



'''simple docstring'''



import math






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						str    ,			__UpperCamelCase		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    len(__UpperCamelCase			)
							snake_case__      :   str      =    [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,			0    ,			self.N - 1			)



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						str			)      ->      Dict:
							"""simple docstring"""





							return idx * 2



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							return idx * 2 + 1



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						str			)      ->      Dict:  # noqa: E741
							"""simple docstring"""





							if l == r:
											snake_case__      :   Optional[Any]      =    A[l]
							else:
											snake_case__      :   Optional[int]      =    (l + r) // 2
											self.build(self.left(__UpperCamelCase			)    ,			__UpperCamelCase    ,			__UpperCamelCase			)
											self.build(self.right(__UpperCamelCase			)    ,			mid + 1    ,			__UpperCamelCase			)
											snake_case__      :   List[Any]      =    max(self.st[self.left(__UpperCamelCase			)]    ,			self.st[self.right(__UpperCamelCase			)]			)



			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						int			)      ->      Dict:
							"""simple docstring"""





							return self.update_recursive(1    ,			0    ,			self.N - 1    ,			a - 1    ,			b - 1    ,			__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Dict			)      ->      List[Any]:  # noqa: E741
							"""simple docstring"""





							if r < a or l > b:
											return True
							if l == r:
											snake_case__      :   str      =    val
											return True
							snake_case__      :   Optional[int]      =    (l + r) // 2
							self.update_recursive(self.left(__UpperCamelCase			)    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							self.update_recursive(self.right(__UpperCamelCase			)    ,			mid + 1    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   str      =    max(self.st[self.left(__UpperCamelCase			)]    ,			self.st[self.right(__UpperCamelCase			)]			)
							return True



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						List[Any]			)      ->      str:
							"""simple docstring"""





							return self.query_recursive(1    ,			0    ,			self.N - 1    ,			a - 1    ,			b - 1			)



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						List[Any]			)      ->      Optional[Any]:  # noqa: E741
							"""simple docstring"""





							if r < a or l > b:
											return -math.inf
							if l >= a and r <= b:
											return self.st[idx]
							snake_case__      :   Optional[int]      =    (l + r) // 2
							snake_case__      :   Optional[int]      =    self.query_recursive(self.left(__UpperCamelCase			)    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   Dict      =    self.query_recursive(self.right(__UpperCamelCase			)    ,			mid + 1    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							return max(__UpperCamelCase    ,			__UpperCamelCase			)







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   List[Any]      =    []
							for i in range(1    ,			N + 1			):
											show_list += [self.query(__UpperCamelCase    ,			__UpperCamelCase			)]
							print(__UpperCamelCase			)


if __name__ == "__main__":
				_lowercase       :						Union[str, Any]						=[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				_lowercase       :						Any						=15
				_lowercase       :						Union[str, Any]						=SegmentTree(A)
				print(segt.query(4, 6))
				print(segt.query(7, 11))
				print(segt.query(7, 12))
				segt.update(1, 3, 111)
				print(segt.query(1, 15))
				segt.update(7, 8, 235)
				segt.show_data()









'''simple docstring'''



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()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    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:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

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

								# self attention
								snake_case__      :   BertSelfAttention      =    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)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    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}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    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}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    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}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    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__":
				_lowercase       :						Any						=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."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




