

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_          =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_          =   'Hello, World!'
SCREAMING_SNAKE_CASE_          =   'en_XX'

def 				__SCREAMING_SNAKE_CASE				(					lowerCAmelCase:	Tuple	,   lowerCAmelCase:	Dict	,   lowerCAmelCase:	List[str]			) ->							Optional[int]:
				_UpperCAmelCase							:		Optional[Any]					=     Path("data_bin"			)
				_UpperCAmelCase							:		List[str]					=     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			)

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

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

				_UpperCAmelCase							:		str					=     XmodForSequenceClassification(__a			) if classification_head else XmodForMaskedLM(__a			)
				model.eval()

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

				_UpperCAmelCase							:		int					=     xmod_sent_encoder.layernorm_embedding.weight
				_UpperCAmelCase							:		Tuple					=     xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								_UpperCAmelCase							:		Union[str, 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."			)

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

								# self-attention output
								_UpperCAmelCase							:		Tuple					=     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."			)
								_UpperCAmelCase							:		int					=     xmod_layer.self_attn.out_proj.weight
								_UpperCAmelCase							:		Dict					=     xmod_layer.self_attn.out_proj.bias
								_UpperCAmelCase							:		Any					=     xmod_layer.self_attn_layer_norm.weight
								_UpperCAmelCase							:		Tuple					=     xmod_layer.self_attn_layer_norm.bias

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

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

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								_UpperCAmelCase							:		Any					=     xmod_sent_encoder.layer_norm.weight
								_UpperCAmelCase							:		int					=     xmod_sent_encoder.layer_norm.bias

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

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

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




"""simple docstring"""




import doctest
from collections import deque

import numpy as np





class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_    			=			[1, 2, 3, 4]







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			len(self.first_signal     )
									SCREAMING_SNAKE_CASE_    			=			len(self.second_signal     )

									SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_    			=			[[0] * max_length for i in range(SCREAMING_SNAKE_CASE_     )]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			deque(self.second_signal     )
											rotated_signal.rotate(SCREAMING_SNAKE_CASE_     )
											for j, item in enumerate(SCREAMING_SNAKE_CASE_     ):
													matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_    			=			np.matmul(np.transpose(SCREAMING_SNAKE_CASE_     )							,							np.transpose(self.first_signal     )     )

									# rounding-off to two decimal places
									return [round(SCREAMING_SNAKE_CASE_							,							2     ) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()