
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()
SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    """Hello world! cécé herlolip"""




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

				__lowercase								= DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE      ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE      )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase								= dataavec_sent_encoder.embed_tokens.weight
				__lowercase								= dataavec_sent_encoder.embed_positions.weight
				__lowercase								= torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight      )  # just zero them out b/c data2vec doesn't use them.
				__lowercase								= dataavec_sent_encoder.layernorm_embedding.weight
				__lowercase								= dataavec_sent_encoder.layernorm_embedding.bias

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

								# self attention
								__lowercase								= 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								= dataavec_layer.self_attn.q_proj.weight
								__lowercase								= dataavec_layer.self_attn.q_proj.bias
								__lowercase								= dataavec_layer.self_attn.k_proj.weight
								__lowercase								= dataavec_layer.self_attn.k_proj.bias
								__lowercase								= dataavec_layer.self_attn.v_proj.weight
								__lowercase								= dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase								= 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								= dataavec_layer.self_attn.out_proj.weight
								__lowercase								= dataavec_layer.self_attn.out_proj.bias
								__lowercase								= dataavec_layer.self_attn_layer_norm.weight
								__lowercase								= dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase								= 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								= dataavec_layer.fca.weight
								__lowercase								= dataavec_layer.fca.bias

								# output
								__lowercase								= 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								= dataavec_layer.fca.weight
								__lowercase								= dataavec_layer.fca.bias
								__lowercase								= dataavec_layer.final_layer_norm.weight
								__lowercase								= dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								__lowercase								= dataavec.model.classification_heads['mnli'].dense.weight
								__lowercase								= dataavec.model.classification_heads['mnli'].dense.bias
								__lowercase								= dataavec.model.classification_heads['mnli'].out_proj.weight
								__lowercase								= dataavec.model.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								__lowercase								= dataavec_model.encoder.lm_head.dense.weight
								__lowercase								= dataavec_model.encoder.lm_head.dense.bias
								__lowercase								= dataavec_model.encoder.lm_head.layer_norm.weight
								__lowercase								= dataavec_model.encoder.lm_head.layer_norm.bias
								__lowercase								= dataavec_model.encoder.lm_head.weight
								__lowercase								= dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase								= dataavec.encode(SCREAMING_SNAKE_CASE      ).unsqueeze(0      )  # batch of size 1

				__lowercase								= model(SCREAMING_SNAKE_CASE      )[0]
				if classification_head:
								__lowercase								= dataavec.model.classification_heads['mnli'](dataavec.extract_features(SCREAMING_SNAKE_CASE      )      )
				else:
								__lowercase								= dataavec_model(SCREAMING_SNAKE_CASE      )[0]
				print(our_output.shape   ,							their_output.shape      )
				__lowercase								= torch.max(torch.abs(our_output - their_output      )      ).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
				__lowercase								= 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__":
							SCREAMING_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."""
							)
							SCREAMING_SNAKE_CASE__							=    parser.parse_args()
							convert_dataavec_checkpoint_to_pytorch(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)







from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


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

							testmod()





