
import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
      raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
      raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase__          =logging.get_logger(__name__)

lowercase__          ='Hello, World!'
lowercase__          ='en_XX'




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    bool  ):
 __a			:  Tuple		    =  Path('''data_bin'''  )
 __a			:  Optional[int]		    =  FairseqXmodModel.from_pretrained(
     model_name_or_path=str(Path(lowerCAmelCase__  ).parent  )  ,       checkpoint_file=Path(lowerCAmelCase__  ).name  ,       _name='''xmod_base'''  ,       arch='''xmod_base'''  ,       task='''multilingual_masked_lm'''  ,       data_name_or_path=str(lowerCAmelCase__  )  ,       bpe='''sentencepiece'''  ,       sentencepiece_model=str(Path(lowerCAmelCase__  ).parent / '''sentencepiece.bpe.model'''  )  ,       src_dict=str(data_dir / '''dict.txt'''  )  ,       )
 xmod.eval()  # disable dropout
 print(lowerCAmelCase__  )

 __a			:  Optional[Any]		    =  xmod.model.encoder.sentence_encoder
 __a			:  str		    =  XmodConfig(
     vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,       hidden_size=xmod.cfg.model.encoder_embed_dim  ,       num_hidden_layers=xmod.cfg.model.encoder_layers  ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,       max_position_embeddings=5_1_4  ,       type_vocab_size=1  ,       layer_norm_eps=1e-5  ,       pre_norm=xmod.cfg.model.encoder_normalize_before  ,       adapter_reduction_factor=getattr(xmod.cfg.model  ,       '''bottleneck'''  ,       2  )  ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,       languages=xmod.cfg.model.languages  ,       )
 if classification_head:
  __a			:  Dict		    =  xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

 print('''Our X-MOD config:'''  ,       lowerCAmelCase__  )

 __a			:  Union[str, Any]		    =  XmodForSequenceClassification(lowerCAmelCase__  ) if classification_head else XmodForMaskedLM(lowerCAmelCase__  )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __a			:  str		    =  xmod_sent_encoder.embed_tokens.weight
 __a			:  Optional[Any]		    =  xmod_sent_encoder.embed_positions.weight
 __a			:  Dict		    =  torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c xmod doesn't use them.

 __a			:  Optional[Any]		    =  xmod_sent_encoder.layernorm_embedding.weight
 __a			:  Dict		    =  xmod_sent_encoder.layernorm_embedding.bias

 for i in range(config.num_hidden_layers  ):
  # Encoder: start of layer
  __a			:  List[str]		    =  model.roberta.encoder.layer[i]
  __a			:  Optional[Any]		    =  xmod_sent_encoder.layers[i]

  # self attention
  __a			:  int		    =  layer.attention.self
  if not (
      xmod_layer.self_attn.k_proj.weight.data.shape
      == xmod_layer.self_attn.q_proj.weight.data.shape
      == xmod_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)  )
  ):
   raise AssertionError('''Dimensions of self-attention weights do not match.'''  )

  __a			:  int		    =  xmod_layer.self_attn.q_proj.weight
  __a			:  int		    =  xmod_layer.self_attn.q_proj.bias
  __a			:  Dict		    =  xmod_layer.self_attn.k_proj.weight
  __a			:  List[str]		    =  xmod_layer.self_attn.k_proj.bias
  __a			:  Dict		    =  xmod_layer.self_attn.v_proj.weight
  __a			:  Union[str, Any]		    =  xmod_layer.self_attn.v_proj.bias

  # self-attention output
  __a			:  int		    =  layer.attention.output
  if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
   raise AssertionError('''Dimensions of self-attention output weights do not match.'''  )
  __a			:  Dict		    =  xmod_layer.self_attn.out_proj.weight
  __a			:  Optional[int]		    =  xmod_layer.self_attn.out_proj.bias
  __a			:  List[Any]		    =  xmod_layer.self_attn_layer_norm.weight
  __a			:  Optional[Any]		    =  xmod_layer.self_attn_layer_norm.bias

