



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__			=		logging.get_logger(__name__)

a__			=		"""The Nymphenburg Palace is a beautiful palace in Munich!"""



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		List[Any] )     ->   List[str]:
 _snake_case			:     Dict 		=    {
     """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,
 }

 _snake_case			:     Optional[int] 		=    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			:     List[str] 		=    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=snake_case__ ,				output_all_encodings=snake_case__ ,				use_residual=predefined_args["""use_residual"""] ,				activation=predefined_args.get("""activation""" ,				"""gelu""" ) ,				layer_norm_eps=predefined_args.get("""layer_norm_eps""" ,				snake_case__ ) ,				)

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

 # Specify download folder to Gluonnlp's vocab
 _snake_case			:     Union[str, Any] 		=    os.path.join(get_home_dir() ,				"""models""" )
 _snake_case			:     Optional[int] 		=    _load_vocab(snake_case__ ,				snake_case__ ,				snake_case__ ,				cls=snake_case__ )

 _snake_case			:     int 		=    nlp.model.BERTModel(
     snake_case__ ,				len(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=snake_case__ ,				use_token_type_embed=snake_case__ ,				token_type_vocab_size=predefined_args["""token_type_vocab_size"""] ,				use_classifier=snake_case__ ,				use_decoder=snake_case__ ,				)

 original_bort.load_parameters(snake_case__ ,				cast_dtype=snake_case__ ,				ignore_extra=snake_case__ )
 _snake_case			:     Dict 		=    original_bort._collect_params_with_prefix()

 # Build our config 🤗
 _snake_case			:     str 		=    {
     """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(snake_case__ ),
 }

 _snake_case			:     Union[str, Any] 		=    BertConfig.from_dict(snake_case__ )
 _snake_case			:     List[Any] 		=    BertForMaskedLM(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__				:		int ) -> 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__				:		Dict ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] ):
  _snake_case			:     List[Any] 		=    hf_param.shape

  _snake_case			:     Any 		=    to_torch(params[gluon_param] )
  _snake_case			:     List[Any] 		=    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			:     Optional[Any] 		=    check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight ,				"""word_embed.0.weight""" )
 _snake_case			:     Dict 		=    check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight ,				"""encoder.position_weight""" )
 _snake_case			:     Optional[Any] 		=    check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias ,				"""encoder.layer_norm.beta""" )
 _snake_case			:     List[Any] 		=    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			:     Optional[Any] 		=    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			:     str 		=    hf_bort_model.bert.encoder.layer[i]

  # self attention
  _snake_case			:     List[Any] 		=    layer.attention.self

  _snake_case			:     Tuple 		=    check_and_map_params(
      self_attn.key.bias.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )

  _snake_case			:     str 		=    check_and_map_params(
      self_attn.key.weight.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
  _snake_case			:     Optional[int] 		=    check_and_map_params(
      self_attn.query.bias.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
  _snake_case			:     Optional[Any] 		=    check_and_map_params(
      self_attn.query.weight.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
  _snake_case			:     List[str] 		=    check_and_map_params(
      self_attn.value.bias.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
  _snake_case			:     List[Any] 		=    check_and_map_params(
      self_attn.value.weight.data ,				F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )

  # self attention output
  _snake_case			:     Optional[Any] 		=    layer.attention.output

  _snake_case			:     List[Any] 		=    check_and_map_params(
      self_output.dense.bias ,				F'''encoder.transformer_cells.{i}.proj.bias''' )
  _snake_case			:     str 		=    check_and_map_params(
      self_output.dense.weight ,				F'''encoder.transformer_cells.{i}.proj.weight''' )
  _snake_case			:     int 		=    check_and_map_params(
      self_output.LayerNorm.bias ,				F'''encoder.transformer_cells.{i}.layer_norm.beta''' )
  _snake_case			:     Tuple 		=    check_and_map_params(
      self_output.LayerNorm.weight ,				F'''encoder.transformer_cells.{i}.layer_norm.gamma''' )

