



'''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()
_A					:     Tuple      =logging.get_logger(__name__)

_A					:     Union[str, Any]      ='''Hello world! cécé herlolip'''







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase       )			->      Optional[Any]:
	_lowercase	,     _lowercase       :							Tuple  	=		os.path.split(__a       )
	_lowercase       :							str  	=		DataaVecTextModel.from_pretrained(
	    __a,					checkpoint_file=__a       )
	dataavec.eval()  # disable dropout
	_lowercase       :							str  	=		dataavec.models[0]
	_lowercase       :							Tuple  	=		dataavec_model.encoder.sentence_encoder
	_lowercase       :							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:
		_lowercase       :							Tuple  	=		dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
	print('Our BERT config:',					__a       )

	_lowercase       :							Tuple  	=		DataaVecTextForSequenceClassification(__a       ) if classification_head else DataaVecTextForMaskedLM(__a       )
	model.eval()

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

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

		# self attention
		_lowercase       :							List[Any]  	=		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)       )}'''
		)

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

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

		# intermediate
		_lowercase       :							Tuple  	=		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}'''
		_lowercase       :							List[str]  	=		dataavec_layer.fca.weight
		_lowercase       :							Optional[Any]  	=		dataavec_layer.fca.bias

		# output
		_lowercase       :							Tuple  	=		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}'''
		_lowercase       :							Dict  	=		dataavec_layer.fca.weight
		_lowercase       :							Optional[Any]  	=		dataavec_layer.fca.bias
		_lowercase       :							Optional[int]  	=		dataavec_layer.final_layer_norm.weight
		_lowercase       :							List[str]  	=		dataavec_layer.final_layer_norm.bias
		# end of layer

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

	# Let's check that we get the same results.
	_lowercase       :							Any  	=		dataavec.encode(__a       ).unsqueeze(0       )  # batch of size 1

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

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


if __name__ == "__main__":
		_A					:     Optional[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.'''
		)
		_A					:     Dict      =parser.parse_args()
		convert_dataavec_checkpoint_to_pytorch(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)





from __future__ import annotations

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

