






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       =    logging.get_logger(__name__)

_a       =    "Hello world! cécé herlolip"


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    int:





						"""simple docstring"""



						_UpperCAmelCase							   =     os.path.split(UpperCamelCase__      )
						_UpperCAmelCase							   =     DataaVecTextModel.from_pretrained(
						    UpperCamelCase__					,						checkpoint_file=UpperCamelCase__      )
						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:'					,						UpperCamelCase__      )

						_UpperCAmelCase							   =     DataaVecTextForSequenceClassification(UpperCamelCase__      ) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__      )
						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(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1

						_UpperCAmelCase							   =     model(UpperCamelCase__      )[0]
						if classification_head:
												_UpperCAmelCase							   =     dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__      )      )
						else:
												_UpperCAmelCase							   =     dataavec_model(UpperCamelCase__      )[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(UpperCamelCase__					,						UpperCamelCase__					,						atol=1E-3      )
						print('Do both models output the same tensors?'					,						'🔥' if success else '💩'      )
						if not success:
												raise Exception('Something went wRoNg'      )

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


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





'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:list			)		->						list:
				snake_case__      :   List[str]      =    sorted(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: value[2]   ,				reverse=UpperCamelCase__			)

				# Create a list of size equal to the maximum deadline
				# and initialize it with -1
				snake_case__      :   Any      =    max(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: value[1]			)[1]
				snake_case__      :   str      =    [-1] * max_deadline

				# Finding the maximum profit and the count of jobs
				snake_case__      :   List[str]      =    0
				snake_case__      :   Optional[Any]      =    0
				for job in jobs:
								# Find a free time slot for this job
								# (Note that we start from the last possible slot)
								for i in range(job[1] - 1   ,				-1   ,				-1			):
												if time_slots[i] == -1:
																snake_case__      :   Optional[Any]      =    job[0]
																count += 1
																max_profit += job[2]
																break
				return [count, max_profit]


if __name__ == "__main__":
				import doctest

				doctest.testmod()




