
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     :	str    = logging.get_logger(__name__)

A     :	Union[str, Any]    = 'Hello, World!'
A     :	Any    = 'en_XX'


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Any:
				__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__":
					A     :	Optional[int]    = 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     :	Optional[int]    = parser.parse_args()
					convert_xmod_checkpoint_to_pytorch(
					    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)

def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))