


"""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()
a__       :							Union[str, Any]        =  logging.get_logger(__name__)

a__       :							Any        =  '''The Nymphenburg Palace is a beautiful palace in Munich!'''




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       {
					    "attention_cell": "multi_head",
					    "num_layers": 4,
					    "units": 1024,
					    "hidden_size": 768,
					    "max_length": 512,
					    "num_heads": 8,
					    "scaled": True,
					    "dropout": 0.1,
					    "use_residual": True,
					    "embed_size": 1024,
					    "embed_dropout": 0.1,
					    "word_embed": None,
					    "layer_norm_eps": 1E-5,
					    "token_type_vocab_size": 2,
					}

					__SCREAMING_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
					__SCREAMING_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=lowerCAmelCase_						,		output_all_encodings=lowerCAmelCase_						,		use_residual=predefined_args["use_residual"]						,		activation=predefined_args.get("activation"						,		"gelu"     )						,		layer_norm_eps=predefined_args.get("layer_norm_eps"						,		lowerCAmelCase_     )						,		)

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

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

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

					original_bort.load_parameters(lowerCAmelCase_						,		cast_dtype=lowerCAmelCase_						,		ignore_extra=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       original_bort._collect_params_with_prefix()

					# Build our config 🤗
					__SCREAMING_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(lowerCAmelCase_     ),
					}

					__SCREAMING_SNAKE_CASE		     =       BertConfig.from_dict(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       BertForMaskedLM(lowerCAmelCase_     )
					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(lowerCAmelCase_     ) -> 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(lowerCAmelCase_						,		lowerCAmelCase_     ):
										__SCREAMING_SNAKE_CASE		     =       hf_param.shape

										__SCREAMING_SNAKE_CASE		     =       to_torch(params[gluon_param]     )
										__SCREAMING_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

					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.word_embeddings.weight						,		"word_embed.0.weight"     )
					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.position_embeddings.weight						,		"encoder.position_weight"     )
					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.LayerNorm.bias						,		"encoder.layer_norm.beta"     )
					__SCREAMING_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)
					__SCREAMING_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     ):
										__SCREAMING_SNAKE_CASE		     =       hf_bort_model.bert.encoder.layer[i]

										# self attention
										__SCREAMING_SNAKE_CASE		     =       layer.attention.self

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

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

										# self attention output
										__SCREAMING_SNAKE_CASE		     =       layer.attention.output

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

										# intermediate
										__SCREAMING_SNAKE_CASE		     =       layer.intermediate

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

										# output
										__SCREAMING_SNAKE_CASE		     =       layer.output

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.dense.bias						,		f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.dense.weight						,		f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.LayerNorm.bias						,		f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""     )
										__SCREAMING_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
					__SCREAMING_SNAKE_CASE		     =       RobertaTokenizer.from_pretrained("roberta-base"     )

					__SCREAMING_SNAKE_CASE		     =       tokenizer.encode_plus(lowerCAmelCase_     )["input_ids"]

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

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

					__SCREAMING_SNAKE_CASE		     =       tokenizer.encode_plus(lowerCAmelCase_						,		return_tensors="pt"     )
					__SCREAMING_SNAKE_CASE		     =       hf_bort_model(**lowerCAmelCase_     )[0]

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

					__SCREAMING_SNAKE_CASE		     =       np.max(np.abs(hf_layer - gluon_layer     )     ).item()
					__SCREAMING_SNAKE_CASE		     =       np.allclose(lowerCAmelCase_						,		lowerCAmelCase_						,		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:"						,		lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							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.'''
		)
		a__       :							List[str]        =  parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""







import functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