  # intermediate
  __a			:  Optional[Any]		    =  layer.intermediate
  if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('''Dimensions of intermediate weights do not match.'''  )
  __a			:  Tuple		    =  xmod_layer.fca.weight
  __a			:  Optional[Any]		    =  xmod_layer.fca.bias

  # output
  __a			:  Any		    =  layer.output
  if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('''Dimensions of feed-forward weights do not match.'''  )
  __a			:  Union[str, Any]		    =  xmod_layer.fca.weight
  __a			:  List[Any]		    =  xmod_layer.fca.bias
  __a			:  Dict		    =  xmod_layer.final_layer_norm.weight
  __a			:  Union[str, Any]		    =  xmod_layer.final_layer_norm.bias
  if bert_output.adapter_layer_norm is not None:
   __a			:  str		    =  xmod_layer.adapter_layer_norm.weight
   __a			:  Any		    =  xmod_layer.adapter_layer_norm.bias

  if sorted(bert_output.adapter_modules.keys()  ) != sorted(xmod_layer.adapter_modules.keys()  ):
   raise AssertionError('''Lists of language adapters do not match.'''  )
  for lang_code, adapter in xmod_layer.adapter_modules.items():
   __a			:  List[str]		    =  bert_output.adapter_modules[lang_code]
   __a			:  str		    =  xmod_layer.adapter_modules[lang_code]
   __a			:  Any		    =  from_adapter.fca.weight
   __a			:  Optional[int]		    =  from_adapter.fca.bias
   __a			:  List[str]		    =  from_adapter.fca.weight
   __a			:  Optional[Any]		    =  from_adapter.fca.bias

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  __a			:  Union[str, Any]		    =  xmod_sent_encoder.layer_norm.weight
  __a			:  Dict		    =  xmod_sent_encoder.layer_norm.bias

 if classification_head:
  __a			:  List[Any]		    =  xmod.model.classification_heads['''mnli'''].dense.weight
  __a			:  Union[str, Any]		    =  xmod.model.classification_heads['''mnli'''].dense.bias
  __a			:  Union[str, Any]		    =  xmod.model.classification_heads['''mnli'''].out_proj.weight
  __a			:  str		    =  xmod.model.classification_heads['''mnli'''].out_proj.bias
 else:
  # LM Head
  __a			:  Tuple		    =  xmod.model.encoder.lm_head.dense.weight
  __a			:  int		    =  xmod.model.encoder.lm_head.dense.bias
  __a			:  int		    =  xmod.model.encoder.lm_head.layer_norm.weight
  __a			:  Optional[int]		    =  xmod.model.encoder.lm_head.layer_norm.bias
  __a			:  List[str]		    =  xmod.model.encoder.lm_head.weight
  __a			:  int		    =  xmod.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 __a			:  Tuple		    =  xmod.encode(lowerCAmelCase__  ).unsqueeze(0  )  # batch of size 1
 model.roberta.set_default_language(lowerCAmelCase__  )

 __a			:  Optional[Any]		    =  model(lowerCAmelCase__  )[0]
 if classification_head:
  __a			:  str		    =  xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowerCAmelCase__  )  )
 else:
  __a			:  Optional[Any]		    =  xmod.model(lowerCAmelCase__  ,       lang_id=[SAMPLE_LANGUAGE]  )[0]
 print(our_output.shape  ,       their_output.shape  )
 __a			:  Optional[int]		    =  torch.max(torch.abs(our_output - their_output  )  ).item()
 print(f"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-7
 __a			:  List[str]		    =  torch.allclose(lowerCAmelCase__  ,       lowerCAmelCase__  ,       atol=1e-3  )
 print('''Do both models output the same tensors?'''  ,       '''🔥''' if success else '''💩'''  )
 if not success:
  raise Exception('''Something went wRoNg'''  )

 Path(lowerCAmelCase__  ).mkdir(parents=lowerCAmelCase__  ,       exist_ok=lowerCAmelCase__  )
 print(f"Saving model to {pytorch_dump_folder_path}"  )
 model.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
      lowercase__          =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--classification_head', action='store_true', help='Whether to convert a final classification head.'
      )
      lowercase__          =parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )


