


import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      str     ,					UpperCamelCase		:      bool	)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   FairseqRobertaModel.from_pretrained(UpperCamelCase	)
	roberta.eval()  # disable dropout
	a_     			=   roberta.model.encoder.sentence_encoder
	a_     			=   RobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,					hidden_size=roberta.args.encoder_embed_dim     ,					num_hidden_layers=roberta.args.encoder_layers     ,					num_attention_heads=roberta.args.encoder_attention_heads     ,					intermediate_size=roberta.args.encoder_ffn_embed_dim     ,					max_position_embeddings=514     ,					type_vocab_size=1     ,					layer_norm_eps=1E-5     ,					)
	if classification_head:
		a_     			=   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""     ,					UpperCamelCase	)

	a_     			=   RobertaForSequenceClassification(UpperCamelCase	) if classification_head else RobertaForMaskedLM(UpperCamelCase	)
	model.eval()

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

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

		# self attention
		a_     			=   layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		)

		a_     			=   roberta_layer.self_attn.q_proj.weight
		a_     			=   roberta_layer.self_attn.q_proj.bias
		a_     			=   roberta_layer.self_attn.k_proj.weight
		a_     			=   roberta_layer.self_attn.k_proj.bias
		a_     			=   roberta_layer.self_attn.v_proj.weight
		a_     			=   roberta_layer.self_attn.v_proj.bias

		# self-attention output
		a_     			=   layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		a_     			=   roberta_layer.self_attn.out_proj.weight
		a_     			=   roberta_layer.self_attn.out_proj.bias
		a_     			=   roberta_layer.self_attn_layer_norm.weight
		a_     			=   roberta_layer.self_attn_layer_norm.bias

		# intermediate
		a_     			=   layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		a_     			=   roberta_layer.fca.weight
		a_     			=   roberta_layer.fca.bias

		# output
		a_     			=   layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		a_     			=   roberta_layer.fca.weight
		a_     			=   roberta_layer.fca.bias
		a_     			=   roberta_layer.final_layer_norm.weight
		a_     			=   roberta_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		a_     			=   roberta.model.classification_heads["""mnli"""].dense.weight
		a_     			=   roberta.model.classification_heads["""mnli"""].dense.bias
		a_     			=   roberta.model.classification_heads["""mnli"""].out_proj.weight
		a_     			=   roberta.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		a_     			=   roberta.model.encoder.lm_head.dense.weight
		a_     			=   roberta.model.encoder.lm_head.dense.bias
		a_     			=   roberta.model.encoder.lm_head.layer_norm.weight
		a_     			=   roberta.model.encoder.lm_head.layer_norm.bias
		a_     			=   roberta.model.encoder.lm_head.weight
		a_     			=   roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	a_     			=   roberta.encode(UpperCamelCase	).unsqueeze(0	)  # batch of size 1

	a_     			=   model(UpperCamelCase	)[0]
	if classification_head:
		a_     			=   roberta.model.classification_heads["""mnli"""](roberta.extract_features(UpperCamelCase	)	)
	else:
		a_     			=   roberta.model(UpperCamelCase	)[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(UpperCamelCase     ,					UpperCamelCase     ,					atol=1E-3	)
	print("""Do both models output the same tensors?"""     ,					"""🔥""" if success else """💩"""	)
	if not success:
		raise Exception("""Something went wRoNg"""	)

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


if __name__ == "__main__":
			_A							     =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--roberta_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_roberta_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	a_     			=   len(UpperCamelCase	)
	a_     			=   [0] * length
	a_     			=   [[False for i in range(UpperCamelCase	)] for j in range(UpperCamelCase	)]
	for i, c in enumerate(UpperCamelCase	):
		a_     			=   i
		for j in range(i + 1	):
			if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
				a_     			=   True
				a_     			=   min(UpperCamelCase     ,					0 if j == 0 else (cut[j - 1] + 1)	)
		a_     			=   mincut
	return cut[length - 1]


if __name__ == "__main__":
			_A							     =       input('Enter the string: ').strip()
			_A							     =       find_minimum_partitions(s)
			print(f'Minimum number of partitions required for the \'{s}\' is {ans}')