




"""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								=							logging.get_logger(__name__)

a								=							'Hello world! cécé herlolip'





def 							lowercase   (snake_case__				:	str ,    snake_case__				:	str ,    snake_case__				:	bool	)       ->					int:


				'''simple docstring'''






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

				lowerCAmelCase		=			DataaVecTextForSequenceClassification(snake_case__	) if classification_head else DataaVecTextForMaskedLM(snake_case__	)
				model.eval()

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

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

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

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

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

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

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

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

				lowerCAmelCase		=			model(snake_case__	)[0]
				if classification_head:
								lowerCAmelCase		=			dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(snake_case__	)	)
				else:
								lowerCAmelCase		=			dataavec_model(snake_case__	)[0]
				print(our_output.shape ,    their_output.shape	)
				lowerCAmelCase		=			torch.max(torch.abs(our_output - their_output	)	).item()
				print(f'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
				lowerCAmelCase		=			torch.allclose(snake_case__ ,    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(snake_case__	).mkdir(parents=snake_case__ ,    exist_ok=snake_case__	)
				print(f'''Saving model to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(snake_case__	)


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 							lowercase   (snake_case__				:	int	)       ->					int:


				'''simple docstring'''






				barcode //= 10  # exclude the last digit
				lowerCAmelCase		=			False
				lowerCAmelCase		=			0

				# extract and check each digit
				while barcode != 0:
								lowerCAmelCase		=			1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								lowerCAmelCase		=			not checker

				return (10 - (s % 10)) % 10





def 							lowercase   (snake_case__				:	int	)       ->					bool:


				'''simple docstring'''






				return len(str(snake_case__	)	) == 13 and get_check_digit(snake_case__	) == barcode % 10





def 							lowercase   (snake_case__				:	str	)       ->					int:


				'''simple docstring'''






				if str(snake_case__	).isalpha():
								lowerCAmelCase		=			f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(snake_case__	)
				elif int(snake_case__	) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""	)
				else:
								return int(snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a								=							get_barcode(input('Barcode: ').strip())

		if is_valid(barcode):
				print(f"""'{barcode}' is a valid barcode.""")
		else:
				print(f"""'{barcode}' is NOT a valid barcode.""")





