




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()
__A        =    logging.get_logger(__name__)

__A        =    "Hello, World!"
__A        =    "en_XX"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      bool      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path('data_bin'      )
				__lowerCamelCase		       =    FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(UpperCamelCase__      ).parent      ) ,     checkpoint_file=Path(UpperCamelCase__      ).name ,     _name='xmod_base' ,     arch='xmod_base' ,     task='multilingual_masked_lm' ,     data_name_or_path=str(UpperCamelCase__      ) ,     bpe='sentencepiece' ,     sentencepiece_model=str(Path(UpperCamelCase__      ).parent / 'sentencepiece.bpe.model'      ) ,     src_dict=str(data_dir / 'dict.txt'      ) ,     )
				xmod.eval()  # disable dropout
				print(UpperCamelCase__      )

				__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=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		       =    xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

				__lowerCamelCase		       =    XmodForSequenceClassification(UpperCamelCase__      ) if classification_head else XmodForMaskedLM(UpperCamelCase__      )
				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(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1
				model.roberta.set_default_language(UpperCamelCase__      )

				__lowerCamelCase		       =    model(UpperCamelCase__      )[0]
				if classification_head:
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCamelCase__      )      )
				else:
								__lowerCamelCase		       =    xmod.model(UpperCamelCase__ ,     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(UpperCamelCase__ ,     UpperCamelCase__ ,     atol=1E-3      )
				print('Do both models output the same tensors?' ,     '🔥' if success else '💩'      )
				if not success:
								raise Exception('Something went wRoNg'      )

				Path(UpperCamelCase__      ).mkdir(parents=UpperCamelCase__ ,     exist_ok=UpperCamelCase__      )
				print(F"""Saving model to {pytorch_dump_folder_path}"""      )
				model.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    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."
					)
					__A        =    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_speech_available, is_torch_available


__A        =    {
    "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        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["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        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


