"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase    :							Dict													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Dict													=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__lowerCAmelCase    :							Dict													=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		for attribute in key.split("""."""     ):
				lowerCAmelCase__        =   getattr(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE     )

		if weight_type is not None:
				lowerCAmelCase__        =   getattr(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE     ).shape
		else:
				lowerCAmelCase__        =   hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    f""" {value.shape} for {full_name}"""     )

		if weight_type == "weight":
				lowerCAmelCase__        =   value
		elif weight_type == "weight_g":
				lowerCAmelCase__        =   value
		elif weight_type == "weight_v":
				lowerCAmelCase__        =   value
		elif weight_type == "bias":
				lowerCAmelCase__        =   value
		elif weight_type == "running_mean":
				lowerCAmelCase__        =   value
		elif weight_type == "running_var":
				lowerCAmelCase__        =   value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase__        =   value
		elif weight_type == "inv_freq":
				lowerCAmelCase__        =   value
		else:
				lowerCAmelCase__        =   value

		logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		lowerCAmelCase__        =   fairseq_model.state_dict()

		lowerCAmelCase__        =   hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCAmelCase__        =   False
				if "conv_layers" in name:
						load_conv_layer(
						    _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  hf_model.config.feat_extract_norm == """group"""			,  )
						lowerCAmelCase__        =   True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCAmelCase__        =   """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0]:
										lowerCAmelCase__        =   True
										if "*" in mapped_key:
												lowerCAmelCase__        =   name.split(_SCREAMING_SNAKE_CASE     )[0].split("""."""     )[-2]
												lowerCAmelCase__        =   mapped_key.replace("""*"""			,  _SCREAMING_SNAKE_CASE     )
										if "pos_bias_u" in name:
												lowerCAmelCase__        =   None
										elif "pos_bias_v" in name:
												lowerCAmelCase__        =   None
										elif "weight_g" in name:
												lowerCAmelCase__        =   """weight_g"""
										elif "weight_v" in name:
												lowerCAmelCase__        =   """weight_v"""
										elif "bias" in name:
												lowerCAmelCase__        =   """bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCAmelCase__        =   """weight"""
										elif "running_mean" in name:
												lowerCAmelCase__        =   """running_mean"""
										elif "inv_freq" in name:
												lowerCAmelCase__        =   """inv_freq"""
										elif "running_var" in name:
												lowerCAmelCase__        =   """running_var"""
										elif "num_batches_tracked" in name:
												lowerCAmelCase__        =   """num_batches_tracked"""
										else:
												lowerCAmelCase__        =   None
										set_recursively(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE     )
								continue
				if not is_used:
						unused_weights.append(_SCREAMING_SNAKE_CASE     )

		logger.warning(f"""Unused weights: {unused_weights}"""     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   full_name.split("""conv_layers."""     )[-1]
		lowerCAmelCase__        =   name.split("""."""     )
		lowerCAmelCase__        =   int(items[0]     )
		lowerCAmelCase__        =   int(items[1]     )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
						lowerCAmelCase__        =   value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
						lowerCAmelCase__        =   value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
						lowerCAmelCase__        =   value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
						lowerCAmelCase__        =   value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
		else:
				unused_weights.append(_SCREAMING_SNAKE_CASE     )







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=True     ):





		"""simple docstring"""




		if config_path is not None:
				lowerCAmelCase__        =   WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,  hidden_act="""swish"""     )
		else:
				lowerCAmelCase__        =   WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowerCAmelCase__        =   """rotary"""

		if is_finetuned:
				if dict_path:
						lowerCAmelCase__        =   Dictionary.load(_SCREAMING_SNAKE_CASE     )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase__        =   target_dict.pad_index
						lowerCAmelCase__        =   target_dict.bos_index
						lowerCAmelCase__        =   target_dict.eos_index
						lowerCAmelCase__        =   len(target_dict.symbols     )
						lowerCAmelCase__        =   os.path.join(_SCREAMING_SNAKE_CASE			,  """vocab.json"""     )
						if not os.path.isdir(_SCREAMING_SNAKE_CASE     ):
								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE     )     )
								return
						os.makedirs(_SCREAMING_SNAKE_CASE			,  exist_ok=_SCREAMING_SNAKE_CASE     )
						lowerCAmelCase__        =   target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowerCAmelCase__        =   0
						lowerCAmelCase__        =   1
						with open(_SCREAMING_SNAKE_CASE			,  """w"""			,  encoding="""utf-8"""     ) as vocab_handle:
								json.dump(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE     )
						lowerCAmelCase__        =   WavaVecaCTCTokenizer(
						    _SCREAMING_SNAKE_CASE			,  unk_token=target_dict.unk_word			,  pad_token=target_dict.pad_word			,  bos_token=target_dict.bos_word			,  eos_token=target_dict.eos_word			,  word_delimiter_token="""|"""			,  do_lower_case=_SCREAMING_SNAKE_CASE			,  )
						lowerCAmelCase__        =   True if config.feat_extract_norm == """layer""" else False
						lowerCAmelCase__        =   WavaVecaFeatureExtractor(
						    feature_size=1			,  sampling_rate=1_6000			,  padding_value=0			,  do_normalize=_SCREAMING_SNAKE_CASE			,  return_attention_mask=_SCREAMING_SNAKE_CASE			,  )
						lowerCAmelCase__        =   WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE			,  tokenizer=_SCREAMING_SNAKE_CASE     )
						processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

				lowerCAmelCase__        =   WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE     )
		else:
				lowerCAmelCase__        =   WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE     )

		if is_finetuned:
				lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]			,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     )}     )
		else:
				lowerCAmelCase__        =   argparse.Namespace(task="""audio_pretraining"""     )
				lowerCAmelCase__        =   fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE     )

				lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,  task=_SCREAMING_SNAKE_CASE     )

		lowerCAmelCase__        =   model[0].eval()

		recursively_load_weights(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  not is_finetuned     )

		hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Dict													=						argparse.ArgumentParser()
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
			parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument(
			    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
			)
			__lowerCAmelCase    :							Optional[Any]													=						parser.parse_args()
			convert_wavaveca_conformer_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





"""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()
__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Any													=						"The Nymphenburg Palace is a beautiful palace in Munich!"







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {
		    """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,
		}

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

		# 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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  cls=lowerCamelCase__     )

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

		original_bort.load_parameters(lowerCamelCase__			,  cast_dtype=lowerCamelCase__			,  ignore_extra=lowerCamelCase__     )
		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""": 0.02,
		    """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(lowerCamelCase__     ),
		}

		lowerCAmelCase__        =   BertConfig.from_dict(lowerCamelCase__     )
		lowerCAmelCase__        =   BertForMaskedLM(lowerCamelCase__     )
		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(lowerCamelCase__     ) -> 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(lowerCamelCase__			,  lowerCamelCase__     ):
				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(lowerCamelCase__     )["""input_ids"""]

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

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

		lowerCAmelCase__        =   tokenizer.encode_plus(lowerCamelCase__			,  return_tensors="""pt"""     )
		lowerCAmelCase__        =   hf_bort_model(**lowerCamelCase__     )[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(lowerCamelCase__			,  lowerCamelCase__			,  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:"""			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, 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    :							str													=						parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




