


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

A	:		Optional[int]    =       'The Nymphenburg Palace is a beautiful palace in Munich!'






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  Dict:
		"""simple docstring"""




		lowercase__					      =		{
		    '''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,
		}

		lowercase__					      =		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
		lowercase__					      =		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
		lowercase__					      =		'''openwebtext_ccnews_stories_books_cased'''

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

		lowercase__					      =		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__					)
		lowercase__					      =		original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowercase__					      =		{
		    '''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(lowerCAmelCase__					),
		}

		lowercase__					      =		BertConfig.from_dict(lowerCAmelCase__					)
		lowercase__					      =		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(__magic_name__					:							List[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(__magic_name__					:							Optional[Any]   ,   __magic_name__					:							Union[str, Any]					):
				lowercase__					      =		hf_param.shape

				lowercase__					      =		to_torch(params[gluon_param]					)
				lowercase__					      =		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

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

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

				# self attention
				lowercase__					      =		layer.attention.self

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

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

				# self attention output
				lowercase__					      =		layer.attention.output

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

				# intermediate
				lowercase__					      =		layer.intermediate

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

				# output
				lowercase__					      =		layer.output

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

		lowercase__					      =		tokenizer.encode_plus(lowerCAmelCase__					)['''input_ids''']

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

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

		lowercase__					      =		tokenizer.encode_plus(lowerCAmelCase__   ,   return_tensors="""pt"""					)
		lowercase__					      =		hf_bort_model(**lowerCAmelCase__					)[0]

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

		lowercase__					      =		np.max(np.abs(hf_layer - gluon_layer					)					).item()
		lowercase__					      =		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__":
	A	:		Optional[int]    =       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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

