





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

lowerCAmelCase_   :       Tuple       =     "Hello, World!"
lowerCAmelCase_   :       Tuple       =     "en_XX"






def 					_lowerCamelCase							(					lowercase		: str       ,				lowercase		: str       ,				lowercase		: bool		)      ->      Optional[int]:
				_a				      =					Path("data_bin"		)
				_a				      =					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__		)

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

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

				_a				      =					XmodForSequenceClassification(A__		) if classification_head else XmodForMaskedLM(A__		)
				model.eval()

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

				_a				      =					xmod_sent_encoder.layernorm_embedding.weight
				_a				      =					xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								_a				      =					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."		)

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

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

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

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

								# end of layer

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

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

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

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




'''simple docstring'''






from typing import TYPE_CHECKING

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


__A   :    str											= {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    List[str]											= [
      "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ASTForAudioClassification",
      "ASTModel",
      "ASTPreTrainedModel",
  ]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Union[str, Any]											= ["ASTFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ASTConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ASTForAudioClassification,
        ASTModel,
        ASTPreTrainedModel,
    )

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
  import sys

  __A   :    List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



