'''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			:      int					      =				logging.get_logger(__name__)

_A			:      str					      =				'''The Nymphenburg Palace is a beautiful palace in Munich!'''







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Dict      )					->     Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						{
     """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,
 }

 __lowerCAmelCase											=						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
 __lowerCAmelCase											=						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
 __lowerCAmelCase											=						"""openwebtext_ccnews_stories_books_cased"""

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

 __lowerCAmelCase											=						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      )
 __lowerCAmelCase											=						original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __lowerCAmelCase											=						{
     """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""": 0.0_2,
     """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      ),
 }

 __lowerCAmelCase											=						BertConfig.from_dict(_UpperCAmelCase      )
 __lowerCAmelCase											=						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(snake_case_       :       str      ) -> 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(snake_case_       :       List[Any]						,		snake_case_       :       Dict      ):
  __lowerCAmelCase											=						hf_param.shape

  __lowerCAmelCase											=						to_torch(params[gluon_param]      )
  __lowerCAmelCase											=						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

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

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

  # self attention
  __lowerCAmelCase											=						layer.attention.self

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

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

  # self attention output
  __lowerCAmelCase											=						layer.attention.output

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

  # intermediate
  __lowerCAmelCase											=						layer.intermediate

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

  # output
  __lowerCAmelCase											=						layer.output

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

 __lowerCAmelCase											=						tokenizer.encode_plus(_UpperCAmelCase      )["""input_ids"""]

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

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

 __lowerCAmelCase											=						tokenizer.encode_plus(_UpperCAmelCase						,		return_tensors="""pt"""      )
 __lowerCAmelCase											=						hf_bort_model(**_UpperCAmelCase      )[0]

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

 __lowerCAmelCase											=						np.max(np.abs(hf_layer - gluon_layer      )      ).item()
 __lowerCAmelCase											=						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__":
 _A			:      Optional[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.'''
 )
 _A			:      Union[str, Any]					      =				parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__snake_case						:List[str]      		=     '''\
    Text data.
    Second line of data.'''

__snake_case						:Optional[Any]      		=     '''file'''





@pytest.fixture(scope='''session'''				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / (FILE_PATH + '''.zstd''')
    __a						       =				bytes(_UpperCAmelCase   , '''utf-8'''				)
    with zstd.open(_UpperCAmelCase   , '''wb'''				) as f:
        f.write(_UpperCAmelCase				)
    return path





@pytest.fixture
def      __snake_case	(			_UpperCAmelCase				):
    with open(os.path.join(tmpfs.local_root_dir   , _UpperCAmelCase				)   , '''w'''				) as f:
        f.write(_UpperCAmelCase				)
    return FILE_PATH





@pytest.mark.parametrize('''compression_format'''   , ['''gzip''', '''xz''', '''zstd''']				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
    __a						       =				input_paths[compression_format]
    __a						       =				tmp_path / '''cache'''
    __a						       =				DownloadConfig(cache_dir=_UpperCAmelCase   , extract_compressed_file=_UpperCAmelCase				)
    __a						       =				cached_path(_UpperCAmelCase   , download_config=_UpperCAmelCase				)
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('''default_extracted'''   , [True, False]				)
@pytest.mark.parametrize('''default_cache_dir'''   , [True, False]				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				'''custom_cache'''
    __a						       =				'''custom_extracted_dir'''
    __a						       =				tmp_path / '''custom_extracted_path'''
    if default_extracted:
        __a						       =				('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
    else:
        monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''   , _UpperCAmelCase				)
        monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''   , str(_UpperCAmelCase				)				)
        __a						       =				custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

    __a						       =				xz_file
    __a						       =				(
        DownloadConfig(extract_compressed_file=_UpperCAmelCase				)
        if default_cache_dir
        else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   , extract_compressed_file=_UpperCAmelCase				)
    )
    __a						       =				cached_path(_UpperCAmelCase   , download_config=_UpperCAmelCase				)
    assert Path(_UpperCAmelCase				).parent.parts[-2:] == expected





def      __snake_case	(			_UpperCAmelCase				):
    # absolute path
    __a						       =				str(Path(_UpperCAmelCase				).resolve()				)
    assert cached_path(_UpperCAmelCase				) == text_file
    # relative path
    __a						       =				str(Path(_UpperCAmelCase				).resolve().relative_to(Path(os.getcwd()				)				)				)
    assert cached_path(_UpperCAmelCase				) == text_file





def      __snake_case	(			_UpperCAmelCase				):
    # absolute path
    __a						       =				str(tmp_path.resolve() / '''__missing_file__.txt'''				)
    with pytest.raises(_UpperCAmelCase				):
        cached_path(_UpperCAmelCase				)
    # relative path
    __a						       =				'''./__missing_file__.txt'''
    with pytest.raises(_UpperCAmelCase				):
        cached_path(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				get_from_cache(f'tmp://{tmpfs_file}'				)
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    assert output_file_content == FILE_CONTENT





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			):
    with pytest.raises(_UpperCAmelCase				):
        cached_path('''https://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        http_get('''https://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        http_head('''https://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        ftp_get('''ftp://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        ftp_head('''ftp://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        fsspec_get('''s3://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        fsspec_head('''s3://huggingface.co'''				)





