





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

_snake_case     :		Dict       						= 'The Nymphenburg Palace is a beautiful palace in Munich!'






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  {
							    "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,
							}

							A		=  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
							A		=  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=UpperCamelCase					,  output_all_encodings=UpperCamelCase					,  use_residual=predefined_args["use_residual"]					,  activation=predefined_args.get("activation"					,  "gelu"       )					,  layer_norm_eps=predefined_args.get("layer_norm_eps"					,  UpperCamelCase       )					,  )

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

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

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

							original_bort.load_parameters(UpperCamelCase					,  cast_dtype=UpperCamelCase					,  ignore_extra=UpperCamelCase       )
							A		=  original_bort._collect_params_with_prefix()

							# Build our config 🤗
							A		=  {
							    "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(UpperCamelCase       ),
							}

							A		=  BertConfig.from_dict(UpperCamelCase       )
							A		=  BertForMaskedLM(UpperCamelCase       )
							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(UpperCamelCase       ) -> 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(UpperCamelCase					,  UpperCamelCase       ):
														A		=  hf_param.shape

														A		=  to_torch(params[gluon_param]       )
														A		=  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

							A		=  check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight					,  "word_embed.0.weight"       )
							A		=  check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight					,  "encoder.position_weight"       )
							A		=  check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias					,  "encoder.layer_norm.beta"       )
							A		=  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)
							A		=  torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

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

														# self attention
														A		=  layer.attention.self

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

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

														# self attention output
														A		=  layer.attention.output

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

														# intermediate
														A		=  layer.intermediate

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

														# output
														A		=  layer.output

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

							A		=  tokenizer.encode_plus(UpperCamelCase       )["input_ids"]

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

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

							A		=  tokenizer.encode_plus(UpperCamelCase					,  return_tensors="pt"       )
							A		=  hf_bort_model(**UpperCamelCase       )[0]

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

							A		=  np.max(np.abs(hf_layer - gluon_layer       )       ).item()
							A		=  np.allclose(UpperCamelCase					,  UpperCamelCase					,  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:"					,  UpperCamelCase       )


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









"""simple docstring"""
def 					A__				(			UpperCamelCase       ):
							A		=  generate_pascal_triangle(UpperCamelCase       )
							for row_idx in range(UpperCamelCase       ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1       ):
																					print(end=" "       )
														# Print row values
														for col_idx in range(row_idx + 1       ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]					,  end=" "       )
																					else:
																												print(triangle[row_idx][col_idx]					,  end=""       )
														print()






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  []
							for current_row_idx in range(UpperCamelCase       ):
														A		=  populate_current_row(UpperCamelCase					,  UpperCamelCase       )
														triangle.append(UpperCamelCase       )
							return triangle






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							A,	A		=  1, 1
							for current_col_idx in range(1					,  UpperCamelCase       ):
														calculate_current_element(
														    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
							return current_row






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  ):
							A		=  triangle[current_row_idx - 1][current_col_idx - 1]
							A		=  triangle[current_row_idx - 1][current_col_idx]
							A		=  above_to_left_elt + above_to_right_elt






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  [[1]]

							for row_index in range(1					,  UpperCamelCase       ):
														A		=  [0] + result[-1] + [0]
														A		=  row_index + 1
														# Calculate the number of distinct elements in a row
														A		=  sum(divmod(UpperCamelCase					,  2       )       )
														A		=  [
														    temp_row[i - 1] + temp_row[i] for i in range(1					,  distinct_elements + 1       )
														]
														A		=  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														A		=  row_first_half + row_second_half
														result.append(UpperCamelCase       )

							return result






def 					A__				(			):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(UpperCamelCase					,  UpperCamelCase       ) -> None:
														A		=  F"{func.__name__}({value})"
														A		=  timeit(F"__main__.{call}"					,  setup="import __main__"       )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F"{call:38} -- {timing:.4f} seconds"       )

							for value in range(15       ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(UpperCamelCase					,  UpperCamelCase       )
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()


