





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()
__A									=				logging.get_logger(__name__)

__A									=				"Hello, World!"
__A									=				"en_XX"





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =Path("data_bin"		)
  lowerCamelCase__:      List[Any]			  =FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__a		).parent		)	,    checkpoint_file=Path(__a		).name	,    _name="xmod_base"	,    arch="xmod_base"	,    task="multilingual_masked_lm"	,    data_name_or_path=str(__a		)	,    bpe="sentencepiece"	,    sentencepiece_model=str(Path(__a		).parent / "sentencepiece.bpe.model"		)	,    src_dict=str(data_dir / "dict.txt"		)	,    )
  xmod.eval()  # disable dropout
  print(__a		)

  lowerCamelCase__:      List[str]			  =xmod.model.encoder.sentence_encoder
  lowerCamelCase__:      Tuple			  =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:
    lowerCamelCase__:      int			  =xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

  print("Our X-MOD config:"	,    __a		)

  lowerCamelCase__:      Optional[int]			  =XmodForSequenceClassification(__a		) if classification_head else XmodForMaskedLM(__a		)
  model.eval()

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

  lowerCamelCase__:      str			  =xmod_sent_encoder.layernorm_embedding.weight
  lowerCamelCase__:      List[Any]			  =xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    lowerCamelCase__:      Any			  =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."		)

    lowerCamelCase__:      Optional[Any]			  =xmod_layer.self_attn.q_proj.weight
    lowerCamelCase__:      Dict			  =xmod_layer.self_attn.q_proj.bias
    lowerCamelCase__:      Any			  =xmod_layer.self_attn.k_proj.weight
    lowerCamelCase__:      str			  =xmod_layer.self_attn.k_proj.bias
    lowerCamelCase__:      Any			  =xmod_layer.self_attn.v_proj.weight
    lowerCamelCase__:      int			  =xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowerCamelCase__:      Optional[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."		)
    lowerCamelCase__:      Any			  =xmod_layer.self_attn.out_proj.weight
    lowerCamelCase__:      Union[str, Any]			  =xmod_layer.self_attn.out_proj.bias
    lowerCamelCase__:      List[Any]			  =xmod_layer.self_attn_layer_norm.weight
    lowerCamelCase__:      Any			  =xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowerCamelCase__:      Optional[Any]			  =xmod_sent_encoder.layer_norm.weight
    lowerCamelCase__:      Optional[int]			  =xmod_sent_encoder.layer_norm.bias

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

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

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

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


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








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			(DDPMParallelScheduler,)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , **UpperCAmelCase_				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ={
       "num_train_timesteps": 1_000,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "variance_type": "fixed_small",
       "clip_sample": True,
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       , [0.002, 0.02, 0.2, 2]):
     self.check_over_configs(beta_start=UpperCAmelCase_       , beta_end=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   self.check_over_configs(thresholding=UpperCAmelCase_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=UpperCAmelCase_       , prediction_type=UpperCAmelCase_       , sample_max_value=UpperCAmelCase_       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Optional[int]:



   '''simple docstring'''



   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



   for t in [0, 500, 999]:
     self.check_over_forward(time_step=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Tuple			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =self.dummy_model()
   lowerCamelCase__:      int			  =self.dummy_sample_deter
   lowerCamelCase__:      Union[str, Any]			  =self.dummy_sample_deter + 0.1
   lowerCamelCase__:      Optional[Any]			  =self.dummy_sample_deter - 0.1

   lowerCamelCase__:      Optional[Any]			  =samplea.shape[0]
   lowerCamelCase__:      List[Any]			  =torch.stack([samplea, samplea, samplea]       , dim=0)
   lowerCamelCase__:      Union[str, Any]			  =torch.arange(UpperCAmelCase_)[0:3, None].repeat(1       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =model(samples.flatten(0       , 1)       , timesteps.flatten(0       , 1))
   lowerCamelCase__:      Tuple			  =scheduler.batch_step_no_noise(UpperCAmelCase_       , timesteps.flatten(0       , 1)       , samples.flatten(0       , 1))

   lowerCamelCase__:      List[str]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Any			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 1153.1833) < 1E-2
   assert abs(result_mean.item() - 0.5005) < 1E-3

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[int]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.dummy_model()
   lowerCamelCase__:      List[Any]			  =self.dummy_sample_deter
   lowerCamelCase__:      int			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Tuple			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Optional[Any]			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      Any			  =pred_prev_sample

   lowerCamelCase__:      Any			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      List[str]			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 258.9606) < 1E-2
   assert abs(result_mean.item() - 0.3372) < 1E-3

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config(prediction_type="v_prediction")
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      str			  =self.dummy_model()
   lowerCamelCase__:      str			  =self.dummy_sample_deter
   lowerCamelCase__:      Dict			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Union[str, Any]			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Dict			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      List[str]			  =pred_prev_sample

   lowerCamelCase__:      List[Any]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Tuple			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 202.0296) < 1E-2
   assert abs(result_mean.item() - 0.2631) < 1E-3

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.scheduler_classes[0]
   lowerCamelCase__:      Union[str, Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      List[Any]			  =[100, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =scheduler.timesteps

   for i, timestep in enumerate(UpperCAmelCase_):
     if i == len(UpperCAmelCase_) - 1:
       lowerCamelCase__:      Dict			  =-1
     else:
       lowerCamelCase__:      Union[str, Any]			  =timesteps[i + 1]

     lowerCamelCase__:      Tuple			  =scheduler.previous_timestep(UpperCAmelCase_)
     lowerCamelCase__:      str			  =prev_t.item()

     self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      List[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Dict			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =[100, 87, 50, 51, 0]

   with self.assertRaises(UpperCAmelCase_       , msg="`custom_timesteps` must be in descending order."):
     scheduler.set_timesteps(timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config()
   lowerCamelCase__:      int			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =[100, 87, 50, 1, 0]
   lowerCamelCase__:      int			  =len(UpperCAmelCase_)

   with self.assertRaises(UpperCAmelCase_       , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."):
     scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_       , timesteps=UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[Any]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =[scheduler.config.num_train_timesteps]

   with self.assertRaises(
       UpperCAmelCase_       , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"       , ):
     scheduler.set_timesteps(timesteps=UpperCAmelCase_)

