return sample






    def _a (      self ,       A_ ,       A_ ,       A_ = ()   )  ->	DDPMSchedulerState:
         __UpperCamelCase   							=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
         __UpperCamelCase   							=(jnp.arange(0 ,       A_   ) * step_ratio).round()[::-1]

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






    def _a (      self ,       A_ ,       A_ ,       A_=None ,       A_=None   )  ->	str:
         __UpperCamelCase   							=state.common.alphas_cumprod[t]
         __UpperCamelCase   							=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
         __UpperCamelCase   							=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
              __UpperCamelCase   							=self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
              __UpperCamelCase   							=jnp.clip(A_ ,       a_min=1E-20   )
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
              __UpperCamelCase   							=jnp.log(jnp.clip(A_ ,       a_min=1E-20   )   )
         elif variance_type == "fixed_large":
              __UpperCamelCase   							=state.common.betas[t]
         elif variance_type == "fixed_large_log":
              # Glide max_log
              __UpperCamelCase   							=jnp.log(state.common.betas[t]   )
         elif variance_type == "learned":
              return predicted_variance
         elif variance_type == "learned_range":
              __UpperCamelCase   							=variance
              __UpperCamelCase   							=state.common.betas[t]
              __UpperCamelCase   							=(predicted_variance + 1) / 2
              __UpperCamelCase   							=frac * max_log + (1 - frac) * min_log

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_ = True ,       )  ->	Union[FlaxDDPMSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

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

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

         # 1. compute alphas, betas
         __UpperCamelCase   							=state.common.alphas_cumprod[t]
         __UpperCamelCase   							=jnp.where(t > 0 ,       state.common.alphas_cumprod[t - 1] ,       jnp.array(1.0 ,       dtype=self.dtype   )   )
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=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":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=model_output
         elif self.config.prediction_type == "v_prediction":
              __UpperCamelCase   							=(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:
              __UpperCamelCase   							=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
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         __UpperCamelCase   							=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
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

         __UpperCamelCase   							=jnp.where(t > 0 ,       random_variance() ,       jnp.zeros(model_output.shape ,       dtype=self.dtype   )   )

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=A_ ,       state=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       )  ->	jnp.ndarray:
         return add_noise_common(state.common ,       A_ ,       A_ ,       A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       )  ->	jnp.ndarray:
         return get_velocity_common(state.common ,       A_ ,       A_ ,       A_   )






    def __len__(      self   )  ->	int:
         return self.config.num_train_timesteps









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='bert', choices=['bert'])
   parser.add_argument('--model_name', default='bert-base-uncased', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   _A			=						parser.parse_args()

   if args.model_type == "bert":
      _A			=						BertForMaskedLM.from_pretrained(args.model_name)
      _A			=						'bert'
   else:
      raise ValueError('args.model_type should be "bert".')

   _A			=						model.state_dict()
   _A			=						{}

   for w in ["word_embeddings", "position_embeddings"]:
      _A			=						state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _A			=						state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _A			=						0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      for w in ["weight", "bias"]:
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _A			=						state_dict['cls.predictions.decoder.weight']
   _A			=						state_dict['cls.predictions.bias']
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _A			=						state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _A			=						state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)





