
"""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()
_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''Hello, World!'''
_lowercase				 =					'''en_XX'''






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  bool  ):
	A     = Path('data_bin'  )
	A     = 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__  )

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

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

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

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

	A     = xmod_sent_encoder.layernorm_embedding.weight
	A     = xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		A     = 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.'  )

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

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

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

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

		# end of layer

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

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

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

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







'''simple docstring'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




