


"""simple docstring"""
import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
				raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
				raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:      int						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      'The Nymphenburg Palace is a beautiful palace in Munich!'







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						{
							    """attention_cell""": """multi_head""",
							    """num_layers""": 4,
							    """units""": 1_024,
							    """hidden_size""": 768,
							    """max_length""": 512,
							    """num_heads""": 8,
							    """scaled""": True,
							    """dropout""": 0.1,
							    """use_residual""": True,
							    """embed_size""": 1_024,
							    """embed_dropout""": 0.1,
							    """word_embed""": None,
							    """layer_norm_eps""": 1E-5,
							    """token_type_vocab_size""": 2,
							}

							snake_case_          =						bort_4_8_768_1024_hparams

							# Let's construct the original Bort model here
							# Taken from official BERT implementation, see:
							# https://github.com/alexa/bort/blob/master/bort/bort.py
							snake_case_          =						BERTEncoder(
							    attention_cell=predefined_args["""attention_cell"""]			,	num_layers=predefined_args["""num_layers"""]			,	units=predefined_args["""units"""]			,	hidden_size=predefined_args["""hidden_size"""]			,	max_length=predefined_args["""max_length"""]			,	num_heads=predefined_args["""num_heads"""]			,	scaled=predefined_args["""scaled"""]			,	dropout=predefined_args["""dropout"""]			,	output_attention=_SCREAMING_SNAKE_CASE			,	output_all_encodings=_SCREAMING_SNAKE_CASE			,	use_residual=predefined_args["""use_residual"""]			,	activation=predefined_args.get("""activation"""			,	"""gelu"""			)			,	layer_norm_eps=predefined_args.get("""layer_norm_eps"""			,	_SCREAMING_SNAKE_CASE			)			,	)

							# Vocab information needs to be fetched first
							# It's the same as RoBERTa, so RobertaTokenizer can be used later
							snake_case_          =						"""openwebtext_ccnews_stories_books_cased"""

							# Specify download folder to Gluonnlp's vocab
							snake_case_          =						os.path.join(get_home_dir()			,	"""models"""			)
							snake_case_          =						_load_vocab(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	cls=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						nlp.model.BERTModel(
							    _SCREAMING_SNAKE_CASE			,	len(_SCREAMING_SNAKE_CASE			)			,	units=predefined_args["""units"""]			,	embed_size=predefined_args["""embed_size"""]			,	embed_dropout=predefined_args["""embed_dropout"""]			,	word_embed=predefined_args["""word_embed"""]			,	use_pooler=_SCREAMING_SNAKE_CASE			,	use_token_type_embed=_SCREAMING_SNAKE_CASE			,	token_type_vocab_size=predefined_args["""token_type_vocab_size"""]			,	use_classifier=_SCREAMING_SNAKE_CASE			,	use_decoder=_SCREAMING_SNAKE_CASE			,	)

							original_bort.load_parameters(_SCREAMING_SNAKE_CASE			,	cast_dtype=_SCREAMING_SNAKE_CASE			,	ignore_extra=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						original_bort._collect_params_with_prefix()

							# Build our config 🤗
							snake_case_          =						{
							    """architectures""": ["""BertForMaskedLM"""],
							    """attention_probs_dropout_prob""": predefined_args["""dropout"""],
							    """hidden_act""": """gelu""",
							    """hidden_dropout_prob""": predefined_args["""dropout"""],
							    """hidden_size""": predefined_args["""embed_size"""],
							    """initializer_range""": 0.02,
							    """intermediate_size""": predefined_args["""hidden_size"""],
							    """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
							    """max_position_embeddings""": predefined_args["""max_length"""],
							    """model_type""": """bort""",
							    """num_attention_heads""": predefined_args["""num_heads"""],
							    """num_hidden_layers""": predefined_args["""num_layers"""],
							    """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
							    """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
							    """vocab_size""": len(_SCREAMING_SNAKE_CASE			),
							}

							snake_case_          =						BertConfig.from_dict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						BertForMaskedLM(_SCREAMING_SNAKE_CASE			)
							hf_bort_model.eval()

							# Parameter mapping table (Gluonnlp to Transformers)
							# * denotes layer index
							#
							# | Gluon Parameter                                                | Transformers Parameter
							# | -------------------------------------------------------------- | ----------------------
							# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
							# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
							# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
							# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
							# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
							# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
							# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

							# Helper function to convert MXNET Arrays to PyTorch
							def to_torch(_SCREAMING_SNAKE_CASE			) -> nn.Parameter:
														return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()			)			)

