




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


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

		SCREAMING_SNAKE_CASE_					      =							xmod.model.encoder.sentence_encoder
		SCREAMING_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:
				SCREAMING_SNAKE_CASE_					      =							xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

		SCREAMING_SNAKE_CASE_					      =							XmodForSequenceClassification(lowerCAmelCase ) if classification_head else XmodForMaskedLM(lowerCAmelCase )
		model.eval()

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

		SCREAMING_SNAKE_CASE_					      =							xmod_sent_encoder.layernorm_embedding.weight
		SCREAMING_SNAKE_CASE_					      =							xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				SCREAMING_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.' )

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

				# self-attention output
				SCREAMING_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.' )
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.self_attn.out_proj.weight
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.self_attn.out_proj.bias
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.self_attn_layer_norm.weight
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.self_attn_layer_norm.bias

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

				# output
				SCREAMING_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.' )
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.fca.weight
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.fca.bias
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.final_layer_norm.weight
				SCREAMING_SNAKE_CASE_					      =							xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						SCREAMING_SNAKE_CASE_					      =							xmod_layer.adapter_layer_norm.weight
						SCREAMING_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():
						SCREAMING_SNAKE_CASE_					      =							bert_output.adapter_modules[lang_code]
						SCREAMING_SNAKE_CASE_					      =							xmod_layer.adapter_modules[lang_code]
						SCREAMING_SNAKE_CASE_					      =							from_adapter.fca.weight
						SCREAMING_SNAKE_CASE_					      =							from_adapter.fca.bias
						SCREAMING_SNAKE_CASE_					      =							from_adapter.fca.weight
						SCREAMING_SNAKE_CASE_					      =							from_adapter.fca.bias

				# end of layer

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

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

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

		SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]
		if classification_head:
				SCREAMING_SNAKE_CASE_					      =							xmod.model.classification_heads['mnli'](xmod.extract_features(lowerCAmelCase ) )
		else:
				SCREAMING_SNAKE_CASE_					      =							xmod.model(lowerCAmelCase    ,						lang_id=[SAMPLE_LANGUAGE] )[0]
		print(our_output.shape    ,						their_output.shape )
		SCREAMING_SNAKE_CASE_					      =							torch.max(torch.abs(our_output - their_output ) ).item()
		print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
		SCREAMING_SNAKE_CASE_					      =							torch.allclose(lowerCAmelCase    ,						lowerCAmelCase    ,						atol=1E-3 )
		print('Do both models output the same tensors?'    ,						'🔥' if success else '💩' )
		if not success:
				raise Exception('Something went wRoNg' )

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     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.'''
						)
						SCREAMING_SNAKE_CASE:     Tuple									= parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)





from collections import defaultdict


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	bool:
		SCREAMING_SNAKE_CASE_					      =							first_str.lower().strip()
		SCREAMING_SNAKE_CASE_					      =							second_str.lower().strip()

		# Remove whitespace
		SCREAMING_SNAKE_CASE_					      =							first_str.replace(' '    ,						'' )
		SCREAMING_SNAKE_CASE_					      =							second_str.replace(' '    ,						'' )

		# Strings of different lengths are not anagrams
		if len(lowerCAmelCase ) != len(lowerCAmelCase ):
				return False

		# Default values for count should be 0
		SCREAMING_SNAKE_CASE_					      =							defaultdict(lowerCAmelCase )

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(lowerCAmelCase ) ):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						SCREAMING_SNAKE_CASE:     Dict									= input('''Enter the first string ''').strip()
						SCREAMING_SNAKE_CASE:     str									= input('''Enter the second string ''').strip()

						SCREAMING_SNAKE_CASE:     Optional[Any]									= check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {'' if status else 'not '}anagrams.""")