




"""simple docstring"""




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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
			raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
lowerCAmelCase							:    Tuple  							=							logging.get_logger(__name__)

lowerCAmelCase							:    List[str]  							=							"""Hello world! cécé herlolip"""



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Any:
			lowerCamelCase       =      FairseqRobertaModel.from_pretrained(snake_case_       )
			roberta.eval()  # disable dropout
			lowerCamelCase       =      roberta.model.encoder.sentence_encoder
			lowerCamelCase       =      XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,				hidden_size=roberta.cfg.model.encoder_embed_dim ,				num_hidden_layers=roberta.cfg.model.encoder_layers ,				num_attention_heads=roberta.cfg.model.encoder_attention_heads ,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,				max_position_embeddings=5_14 ,				type_vocab_size=1 ,				layer_norm_eps=1E-5 ,				)
			if classification_head:
						lowerCamelCase       =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("""Our RoBERTa config:""" ,				snake_case_       )

			lowerCamelCase       =      XLMRobertaXLForSequenceClassification(snake_case_       ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_       )
			model.eval()

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

			lowerCamelCase       =      roberta_sent_encoder.layer_norm.weight
			lowerCamelCase       =      roberta_sent_encoder.layer_norm.bias

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

						lowerCamelCase       =      layer.attention
						lowerCamelCase       =      roberta_layer.self_attn_layer_norm.weight
						lowerCamelCase       =      roberta_layer.self_attn_layer_norm.bias

						# self attention
						lowerCamelCase       =      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)       )
						)

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

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

						# this one is final layer norm
						lowerCamelCase       =      roberta_layer.final_layer_norm.weight
						lowerCamelCase       =      roberta_layer.final_layer_norm.bias

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

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

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

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

			lowerCamelCase       =      model(snake_case_       )[0]
			if classification_head:
						lowerCamelCase       =      roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_       )       )
			else:
						lowerCamelCase       =      roberta.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__":
			lowerCAmelCase							:    Dict  							=							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."""
			)
			lowerCAmelCase							:    List[Any]  							=							parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


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

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



