"""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()
_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		"Hello world! cécé herlolip"

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       ):
							A__							,       A__					:    Tuple														= os.path.split(lowerCAmelCase       )
							A__					:    Any														= DataaVecTextModel.from_pretrained(
							    lowerCAmelCase ,     checkpoint_file=lowerCAmelCase       )
							dataavec.eval()  # disable dropout
							A__					:    Optional[Any]														= dataavec.models[0]
							A__					:    Union[str, Any]														= dataavec_model.encoder.sentence_encoder
							A__					:    Dict														= 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:
														A__					:    List[str]														= dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
							print("""Our BERT config:""" ,     lowerCAmelCase       )

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

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

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

														# self attention
														A__					:    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)       )}'''
														)

														A__					:    Optional[Any]														= dataavec_layer.self_attn.q_proj.weight
														A__					:    Any														= dataavec_layer.self_attn.q_proj.bias
														A__					:    Any														= dataavec_layer.self_attn.k_proj.weight
														A__					:    Union[str, Any]														= dataavec_layer.self_attn.k_proj.bias
														A__					:    Optional[Any]														= dataavec_layer.self_attn.v_proj.weight
														A__					:    int														= dataavec_layer.self_attn.v_proj.bias

														# self-attention output
														A__					:    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}'''
														A__					:    Dict														= dataavec_layer.self_attn.out_proj.weight
														A__					:    str														= dataavec_layer.self_attn.out_proj.bias
														A__					:    Tuple														= dataavec_layer.self_attn_layer_norm.weight
														A__					:    Optional[Any]														= dataavec_layer.self_attn_layer_norm.bias

														# intermediate
														A__					:    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}'''
														A__					:    str														= dataavec_layer.fca.weight
														A__					:    int														= dataavec_layer.fca.bias

														# output
														A__					:    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}'''
														A__					:    Optional[int]														= dataavec_layer.fca.weight
														A__					:    Tuple														= dataavec_layer.fca.bias
														A__					:    Tuple														= dataavec_layer.final_layer_norm.weight
														A__					:    Tuple														= dataavec_layer.final_layer_norm.bias
														# end of layer

							if classification_head:
														A__					:    List[Any]														= dataavec.model.classification_heads["""mnli"""].dense.weight
														A__					:    Tuple														= dataavec.model.classification_heads["""mnli"""].dense.bias
														A__					:    int														= dataavec.model.classification_heads["""mnli"""].out_proj.weight
														A__					:    Optional[int]														= dataavec.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														A__					:    List[str]														= dataavec_model.encoder.lm_head.dense.weight
														A__					:    int														= dataavec_model.encoder.lm_head.dense.bias
														A__					:    List[str]														= dataavec_model.encoder.lm_head.layer_norm.weight
														A__					:    int														= dataavec_model.encoder.lm_head.layer_norm.bias
														A__					:    Optional[Any]														= dataavec_model.encoder.lm_head.weight
														A__					:    Optional[int]														= dataavec_model.encoder.lm_head.bias

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

							A__					:    int														= model(lowerCAmelCase       )[0]
							if classification_head:
														A__					:    Any														= dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(lowerCAmelCase       )       )
							else:
														A__					:    Union[str, Any]														= dataavec_model(lowerCAmelCase       )[0]
							print(our_output.shape ,     their_output.shape       )
							A__					:    Optional[int]														= torch.max(torch.abs(our_output - their_output       )       ).item()
							print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
							A__					:    Tuple														= torch.allclose(lowerCAmelCase ,     lowerCAmelCase ,     atol=1E-3       )
							print("""Do both models output the same tensors?""" ,     """🔥""" if success else """💩"""       )
							if not success:
														raise Exception("""Something went wRoNg"""       )

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


if __name__ == "__main__":
				_UpperCamelCase         =		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."
				)
				_UpperCamelCase         =		parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase         =		TypeVar("T", bound=bool)
class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

										if random.choice([True, False]       ):
																	A__							,       A__					:    Any														= roota.right, roota.left

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

										if data:
																	for item in data:
																								self.insert(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self._root:
																	raise IndexError("""Can't get top element for the empty heap."""       )
										return self._root.value





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





