

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:      Optional[Any]								=     'Hello world! cécé herlolip'



def  __lowerCAmelCase (  A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     os.path.split(_SCREAMING_SNAKE_CASE      )
	UpperCAmelCase_    			=     DataaVecTextModel.from_pretrained(
	    _SCREAMING_SNAKE_CASE    ,       checkpoint_file=_SCREAMING_SNAKE_CASE      )
	dataavec.eval()  # disable dropout
	UpperCAmelCase_    			=     dataavec.models[0]
	UpperCAmelCase_    			=     dataavec_model.encoder.sentence_encoder
	UpperCAmelCase_    			=     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:
		UpperCAmelCase_    			=     dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
	print("Our BERT config:"    ,       _SCREAMING_SNAKE_CASE      )

	UpperCAmelCase_    			=     DataaVecTextForSequenceClassification(_SCREAMING_SNAKE_CASE      ) if classification_head else DataaVecTextForMaskedLM(_SCREAMING_SNAKE_CASE      )
	model.eval()

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

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

		# self attention
		UpperCAmelCase_    			=     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)      )}"
		)

		UpperCAmelCase_    			=     dataavec_layer.self_attn.q_proj.weight
		UpperCAmelCase_    			=     dataavec_layer.self_attn.q_proj.bias
		UpperCAmelCase_    			=     dataavec_layer.self_attn.k_proj.weight
		UpperCAmelCase_    			=     dataavec_layer.self_attn.k_proj.bias
		UpperCAmelCase_    			=     dataavec_layer.self_attn.v_proj.weight
		UpperCAmelCase_    			=     dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		UpperCAmelCase_    			=     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}"
		UpperCAmelCase_    			=     dataavec_layer.self_attn.out_proj.weight
		UpperCAmelCase_    			=     dataavec_layer.self_attn.out_proj.bias
		UpperCAmelCase_    			=     dataavec_layer.self_attn_layer_norm.weight
		UpperCAmelCase_    			=     dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		UpperCAmelCase_    			=     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}"
		UpperCAmelCase_    			=     dataavec_layer.fca.weight
		UpperCAmelCase_    			=     dataavec_layer.fca.bias

		# output
		UpperCAmelCase_    			=     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}"
		UpperCAmelCase_    			=     dataavec_layer.fca.weight
		UpperCAmelCase_    			=     dataavec_layer.fca.bias
		UpperCAmelCase_    			=     dataavec_layer.final_layer_norm.weight
		UpperCAmelCase_    			=     dataavec_layer.final_layer_norm.bias
		# end of layer

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

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

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


def  __lowerCAmelCase (  A      ):
	UpperCAmelCase_    			=     len(A      )

	# Set the gap value to be decreased by a factor of 1.3
	# after each iteration
	UpperCAmelCase_    			=     1.3

	# Continue sorting until the gap is 1
	while gap > 1:
		# Decrease the gap value
		UpperCAmelCase_    			=     int(gap / shrink      )

		# Sort the elements using insertion sort
		for i in range(A    ,       len(A      )      ):
			UpperCAmelCase_    			=     collection[i]
			UpperCAmelCase_    			=     i
			while j >= gap and collection[j - gap] > temp:
				UpperCAmelCase_    			=     collection[j - gap]
				j -= gap
			UpperCAmelCase_    			=     temp

	return collection


if __name__ == "__main__":
				import doctest

				doctest.testmod()