

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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    Path("data_bin"				)
     _lowerCAmelCase   :					Tuple        =    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				)

     _lowerCAmelCase   :					int        =    xmod.model.encoder.sentence_encoder
     _lowerCAmelCase   :					List[Any]        =    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   :					Dict        =    xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

     _lowerCAmelCase   :					Any        =    XmodForSequenceClassification(SCREAMING_SNAKE_CASE				) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE				)
     model.eval()

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

     _lowerCAmelCase   :					int        =    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   :					Optional[int]        =    model.roberta.encoder.layer[i]
          _lowerCAmelCase   :					List[str]        =    xmod_sent_encoder.layers[i]

          # self attention
          _lowerCAmelCase   :					Optional[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   :					List[Any]        =    xmod_layer.self_attn.q_proj.weight
          _lowerCAmelCase   :					str        =    xmod_layer.self_attn.q_proj.bias
          _lowerCAmelCase   :					Tuple        =    xmod_layer.self_attn.k_proj.weight
          _lowerCAmelCase   :					List[Any]        =    xmod_layer.self_attn.k_proj.bias
          _lowerCAmelCase   :					Any        =    xmod_layer.self_attn.v_proj.weight
          _lowerCAmelCase   :					List[str]        =    xmod_layer.self_attn.v_proj.bias

          # self-attention output
          _lowerCAmelCase   :					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   :					Tuple        =    xmod_layer.self_attn.out_proj.weight
          _lowerCAmelCase   :					Dict        =    xmod_layer.self_attn.out_proj.bias
          _lowerCAmelCase   :					List[str]        =    xmod_layer.self_attn_layer_norm.weight
          _lowerCAmelCase   :					List[str]        =    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   :					List[Any]        =    xmod_layer.fca.weight
          _lowerCAmelCase   :					str        =    xmod_layer.fca.bias

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

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          _lowerCAmelCase   :					Dict        =    xmod_sent_encoder.layer_norm.weight
          _lowerCAmelCase   :					Optional[int]        =    xmod_sent_encoder.layer_norm.bias

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

     # Let's check that we get the same results.
     _lowerCAmelCase   :					List[str]        =    xmod.encode(SCREAMING_SNAKE_CASE				).unsqueeze(0				)  # batch of size 1
     model.roberta.set_default_language(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					List[Any]        =    model(SCREAMING_SNAKE_CASE				)[0]
     if classification_head:
          _lowerCAmelCase   :					Any        =    xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE				)				)
     else:
          _lowerCAmelCase   :					Any        =    xmod.model(SCREAMING_SNAKE_CASE		,							lang_id=[SAMPLE_LANGUAGE]				)[0]
     print(our_output.shape		,							their_output.shape				)
     _lowerCAmelCase   :					int        =    torch.max(torch.abs(our_output - their_output				)				).item()
     print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
     _lowerCAmelCase   :					Dict        =    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__":
  __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_torch_available, is_vision_available


__UpperCAmelCase    				=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['YolosFeatureExtractor']
  __UpperCAmelCase    				=     ['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

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






