





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
			raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)

_a									=				"Hello world! cécé herlolip"





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__    ,     lowerCamelCase__        =		os.path.split(__snake_case							)
	lowerCamelCase__        =		DataaVecTextModel.from_pretrained(
	    __snake_case	,checkpoint_file=__snake_case							)
	dataavec.eval()  # disable dropout
	lowerCamelCase__        =		dataavec.models[0]
	lowerCamelCase__        =		dataavec_model.encoder.sentence_encoder
	lowerCamelCase__        =		DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings	,hidden_size=dataavec_model.args.encoder_embed_dim	,num_hidden_layers=dataavec_model.args.encoder_layers	,num_attention_heads=dataavec_model.args.encoder_attention_heads	,intermediate_size=dataavec_model.args.encoder_ffn_embed_dim	,max_position_embeddings=514	,type_vocab_size=1	,layer_norm_eps=1E-5	,)
	if classification_head:
		lowerCamelCase__        =		dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
	print('''Our BERT config:'''	,__snake_case							)

	lowerCamelCase__        =		DataaVecTextForSequenceClassification(__snake_case							) if classification_head else DataaVecTextForMaskedLM(__snake_case							)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCamelCase__        =		dataavec_sent_encoder.embed_tokens.weight
	lowerCamelCase__        =		dataavec_sent_encoder.embed_positions.weight
	lowerCamelCase__        =		torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight							)  # just zero them out b/c data2vec doesn't use them.
	lowerCamelCase__        =		dataavec_sent_encoder.layernorm_embedding.weight
	lowerCamelCase__        =		dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers							):
		# Encoder: start of layer
		lowerCamelCase__        =		model.dataavec_text.encoder.layer[i]
		lowerCamelCase__        =		dataavec_sent_encoder.layers[i]

		# self attention
		lowerCamelCase__        =		layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)							), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F' {torch.Size((config.hidden_size, config.hidden_size)							)}'
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)							), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F' {torch.Size((config.hidden_size, config.hidden_size)							)}'
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)							), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F' {torch.Size((config.hidden_size, config.hidden_size)							)}'
		)

		lowerCamelCase__        =		dataavec_layer.self_attn.q_proj.weight
		lowerCamelCase__        =		dataavec_layer.self_attn.q_proj.bias
		lowerCamelCase__        =		dataavec_layer.self_attn.k_proj.weight
		lowerCamelCase__        =		dataavec_layer.self_attn.k_proj.bias
		lowerCamelCase__        =		dataavec_layer.self_attn.v_proj.weight
		lowerCamelCase__        =		dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCamelCase__        =		layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
		lowerCamelCase__        =		dataavec_layer.self_attn.out_proj.weight
		lowerCamelCase__        =		dataavec_layer.self_attn.out_proj.bias
		lowerCamelCase__        =		dataavec_layer.self_attn_layer_norm.weight
		lowerCamelCase__        =		dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCamelCase__        =		layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
		lowerCamelCase__        =		dataavec_layer.fca.weight
		lowerCamelCase__        =		dataavec_layer.fca.bias

		# output
		lowerCamelCase__        =		layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
		lowerCamelCase__        =		dataavec_layer.fca.weight
		lowerCamelCase__        =		dataavec_layer.fca.bias
		lowerCamelCase__        =		dataavec_layer.final_layer_norm.weight
		lowerCamelCase__        =		dataavec_layer.final_layer_norm.bias
		# end of layer

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

	# Let's check that we get the same results.
	lowerCamelCase__        =		dataavec.encode(__snake_case							).unsqueeze(0							)  # batch of size 1

	lowerCamelCase__        =		model(__snake_case							)[0]
	if classification_head:
		lowerCamelCase__        =		dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__snake_case							)							)
	else:
		lowerCamelCase__        =		dataavec_model(__snake_case							)[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'''							)

	pathlib.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__":
			_a									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--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."
			)
			_a									=				parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)










import os





def lowerCAmelCase__()    ->				Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__        =		os.path.dirname(os.path.realpath(__snake_case							)							)
	lowerCamelCase__        =		os.path.join(__snake_case	,'''triangle.txt'''							)

	with open(__snake_case							) as f:
		lowerCamelCase__        =		f.readlines()

	lowerCamelCase__        =		[[int(__snake_case							) for y in x.rstrip('''\r\n'''							).split(''' '''							)] for x in triangle]

	for i in range(1	,len(__snake_case							)							):
		for j in range(len(a[i]							)							):
			lowerCamelCase__        =		a[i - 1][j] if j != len(a[i - 1]							) else 0
			lowerCamelCase__        =		a[i - 1][j - 1] if j > 0 else 0
			a[i][j] += max(__snake_case	,__snake_case							)
	return max(a[-1]							)


if __name__ == "__main__":
			print(solution())



