


"""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()
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.get_logger(__name__)

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







def     _a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		Path("""data_bin"""				)
							snake_case_           =		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__				)

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

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

							snake_case_           =		XmodForSequenceClassification(snake_case__				) if classification_head else XmodForMaskedLM(snake_case__				)
							model.eval()

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

							snake_case_           =		xmod_sent_encoder.layernorm_embedding.weight
							snake_case_           =		xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														snake_case_           =		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."""				)

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

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

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

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

														# end of layer

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

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

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

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







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