# 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__  :
   _SCREAMING_SNAKE_CASE				:							CommonSchedulerState

   # setable values
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   @classmethod
   def 				lowerCAmelCase      (cls			: int ,    snake_case_			: CommonSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray       ):
    return cls(common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_       )





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							DDPMSchedulerState





class 		UpperCamelCase__  (	__lowercase   ,__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   _SCREAMING_SNAKE_CASE				:							jnp.dtype
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return True
   @register_to_config
   def __init__(self			: Any ,    snake_case_			: int = 1_0_0_0 ,    snake_case_			: float = 0.0001 ,    snake_case_			: float = 0.02 ,    snake_case_			: str = "linear" ,    snake_case_			: Optional[jnp.ndarray] = None ,    snake_case_			: str = "fixed_small" ,    snake_case_			: bool = True ,    snake_case_			: str = "epsilon" ,    snake_case_			: jnp.dtype = jnp.floataa ,    ):
    __a			:  str		    =  dtype
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[CommonSchedulerState] = None       ):
    if common is None:
     __a			:  Optional[Any]		    =  CommonSchedulerState.create(self       )

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

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

    return DDPMSchedulerState.create(
        common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[int] = None       ):
    return sample
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: int ,    snake_case_			: Tuple = ()       ):
    __a			:  Tuple		    =  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			:  Any		    =  (jnp.arange(0 ,    snake_case_       ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str]=None ,    snake_case_			: Union[str, Any]=None       ):
    __a			:  Optional[Any]		    =  state.common.alphas_cumprod[t]
    __a			:  List[str]		    =  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			:  Optional[int]		    =  (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			:  Optional[Any]		    =  jnp.clip(snake_case_ ,    a_min=1E-20       )
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
     __a			:  int		    =  jnp.log(jnp.clip(snake_case_ ,    a_min=1E-20       )       )
    elif variance_type == "fixed_large":
     __a			:  List[str]		    =  state.common.betas[t]
    elif variance_type == "fixed_large_log":
     # Glide max_log
     __a			:  Union[str, Any]		    =  jnp.log(state.common.betas[t]       )
    elif variance_type == "learned":
     return predicted_variance
    elif variance_type == "learned_range":
     __a			:  Any		    =  variance
     __a			:  Dict		    =  state.common.betas[t]
     __a			:  Any		    =  (predicted_variance + 1) / 2
     __a			:  Optional[Any]		    =  frac * max_log + (1 - frac) * min_log

    return variance
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: int ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[jax.random.KeyArray] = None ,    snake_case_			: bool = True ,    ):
    __a			:  int		    =  timestep

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

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

    # 1. compute alphas, betas
    __a			:  Optional[int]		    =  state.common.alphas_cumprod[t]
    __a			:  Tuple		    =  jnp.where(t > 0 ,    state.common.alphas_cumprod[t - 1] ,    jnp.array(1.0 ,    dtype=self.dtype       )       )
    __a			:  Optional[int]		    =  1 - alpha_prod_t
    __a			:  Union[str, Any]		    =  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			:  int		    =  (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			:  Tuple		    =  (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			:  Dict		    =  jnp.clip(snake_case_ ,    -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			:  str		    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
    __a			:  str		    =  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			:  Tuple		    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    def random_variance():
     __a			:  Optional[int]		    =  jax.random.split(snake_case_ ,    num=1       )
     __a			:  Union[str, Any]		    =  jax.random.normal(snake_case_ ,    shape=model_output.shape ,    dtype=self.dtype       )
     return (self._get_variance(snake_case_ ,    snake_case_ ,    predicted_variance=snake_case_       ) ** 0.5) * noise

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

    __a			:  Tuple		    =  pred_prev_sample + variance

    if not return_dict:
     return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=snake_case_ ,    state=snake_case_       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return add_noise_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return get_velocity_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )







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