  # intermediate
  _snake_case			:     str 		=    layer.intermediate

  _snake_case			:     List[Any] 		=    check_and_map_params(
      intermediate.dense.bias ,				F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
  _snake_case			:     List[str] 		=    check_and_map_params(
      intermediate.dense.weight ,				F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )

  # output
  _snake_case			:     Any 		=    layer.output

  _snake_case			:     Dict 		=    check_and_map_params(
      bert_output.dense.bias ,				F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
  _snake_case			:     Any 		=    check_and_map_params(
      bert_output.dense.weight ,				F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
  _snake_case			:     List[str] 		=    check_and_map_params(
      bert_output.LayerNorm.bias ,				F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
  _snake_case			:     List[Any] 		=    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			:     str 		=    RobertaTokenizer.from_pretrained("""roberta-base""" )

 _snake_case			:     Optional[int] 		=    tokenizer.encode_plus(snake_case__ )["""input_ids"""]

 # Get gluon output
 _snake_case			:     Tuple 		=    mx.nd.array([input_ids] )
 _snake_case			:     Optional[Any] 		=    original_bort(inputs=snake_case__ ,				token_types=[] )

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(snake_case__ )
 _snake_case			:     List[Any] 		=    BertModel.from_pretrained(snake_case__ )
 hf_bort_model.eval()

 _snake_case			:     str 		=    tokenizer.encode_plus(snake_case__ ,				return_tensors="""pt""" )
 _snake_case			:     Tuple 		=    hf_bort_model(**snake_case__ )[0]

 _snake_case			:     Any 		=    output_gluon[0].asnumpy()
 _snake_case			:     Tuple 		=    output_hf[0].detach().numpy()

 _snake_case			:     int 		=    np.max(np.abs(hf_layer - gluon_layer ) ).item()
 _snake_case			:     List[Any] 		=    np.allclose(snake_case__ ,				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:""" ,				snake_case__ )


if __name__ == "__main__":
  a__			=		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__			=		parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					spectrogram(
							    _a		, window_function(self.n_fft		, """hann""" )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel="""dB"""		, db_range=80.0		, )
							lowerCamelCase			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					np.clip(log_spec / 40.0		, -2.0		, 0.0 ) + 1.0
							return log_spec





				def __call__(       self		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""simple docstring"""

							if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
													raise ValueError(
													    """This feature extractor is set to support sampling rate"""
													    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
													    f' with {self.sampling_rate} and not {sampling_rate}.' )
							else:
										logger.warning(
										    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
										    """Failing to do so can result in silent errors that might be hard to debug.""" )

							lowerCamelCase			      =					isinstance(_a		, np.ndarray ) and len(raw_speech.shape ) > 1
							if is_batched_numpy and len(raw_speech.shape ) > 2:
										raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
							lowerCamelCase			      =					is_batched_numpy or (
							    isinstance(_a		, (list, tuple) ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list) ))
							)
							if is_batched:
										lowerCamelCase			      =					[np.asarray([speech]		, dtype=np.floataa ).T for speech in raw_speech]
							elif not is_batched and not isinstance(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
										lowerCamelCase			      =					raw_speech.astype(np.floataa )
							# always return batch
							if not is_batched:
										lowerCamelCase			      =					[np.asarray([raw_speech] ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							lowerCamelCase			      =					[
							    self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]		, _a ):
										lowerCamelCase			      =					[np.asarray(_a		, dtype=np.floataa ) for feature in audio_features]

							# Create audio attention mask
							lowerCamelCase			      =					max(
							    [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
							if return_attention_mask:
										lowerCamelCase			      =					[
										    (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
										    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
										    for feature in audio_features
										]
										lowerCamelCase			      =					np.array(_a ).astype(np.floataa )

							# convert into correct format for padding
							lowerCamelCase			      =					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							lowerCamelCase			      =					np.ones([len(_a ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
							lowerCamelCase			      =					padded_audio_features * self.padding_value
							for i in range(len(_a ) ):
										lowerCamelCase			      =					audio_features[i]
										lowerCamelCase			      =					feature

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



