






"""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()
lowercase           =							logging.get_logger(__name__)

lowercase           =							'''Hello world! cécé herlolip'''






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:str			,    __lowerCamelCase:bool ):

						'''simple docstring'''






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

						__magic_name__      =  DataaVecTextForSequenceClassification(__lowerCamelCase ) if classification_head else DataaVecTextForMaskedLM(__lowerCamelCase )
						model.eval()

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

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

												# self attention
												__magic_name__      =  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) )}'''
												)

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

												# self-attention output
												__magic_name__      =  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}'''
												__magic_name__      =  dataavec_layer.self_attn.out_proj.weight
												__magic_name__      =  dataavec_layer.self_attn.out_proj.bias
												__magic_name__      =  dataavec_layer.self_attn_layer_norm.weight
												__magic_name__      =  dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												__magic_name__      =  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}'''
												__magic_name__      =  dataavec_layer.fca.weight
												__magic_name__      =  dataavec_layer.fca.bias

												# output
												__magic_name__      =  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}'''
												__magic_name__      =  dataavec_layer.fca.weight
												__magic_name__      =  dataavec_layer.fca.bias
												__magic_name__      =  dataavec_layer.final_layer_norm.weight
												__magic_name__      =  dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

						__magic_name__      =  model(__lowerCamelCase )[0]
						if classification_head:
												__magic_name__      =  dataavec.model.classification_heads["mnli"](dataavec.extract_features(__lowerCamelCase ) )
						else:
												__magic_name__      =  dataavec_model(__lowerCamelCase )[0]
						print(our_output.shape			,    their_output.shape )
						__magic_name__      =  torch.max(torch.abs(our_output - their_output ) ).item()
						print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
						__magic_name__      =  torch.allclose(__lowerCamelCase			,    __lowerCamelCase			,    atol=1E-3 )
						print("Do both models output the same tensors?"			,    "🔥" if success else "💩" )
						if not success:
												raise Exception("Something went wRoNg" )

						pathlib.Path(__lowerCamelCase ).mkdir(parents=__lowerCamelCase			,    exist_ok=__lowerCamelCase )
						print(f'''Saving model to {pytorch_dump_folder_path}''' )
						model.save_pretrained(__lowerCamelCase )


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











"""simple docstring"""

import math






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number == 0:
												__magic_name__      =  "Zero"
												return words
						else:
												__magic_name__      =  math.logaa(__lowerCamelCase )
												__magic_name__      =  digits + 1
												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "One"
												__magic_name__      =  "Two"
												__magic_name__      =  "Three"
												__magic_name__      =  "Four"
												__magic_name__      =  "Five"
												__magic_name__      =  "Six"
												__magic_name__      =  "Seven"
												__magic_name__      =  "Eight"
												__magic_name__      =  "Nine"

												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "Twenty"
												__magic_name__      =  "Thirty"
												__magic_name__      =  "Forty"
												__magic_name__      =  "Fifty"
												__magic_name__      =  "Sixty"
												__magic_name__      =  "Seventy"
												__magic_name__      =  "Eighty"
												__magic_name__      =  "Ninety"

												__magic_name__      =  {}
												__magic_name__      =  "Ten"
												__magic_name__      =  "Eleven"
												__magic_name__      =  "Twelve"
												__magic_name__      =  "Thirteen"
												__magic_name__      =  "Fourteen"
												__magic_name__      =  "Fifteen"
												__magic_name__      =  "Sixteen"
												__magic_name__      =  "Seventeen"
												__magic_name__      =  "Eighteen"
												__magic_name__      =  "Nineteen"

												__magic_name__      =  {}
												__magic_name__      =  "Hundred,"
												__magic_name__      =  "Thousand,"
												__magic_name__      =  "Lakh,"
												__magic_name__      =  "Crore,"

												__magic_name__      =  number
												__magic_name__      =  ""
												__magic_name__      =  0
												__magic_name__      =  int(__lowerCamelCase )
												while counter < digits:
																		__magic_name__      =  temp_num % 1_0
																		if counter % 2 == 0:
																								__magic_name__      =  ""
																								if counter in placevalue and current != 0:
																														__magic_name__      =  placevalue[counter]
																								if counter == 2:
																														__magic_name__      =  singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words

																								else:
																														__magic_name__      =  doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				__magic_name__      =  teens[number % 1_0] + words
																														else:
																																				__magic_name__      =  ""
																																				if counter in placevalue:
																																										__magic_name__      =  placevalue[counter]
																																				__magic_name__      =  doubles[current] + addition + words
																								else:
																														__magic_name__      =  ""
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										__magic_name__      =  ""
																																				else:
																																										__magic_name__      =  placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words
																		counter += 1
																		__magic_name__      =  temp_num // 1_0
						return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()



