



'''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()
lowerCAmelCase_     :       str						=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[str]						=						'Hello, World!'
lowerCAmelCase_     :       int						=						'en_XX'
def 					UpperCAmelCase   (						A  :				str					,		A  :				str					,		A  :				bool				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			Path('''data_bin'''				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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				)

							SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE					:      List[str]     					=			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:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			XmodForSequenceClassification(A				) if classification_head else XmodForMaskedLM(A				)
							model.eval()

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

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_sent_encoder.layernorm_embedding.weight
							SCREAMING_SNAKE_CASE					:      int     					=			xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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.'''				)

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

														# self-attention output
														SCREAMING_SNAKE_CASE					:      int     					=			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.'''				)
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE					:      Any     					=			xmod_layer.self_attn.out_proj.bias
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Dict     					=			xmod_layer.self_attn_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE					:      Any     					=			layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Dict     					=			xmod_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE					:      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.'''				)
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod_layer.fca.bias
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod_layer.adapter_layer_norm.weight
																					SCREAMING_SNAKE_CASE					:      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():
																					SCREAMING_SNAKE_CASE					:      Dict     					=			bert_output.adapter_modules[lang_code]
																					SCREAMING_SNAKE_CASE					:      str     					=			xmod_layer.adapter_modules[lang_code]
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			from_adapter.fca.weight
																					SCREAMING_SNAKE_CASE					:      str     					=			from_adapter.fca.bias
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			from_adapter.fca.weight
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod_sent_encoder.layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod_sent_encoder.layer_norm.bias

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

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

							SCREAMING_SNAKE_CASE					:      Dict     					=			model(A				)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(A				)				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			xmod.model(A					,		lang_id=[SAMPLE_LANGUAGE]				)[0]
							print(our_output.shape					,		their_output.shape				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							SCREAMING_SNAKE_CASE					:      Dict     					=			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_     :       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.'
							)
							lowerCAmelCase_     :       List[Any]						=						parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









'''simple docstring'''



from bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




