



"""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()
lowercase__       =	logging.get_logger(__name__)

lowercase__       =	"Hello, World!"
lowercase__       =	"en_XX"





def    __magic_name__     ( _lowerCamelCase					:			str	,							_lowerCamelCase					:			str	,							_lowerCamelCase					:			bool       ):
							__a		:		int						=							Path("""data_bin"""       )
							__a		:		Dict						=							FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(snake_case__       ).parent       )	,							checkpoint_file=Path(snake_case__       ).name	,							_name="""xmod_base"""	,							arch="""xmod_base"""	,							task="""multilingual_masked_lm"""	,							data_name_or_path=str(snake_case__       )	,							bpe="""sentencepiece"""	,							sentencepiece_model=str(Path(snake_case__       ).parent / """sentencepiece.bpe.model"""       )	,							src_dict=str(data_dir / """dict.txt"""       )	,							)
							xmod.eval()  # disable dropout
							print(snake_case__       )

							__a		:		Union[str, Any]						=							xmod.model.encoder.sentence_encoder
							__a		:		Union[str, 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_1_4	,							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		:		Union[str, Any]						=							xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

							__a		:		Union[str, Any]						=							XmodForSequenceClassification(snake_case__       ) if classification_head else XmodForMaskedLM(snake_case__       )
							model.eval()

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

							__a		:		Optional[Any]						=							xmod_sent_encoder.layernorm_embedding.weight
							__a		:		List[str]						=							xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers       ):
														# Encoder: start of layer
														__a		:		Union[str, Any]						=							model.roberta.encoder.layer[i]
														__a		:		Dict						=							xmod_sent_encoder.layers[i]

														# self attention
														__a		:		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."""       )

														__a		:		str						=							xmod_layer.self_attn.q_proj.weight
														__a		:		Optional[int]						=							xmod_layer.self_attn.q_proj.bias
														__a		:		int						=							xmod_layer.self_attn.k_proj.weight
														__a		:		List[Any]						=							xmod_layer.self_attn.k_proj.bias
														__a		:		List[Any]						=							xmod_layer.self_attn.v_proj.weight
														__a		:		Union[str, Any]						=							xmod_layer.self_attn.v_proj.bias

														# self-attention output
														__a		:		Union[str, Any]						=							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		:		List[Any]						=							xmod_layer.self_attn.out_proj.weight
														__a		:		str						=							xmod_layer.self_attn.out_proj.bias
														__a		:		List[Any]						=							xmod_layer.self_attn_layer_norm.weight
														__a		:		Optional[Any]						=							xmod_layer.self_attn_layer_norm.bias

														# intermediate
														__a		:		Dict						=							layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("""Dimensions of intermediate weights do not match."""       )
														__a		:		Any						=							xmod_layer.fca.weight
														__a		:		List[Any]						=							xmod_layer.fca.bias

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

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														__a		:		str						=							xmod_sent_encoder.layer_norm.weight
														__a		:		Any						=							xmod_sent_encoder.layer_norm.bias

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

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

							__a		:		str						=							model(snake_case__       )[0]
							if classification_head:
														__a		:		Dict						=							xmod.model.classification_heads["""mnli"""](xmod.extract_features(snake_case__       )       )
							else:
														__a		:		Optional[Any]						=							xmod.model(snake_case__	,							lang_id=[SAMPLE_LANGUAGE]       )[0]
							print(our_output.shape	,							their_output.shape       )
							__a		:		Union[str, Any]						=							torch.max(torch.abs(our_output - their_output       )       ).item()
							print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
							__a		:		Optional[Any]						=							torch.allclose(snake_case__	,							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(snake_case__       ).mkdir(parents=snake_case__	,							exist_ok=snake_case__       )
							print(F'''Saving model to {pytorch_dump_folder_path}'''       )
							model.save_pretrained(snake_case__       )


if __name__ == "__main__":
	lowercase__       =	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."
	)
	lowercase__       =	parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=lowercase_)
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			field(default="audio-classification"						,     metadata={"include_in_asdict_even_if_is_default": True})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"audio": Audio()})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"labels": ClassLabel})
       lowerCamelCase				:					str								=			"audio"
       lowerCamelCase				:					str								=			"labels"



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''     )
            if not isinstance(features[self.label_column]      ,    a__     ):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''     )
            __snake_case      :Any 							=    copy.deepcopy(self     )
            __snake_case      :List[Any] 							=    self.label_schema.copy()
            __snake_case      :List[Any] 							=    features[self.label_column]
            __snake_case      :Optional[Any] 							=    label_schema
            return task_template



       @property
       def        __lowercase    (				self     )			->   Dict[str, str]:






            '''simple docstring'''





            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }


