
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_						: str         =  logging.get_logger(__name__)

lowerCamelCase_						: Tuple         =  '''Hello, World!'''
lowerCamelCase_						: Optional[Any]         =  '''en_XX'''







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							Dict	=						Path("""data_bin"""						)
				UpperCamelCase_:							Tuple	=						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						)

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

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

				UpperCamelCase_:							str	=						XmodForSequenceClassification(_A						) if classification_head else XmodForMaskedLM(_A						)
				model.eval()

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

				UpperCamelCase_:							int	=						xmod_sent_encoder.layernorm_embedding.weight
				UpperCamelCase_:							Dict	=						xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								UpperCamelCase_:							Union[str, 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."""						)

								UpperCamelCase_:							List[str]	=						xmod_layer.self_attn.q_proj.weight
								UpperCamelCase_:							Dict	=						xmod_layer.self_attn.q_proj.bias
								UpperCamelCase_:							int	=						xmod_layer.self_attn.k_proj.weight
								UpperCamelCase_:							str	=						xmod_layer.self_attn.k_proj.bias
								UpperCamelCase_:							str	=						xmod_layer.self_attn.v_proj.weight
								UpperCamelCase_:							int	=						xmod_layer.self_attn.v_proj.bias

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

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

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

								# end of layer

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

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

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

				UpperCamelCase_:							Tuple	=						model(_A						)[0]
				if classification_head:
								UpperCamelCase_:							int	=						xmod.model.classification_heads["""mnli"""](xmod.extract_features(_A						)						)
				else:
								UpperCamelCase_:							Dict	=						xmod.model(_A     ,    lang_id=[SAMPLE_LANGUAGE]						)[0]
				print(our_output.shape     ,    their_output.shape						)
				UpperCamelCase_:							Dict	=						torch.max(torch.abs(our_output - their_output						)						).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
				UpperCamelCase_:							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__":
							lowerCamelCase_						: List[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."""
							)
							lowerCamelCase_						: List[Any]         =  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

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






