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

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

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      List[str]:
							lowerCAmelCase      						=						Path('''data_bin'''				)
							lowerCAmelCase      						=						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__				)

							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_1_4  ,    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:'''  ,    snake_case__				)

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

							lowerCAmelCase      						=						model(snake_case__				)[0]
							if classification_head:
														lowerCAmelCase      						=						xmod.model.classification_heads['''mnli'''](xmod.extract_features(snake_case__				)				)
							else:
														lowerCAmelCase      						=						xmod.model(snake_case__  ,    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(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__     :							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.'''
	)
	lowercase__     :							Tuple            =      parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)









import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

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

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

		if len(lowerCAmelCase ) == 0:
				return 0

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

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

		SCREAMING_SNAKE_CASE_					      =							set(lowerCAmelCase )

		@functools.cache
		def dynamic_programming(lowerCAmelCase ) -> int:
				if index > 365:
						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()