							# Check param shapes and map new HF param back
							def check_and_map_params(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														snake_case_          =						hf_param.shape

														snake_case_          =						to_torch(params[gluon_param]			)
														snake_case_          =						gluon_param.shape

														assert (
														    shape_hf == shape_gluon
														), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

														return gluon_param

							snake_case_          =						check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight			,	"""word_embed.0.weight"""			)
							snake_case_          =						check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight			,	"""encoder.position_weight"""			)
							snake_case_          =						check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias			,	"""encoder.layer_norm.beta"""			)
							snake_case_          =						check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.weight			,	"""encoder.layer_norm.gamma"""			)

							# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
							snake_case_          =						torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

							for i in range(hf_bort_config.num_hidden_layers			):
														snake_case_          =						hf_bort_model.bert.encoder.layer[i]

														# self attention
														snake_case_          =						layer.attention.self

														snake_case_          =						check_and_map_params(
														    self_attn.key.bias.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""			)

														snake_case_          =						check_and_map_params(
														    self_attn.key.weight.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""			)
														snake_case_          =						check_and_map_params(
														    self_attn.query.bias.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""			)
														snake_case_          =						check_and_map_params(
														    self_attn.query.weight.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""			)
														snake_case_          =						check_and_map_params(
														    self_attn.value.bias.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""			)
														snake_case_          =						check_and_map_params(
														    self_attn.value.weight.data			,	f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""			)

														# self attention output
														snake_case_          =						layer.attention.output

														snake_case_          =						check_and_map_params(
														    self_output.dense.bias			,	f"""encoder.transformer_cells.{i}.proj.bias"""			)
														snake_case_          =						check_and_map_params(
														    self_output.dense.weight			,	f"""encoder.transformer_cells.{i}.proj.weight"""			)
														snake_case_          =						check_and_map_params(
														    self_output.LayerNorm.bias			,	f"""encoder.transformer_cells.{i}.layer_norm.beta"""			)
														snake_case_          =						check_and_map_params(
														    self_output.LayerNorm.weight			,	f"""encoder.transformer_cells.{i}.layer_norm.gamma"""			)

														# intermediate
														snake_case_          =						layer.intermediate

														snake_case_          =						check_and_map_params(
														    intermediate.dense.bias			,	f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""			)
														snake_case_          =						check_and_map_params(
														    intermediate.dense.weight			,	f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""			)

														# output
														snake_case_          =						layer.output

														snake_case_          =						check_and_map_params(
														    bert_output.dense.bias			,	f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""			)
														snake_case_          =						check_and_map_params(
														    bert_output.dense.weight			,	f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""			)
														snake_case_          =						check_and_map_params(
														    bert_output.LayerNorm.bias			,	f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""			)
														snake_case_          =						check_and_map_params(
														    bert_output.LayerNorm.weight			,	f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""			)

							# Save space and energy 🎄
							hf_bort_model.half()

							# Compare output of both models
							snake_case_          =						RobertaTokenizer.from_pretrained("""roberta-base"""			)

							snake_case_          =						tokenizer.encode_plus(_SCREAMING_SNAKE_CASE			)["""input_ids"""]

							# Get gluon output
							snake_case_          =						mx.nd.array([input_ids]			)
							snake_case_          =						original_bort(inputs=_SCREAMING_SNAKE_CASE			,	token_types=[]			)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						BertModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
							hf_bort_model.eval()

							snake_case_          =						tokenizer.encode_plus(_SCREAMING_SNAKE_CASE			,	return_tensors="""pt"""			)
							snake_case_          =						hf_bort_model(**_SCREAMING_SNAKE_CASE			)[0]

							snake_case_          =						output_gluon[0].asnumpy()
							snake_case_          =						output_hf[0].detach().numpy()

							snake_case_          =						np.max(np.abs(hf_layer - gluon_layer			)			).item()
							snake_case_          =						np.allclose(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	atol=1E-3			)

							if success:
														print("""✔️ Both model do output the same tensors"""			)
							else:
														print("""❌ Both model do **NOT** output the same tensors"""			)
														print("""Absolute difference is:"""			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      int						=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				__SCREAMING_SNAKE_CASE							:      str						=      parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list[int]:
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			)
							for i in range(_SCREAMING_SNAKE_CASE			):
														for j in range(i + 1			,	_SCREAMING_SNAKE_CASE			):
																					if numbers[j] < numbers[i]:
																												snake_case_    ,  snake_case_          =						numbers[j], numbers[i]
							return numbers


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      input('Enter numbers separated by a comma:\n').strip()
				__SCREAMING_SNAKE_CASE							:      List[str]						=      [int(item) for item in user_input.split(',')]
				print(exchange_sort(unsorted))



