





'''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()
__magic_name__   :						Any    =			logging.get_logger(__name__)

__magic_name__   :						List[Any]    =			"""The Nymphenburg Palace is a beautiful palace in Munich!"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				{
      "attention_cell": "multi_head",
      "num_layers": 4,
      "units": 10_24,
      "hidden_size": 7_68,
      "max_length": 5_12,
      "num_heads": 8,
      "scaled": True,
      "dropout": 0.1,
      "use_residual": True,
      "embed_size": 10_24,
      "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__":
 __magic_name__   :						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."""
 )
 __magic_name__   :						Optional[int]    =			parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						str    =			[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
    _snake_case     		=				[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      _snake_case     		=				0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      _snake_case     		=				cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(SCREAMING_SNAKE_CASE__       )
  return next_generation




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    # Create output image
    _snake_case     		=				Image.new("RGB"					,	(len(cells[0]       ), len(SCREAMING_SNAKE_CASE__       ))       )
    _snake_case     		=				img.load()

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])


