



"""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  :						List[Any]    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						List[Any]    							=      '''Hello, World!'''
__SCREAMING_SNAKE_CASE  :						Any    							=      '''en_XX'''



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str						,   lowercase_     :							bool							)	->       List[str]:
				_lowerCamelCase        =				Path('''data_bin'''							)
				_lowerCamelCase        =				FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(lowercase_							).parent							)						,   checkpoint_file=Path(lowercase_							).name						,   _name='''xmod_base'''						,   arch='''xmod_base'''						,   task='''multilingual_masked_lm'''						,   data_name_or_path=str(lowercase_							)						,   bpe='''sentencepiece'''						,   sentencepiece_model=str(Path(lowercase_							).parent / '''sentencepiece.bpe.model'''							)						,   src_dict=str(data_dir / '''dict.txt'''							)						,   )
				xmod.eval()  # disable dropout
				print(lowercase_							)

				_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=5_14						,   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:'''						,   lowercase_							)

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

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

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						str    							=      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  :						str    							=      parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)





"""simple docstring"""
from __future__ import annotations

from typing import Any



def        lowerCAmelCase_(   lowercase_     :							list[Any]							)	->       None:
				create_state_space_tree(lowercase_						,   []						,   0							)



def        lowerCAmelCase_(   lowercase_     :							list[Any]						,   lowercase_     :							list[Any]						,   lowercase_     :							int							)	->       None:

				if index == len(lowercase_							):
								print(lowercase_							)
								return

				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.append(sequence[index]							)
				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.pop()


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						list[Any]    							=      [3, 1, 2, 4]
			generate_all_subsequences(seq)

			seq.clear()
			seq.extend(['''A''', '''B''', '''C'''])
			generate_all_subsequences(seq)
