'''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()
__UpperCAmelCase     							=logging.get_logger(__name__)

__UpperCAmelCase     							="Hello, World!"
__UpperCAmelCase     							="en_XX"






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  Path('''data_bin'''				)
    __lowerCamelCase		=  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		=  xmod.model.encoder.sentence_encoder
    __lowerCamelCase		=  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:
        __lowerCamelCase		=  xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

    __lowerCamelCase		=  XmodForSequenceClassification(_A				) if classification_head else XmodForMaskedLM(_A				)
    model.eval()

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

    __lowerCamelCase		=  xmod_sent_encoder.layernorm_embedding.weight
    __lowerCamelCase		=  xmod_sent_encoder.layernorm_embedding.bias

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

        # self attention
        __lowerCamelCase		=  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		=  xmod_layer.self_attn.q_proj.weight
        __lowerCamelCase		=  xmod_layer.self_attn.q_proj.bias
        __lowerCamelCase		=  xmod_layer.self_attn.k_proj.weight
        __lowerCamelCase		=  xmod_layer.self_attn.k_proj.bias
        __lowerCamelCase		=  xmod_layer.self_attn.v_proj.weight
        __lowerCamelCase		=  xmod_layer.self_attn.v_proj.bias

        # self-attention output
        __lowerCamelCase		=  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		=  xmod_layer.self_attn.out_proj.weight
        __lowerCamelCase		=  xmod_layer.self_attn.out_proj.bias
        __lowerCamelCase		=  xmod_layer.self_attn_layer_norm.weight
        __lowerCamelCase		=  xmod_layer.self_attn_layer_norm.bias

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

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

        # end of layer

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

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

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

    __lowerCamelCase		=  model(_A				)[0]
    if classification_head:
        __lowerCamelCase		=  xmod.model.classification_heads['''mnli'''](xmod.extract_features(_A				)				)
    else:
        __lowerCamelCase		=  xmod.model(_A				,  lang_id=[SAMPLE_LANGUAGE]				)[0]
    print(our_output.shape				,  their_output.shape				)
    __lowerCamelCase		=  torch.max(torch.abs(our_output - their_output				)				).item()
    print(f"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
    __lowerCamelCase		=  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__":
  __UpperCAmelCase     							=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."
  )
  __UpperCAmelCase     							=parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''MvpTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MvpForCausalLM''',
           '''MvpForConditionalGeneration''',
           '''MvpForQuestionAnswering''',
           '''MvpForSequenceClassification''',
           '''MvpModel''',
           '''MvpPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
