





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()
snake_case_				=      logging.get_logger(__name__)

snake_case_				=      '''Hello world! cécé herlolip'''


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase		, lowerCamelCase :			Dict    		=os.path.split(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=DataaVecTextModel.from_pretrained(
		    SCREAMING_SNAKE_CASE_				,							checkpoint_file=SCREAMING_SNAKE_CASE_		)
		dataavec.eval()  # disable dropout
		lowerCamelCase :			str    		=dataavec.models[0]
		lowerCamelCase :			Optional[Any]    		=dataavec_model.encoder.sentence_encoder
		lowerCamelCase :			str    		=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=5_1_4				,							type_vocab_size=1				,							layer_norm_eps=1E-5				,							)
		if classification_head:
				lowerCamelCase :			Dict    		=dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
		print('''Our BERT config:'''				,							SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Tuple    		=DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE_		) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE_		)
		model.eval()

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

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

				# self attention
				lowerCamelCase :			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)		)}"
				)

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

				# self-attention output
				lowerCamelCase :			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}"
				lowerCamelCase :			Union[str, Any]    		=dataavec_layer.self_attn.out_proj.weight
				lowerCamelCase :			Optional[int]    		=dataavec_layer.self_attn.out_proj.bias
				lowerCamelCase :			str    		=dataavec_layer.self_attn_layer_norm.weight
				lowerCamelCase :			Any    		=dataavec_layer.self_attn_layer_norm.bias

				# intermediate
				lowerCamelCase :			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}"
				lowerCamelCase :			str    		=dataavec_layer.fca.weight
				lowerCamelCase :			str    		=dataavec_layer.fca.bias

				# output
				lowerCamelCase :			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}"
				lowerCamelCase :			Dict    		=dataavec_layer.fca.weight
				lowerCamelCase :			Any    		=dataavec_layer.fca.bias
				lowerCamelCase :			Optional[int]    		=dataavec_layer.final_layer_norm.weight
				lowerCamelCase :			Optional[int]    		=dataavec_layer.final_layer_norm.bias
				# end of layer

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

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

		lowerCamelCase :			Optional[int]    		=model(SCREAMING_SNAKE_CASE_		)[0]
		if classification_head:
				lowerCamelCase :			int    		=dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(SCREAMING_SNAKE_CASE_		)		)
		else:
				lowerCamelCase :			List[Any]    		=dataavec_model(SCREAMING_SNAKE_CASE_		)[0]
		print(our_output.shape				,							their_output.shape		)
		lowerCamelCase :			Tuple    		=torch.max(torch.abs(our_output - their_output		)		).item()
		print(F"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
		lowerCamelCase :			Any    		=torch.allclose(SCREAMING_SNAKE_CASE_				,							SCREAMING_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(SCREAMING_SNAKE_CASE_		).mkdir(parents=SCREAMING_SNAKE_CASE_				,							exist_ok=SCREAMING_SNAKE_CASE_		)
		print(F"Saving model to {pytorch_dump_folder_path}"		)
		model.save_pretrained(SCREAMING_SNAKE_CASE_		)


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








def        A__						(							SCREAMING_SNAKE_CASE_ = 1_0_0_0		) ->  int:
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Union[str, Any]    		=3
		lowerCamelCase :			List[str]    		=5
		lowerCamelCase :			Union[str, Any]    		=1
		while True:
				lowerCamelCase :			Dict    		=z * temp
				if result < n:
						zmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						lowerCamelCase :			List[Any]    		=1
						break
		while True:
				lowerCamelCase :			Optional[Any]    		=x * temp
				if result < n:
						xmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						break
		lowerCamelCase :			str    		=list(set(xmulti + zmulti		)		)
		return sum(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				print(F"""{solution() = }""")

