
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()
__lowerCAmelCase			:     Tuple           =   logging.get_logger(__name__)

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

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       {
      """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,
  }

  __magic_name__				     =       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
  __magic_name__				     =       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=A_,     output_all_encodings=A_,     use_residual=predefined_args["""use_residual"""],     activation=predefined_args.get("""activation""",     """gelu"""					),     layer_norm_eps=predefined_args.get("""layer_norm_eps""",     A_					),     )

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

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

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

  original_bort.load_parameters(A_,     cast_dtype=A_,     ignore_extra=A_					)
  __magic_name__				     =       original_bort._collect_params_with_prefix()

  # Build our config 🤗
  __magic_name__				     =       {
      """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(A_					),
  }

  __magic_name__				     =       BertConfig.from_dict(A_					)
  __magic_name__				     =       BertForMaskedLM(A_					)
  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(A_					) -> 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(A_,     A_					):
    __magic_name__				     =       hf_param.shape

    __magic_name__				     =       to_torch(params[gluon_param]					)
    __magic_name__				     =       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

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

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

    # self attention
    __magic_name__				     =       layer.attention.self

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

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

    # self attention output
    __magic_name__				     =       layer.attention.output

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

    # intermediate
    __magic_name__				     =       layer.intermediate

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

    # output
    __magic_name__				     =       layer.output

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

  __magic_name__				     =       tokenizer.encode_plus(A_					)["""input_ids"""]

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

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

  __magic_name__				     =       tokenizer.encode_plus(A_,     return_tensors="""pt"""					)
  __magic_name__				     =       hf_bort_model(**A_					)[0]

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

  __magic_name__				     =       np.max(np.abs(hf_layer - gluon_layer					)					).item()
  __magic_name__				     =       np.allclose(A_,     A_,     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:""",     A_					)


if __name__ == "__main__":
    __lowerCAmelCase			:     Any           =   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.'
    )
    __lowerCAmelCase			:     Optional[int]           =   parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase			:     int           =   subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase			:     Any           =   (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)

__lowerCAmelCase			:     str           =   '|'.join(sys.argv[1:])
__lowerCAmelCase			:     Tuple           =   re.compile(RF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase			:     Union[str, Any]           =   [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')

