"""simple docstring"""




from typing import Any

import numpy as np







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return np.array_equal(lowerCamelCase__			,  matrix.conjugate().T     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   v.conjugate().T
		lowerCAmelCase__        =   v_star.dot(lowerCamelCase__     )
		assert isinstance(lowerCamelCase__			,  np.ndarray     )
		return (v_star_dot.dot(lowerCamelCase__     )) / (v_star.dot(lowerCamelCase__     ))







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]     )
		lowerCAmelCase__        =   np.array([[1], [2], [3]]     )
		assert is_hermitian(lowerCamelCase__     ), f"""{a} is not hermitian."""
		print(rayleigh_quotient(lowerCamelCase__			,  lowerCamelCase__     )     )

		lowerCAmelCase__        =   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]     )
		assert is_hermitian(lowerCamelCase__     ), f"""{a} is not hermitian."""
		assert rayleigh_quotient(lowerCamelCase__			,  lowerCamelCase__     ) == float(3     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			tests()





"""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)




