
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()
_snake_case      = logging.get_logger(__name__)

_snake_case      = '''Hello, World!'''
_snake_case      = '''en_XX'''

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

     UpperCamelCase						    =					xmod.model.encoder.sentence_encoder
     UpperCamelCase						    =					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=514						,     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:
          UpperCamelCase						    =					xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

     UpperCamelCase						    =					XmodForSequenceClassification(_lowercase			) if classification_head else XmodForMaskedLM(_lowercase			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCamelCase						    =					xmod_sent_encoder.embed_tokens.weight
     UpperCamelCase						    =					xmod_sent_encoder.embed_positions.weight
     UpperCamelCase						    =					torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c xmod doesn't use them.

     UpperCamelCase						    =					xmod_sent_encoder.layernorm_embedding.weight
     UpperCamelCase						    =					xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          UpperCamelCase						    =					model.roberta.encoder.layer[i]
          UpperCamelCase						    =					xmod_sent_encoder.layers[i]

          # self attention
          UpperCamelCase						    =					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.'			)

          UpperCamelCase						    =					xmod_layer.self_attn.q_proj.weight
          UpperCamelCase						    =					xmod_layer.self_attn.q_proj.bias
          UpperCamelCase						    =					xmod_layer.self_attn.k_proj.weight
          UpperCamelCase						    =					xmod_layer.self_attn.k_proj.bias
          UpperCamelCase						    =					xmod_layer.self_attn.v_proj.weight
          UpperCamelCase						    =					xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCamelCase						    =					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.'			)
          UpperCamelCase						    =					xmod_layer.self_attn.out_proj.weight
          UpperCamelCase						    =					xmod_layer.self_attn.out_proj.bias
          UpperCamelCase						    =					xmod_layer.self_attn_layer_norm.weight
          UpperCamelCase						    =					xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCamelCase						    =					layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('Dimensions of intermediate weights do not match.'			)
          UpperCamelCase						    =					xmod_layer.fca.weight
          UpperCamelCase						    =					xmod_layer.fca.bias

          # output
          UpperCamelCase						    =					layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('Dimensions of feed-forward weights do not match.'			)
          UpperCamelCase						    =					xmod_layer.fca.weight
          UpperCamelCase						    =					xmod_layer.fca.bias
          UpperCamelCase						    =					xmod_layer.final_layer_norm.weight
          UpperCamelCase						    =					xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCamelCase						    =					xmod_layer.adapter_layer_norm.weight
               UpperCamelCase						    =					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():
               UpperCamelCase						    =					bert_output.adapter_modules[lang_code]
               UpperCamelCase						    =					xmod_layer.adapter_modules[lang_code]
               UpperCamelCase						    =					from_adapter.fca.weight
               UpperCamelCase						    =					from_adapter.fca.bias
               UpperCamelCase						    =					from_adapter.fca.weight
               UpperCamelCase						    =					from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCamelCase						    =					xmod_sent_encoder.layer_norm.weight
          UpperCamelCase						    =					xmod_sent_encoder.layer_norm.bias

     if classification_head:
          UpperCamelCase						    =					xmod.model.classification_heads['mnli'].dense.weight
          UpperCamelCase						    =					xmod.model.classification_heads['mnli'].dense.bias
          UpperCamelCase						    =					xmod.model.classification_heads['mnli'].out_proj.weight
          UpperCamelCase						    =					xmod.model.classification_heads['mnli'].out_proj.bias
     else:
          # LM Head
          UpperCamelCase						    =					xmod.model.encoder.lm_head.dense.weight
          UpperCamelCase						    =					xmod.model.encoder.lm_head.dense.bias
          UpperCamelCase						    =					xmod.model.encoder.lm_head.layer_norm.weight
          UpperCamelCase						    =					xmod.model.encoder.lm_head.layer_norm.bias
          UpperCamelCase						    =					xmod.model.encoder.lm_head.weight
          UpperCamelCase						    =					xmod.model.encoder.lm_head.bias

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

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

     Path(_lowercase			).mkdir(parents=_lowercase						,     exist_ok=_lowercase			)
     print(F'Saving model to {pytorch_dump_folder_path}'			)
     model.save_pretrained(_lowercase			)


if __name__ == "__main__":
 _snake_case      = 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.'''
 )
 _snake_case      = parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )




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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


