





'''simple docstring'''



from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__magic_name__    =			"""\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

__magic_name__    =			"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

__magic_name__    =			"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION			)
class       __SCREAMING_SNAKE_CASE	(						datasets.Metric			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       return datasets.MetricInfo(
           description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("string"						,   id="token"	)						,   id="sequence"	),
                   "references": datasets.Sequence(
                       datasets.Sequence(datasets.Value("string"						,   id="token"	)						,   id="sequence"	)						,   id="references"	),
               }	)						,   )






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = 1						,   lowerCamelCase = 4						,   ):
       return {
           "google_bleu": gleu_score.corpus_gleu(
               list_of_references=_snake_case						,   hypotheses=_snake_case						,   min_len=_snake_case						,   max_len=_snake_case	)
       }









'''simple docstring'''



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()
__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			"""Hello, World!"""
__magic_name__   :						Union[str, Any]    =			"""en_XX"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  _snake_case     		=				xmod.model.encoder.sentence_encoder
  _snake_case     		=				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_14					,	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:
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  _snake_case     		=				XmodForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.weight
  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _snake_case     		=				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."       )

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

    # self-attention output
    _snake_case     		=				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."       )
    _snake_case     		=				xmod_layer.self_attn.out_proj.weight
    _snake_case     		=				xmod_layer.self_attn.out_proj.bias
    _snake_case     		=				xmod_layer.self_attn_layer_norm.weight
    _snake_case     		=				xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

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

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

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

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

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


if __name__ == "__main__":
 __magic_name__   :						str    =			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."""
 )
 __magic_name__   :						List[Any]    =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


