


from typing import Dict, Optional

import numpy as np

import datasets


lowerCamelCase						=		'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

lowerCamelCase						=		'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

lowerCamelCase						=		'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






		if label_map is not None:
				for old_id, new_id in label_map.items():
						lowerCAmelCase__       :      List[Any]   					= new_id

    # turn into Numpy arrays
		lowerCAmelCase__       :      Any   					= np.array(_a	)
		lowerCAmelCase__       :      List[Any]   					= np.array(_a	)

		if reduce_labels:
				lowerCAmelCase__       :      str   					= 255
				lowerCAmelCase__       :      int   					= label - 1
				lowerCAmelCase__       :      List[str]   					= 255

		lowerCAmelCase__       :      Union[str, Any]   					= label != ignore_index
		lowerCAmelCase__       :      Dict   					= np.not_equal(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= pred_label[mask]
		lowerCAmelCase__       :      List[Any]   					= np.array(_a	)[mask]

		lowerCAmelCase__       :      Dict   					= pred_label[pred_label == label]

		lowerCAmelCase__       :      List[Any]   					= np.histogram(_a     ,       bins=_a     ,       range=(0, num_labels - 1)	)[0]
		lowerCAmelCase__       :      Optional[Any]   					= np.histogram(_a     ,       bins=_a     ,       range=(0, num_labels - 1)	)[0]
		lowerCAmelCase__       :      Optional[Any]   					= np.histogram(_a     ,       bins=_a     ,       range=(0, num_labels - 1)	)[0]

		lowerCAmelCase__       :      Union[str, Any]   					= area_pred_label + area_label - area_intersect

		return area_intersect, area_union, area_pred_label, area_label






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      Dict   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      List[str]   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      Dict   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		for result, gt_seg_map in zip(_a     ,       _a	):
				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= intersect_and_union(
				    _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	)
				total_area_intersect += area_intersect
				total_area_union += area_union
				total_area_pred_label += area_pred_label
				total_area_label += area_label
		return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= total_intersect_and_union(
		    _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	)

		# compute metrics
		lowerCAmelCase__       :      Optional[Any]   					= {}

		lowerCAmelCase__       :      Optional[Any]   					= total_area_intersect.sum() / total_area_label.sum()
		lowerCAmelCase__       :      Tuple   					= total_area_intersect / total_area_union
		lowerCAmelCase__       :      Optional[Any]   					= total_area_intersect / total_area_label

		lowerCAmelCase__       :      Dict   					= np.nanmean(_a	)
		lowerCAmelCase__       :      int   					= np.nanmean(_a	)
		lowerCAmelCase__       :      Tuple   					= all_acc
		lowerCAmelCase__       :      str   					= iou
		lowerCAmelCase__       :      str   					= acc

		if nan_to_num is not None:
				lowerCAmelCase__       :      Tuple   					= {metric: np.nan_to_num(_a     ,       nan=_a	) for metric, metric_value in metrics.items()}

		return metrics





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION)
class  _a							(    datasets.Metric):




	def 							UpperCAmelCase__( self						:		List[str]		)->	Optional[Any]:
			return datasets.MetricInfo(
			    description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
			        # 1st Seq - height dim, 2nd - width dim
			        {
			            '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''		)		)		),
			            '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''		)		)		),
			        }		)		, reference_urls=[
			        '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
			    ]		, )




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		bool		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Dict[int, int]] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		, )->	int:
			lowerCAmelCase__       :      Union[str, Any]   					= mean_iou(
			    results=_SCREAMING_SNAKE_CASE		, gt_seg_maps=_SCREAMING_SNAKE_CASE		, num_labels=_SCREAMING_SNAKE_CASE		, ignore_index=_SCREAMING_SNAKE_CASE		, nan_to_num=_SCREAMING_SNAKE_CASE		, label_map=_SCREAMING_SNAKE_CASE		, reduce_labels=_SCREAMING_SNAKE_CASE		, )
			return iou_result








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						=		logging.get_logger(__name__)

lowerCamelCase						=		'''The Nymphenburg Palace is a beautiful palace in Munich!'''






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {
		    '''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,
		}

		lowerCAmelCase__       :      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
		lowerCAmelCase__       :      List[Any]   					= 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=_a     ,       output_all_encodings=_a     ,       use_residual=predefined_args['''use_residual''']     ,       activation=predefined_args.get('''activation'''     ,       '''gelu'''	)     ,       layer_norm_eps=predefined_args.get('''layer_norm_eps'''     ,       _a	)     ,       )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase__       :      Union[str, Any]   					= '''openwebtext_ccnews_stories_books_cased'''

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase__       :      Optional[Any]   					= os.path.join(get_home_dir()     ,       '''models'''	)
		lowerCAmelCase__       :      Optional[int]   					= _load_vocab(_a     ,       _a     ,       _a     ,       cls=_a	)

		lowerCAmelCase__       :      Any   					= nlp.model.BERTModel(
		    _a     ,       len(_a	)     ,       units=predefined_args['''units''']     ,       embed_size=predefined_args['''embed_size''']     ,       embed_dropout=predefined_args['''embed_dropout''']     ,       word_embed=predefined_args['''word_embed''']     ,       use_pooler=_a     ,       use_token_type_embed=_a     ,       token_type_vocab_size=predefined_args['''token_type_vocab_size''']     ,       use_classifier=_a     ,       use_decoder=_a     ,       )

		original_bort.load_parameters(_a     ,       cast_dtype=_a     ,       ignore_extra=_a	)
		lowerCAmelCase__       :      Tuple   					= original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase__       :      int   					= {
		    '''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(_a	),
		}

		lowerCAmelCase__       :      str   					= BertConfig.from_dict(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= BertForMaskedLM(_a	)
		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(_a	) -> 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(_a     ,       _a	):
				lowerCAmelCase__       :      Dict   					= hf_param.shape

				lowerCAmelCase__       :      List[str]   					= to_torch(params[gluon_param]	)
				lowerCAmelCase__       :      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

		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight     ,       '''word_embed.0.weight'''	)
		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight     ,       '''encoder.position_weight'''	)
		lowerCAmelCase__       :      Any   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias     ,       '''encoder.layer_norm.beta'''	)
		lowerCAmelCase__       :      Union[str, 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)
		lowerCAmelCase__       :      Union[str, Any]   					= torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

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

				# self attention
				lowerCAmelCase__       :      BertSelfAttention   					= layer.attention.self

				lowerCAmelCase__       :      Optional[Any]   					= check_and_map_params(
				    self_attn.key.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'	)

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

				# self attention output
				lowerCAmelCase__       :      BertSelfOutput   					= layer.attention.output

				lowerCAmelCase__       :      Dict   					= check_and_map_params(
				    self_output.dense.bias     ,       f'encoder.transformer_cells.{i}.proj.bias'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    self_output.dense.weight     ,       f'encoder.transformer_cells.{i}.proj.weight'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    self_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= check_and_map_params(
				    self_output.LayerNorm.weight     ,       f'encoder.transformer_cells.{i}.layer_norm.gamma'	)

				# intermediate
				lowerCAmelCase__       :      BertIntermediate   					= layer.intermediate

				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'	)
				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'	)

				# output
				lowerCAmelCase__       :      BertOutput   					= layer.output

				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    bert_output.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= 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__       :      Dict   					= RobertaTokenizer.from_pretrained('''roberta-base'''	)

		lowerCAmelCase__       :      List[str]   					= tokenizer.encode_plus(_a	)['''input_ids''']

		# Get gluon output
		lowerCAmelCase__       :      str   					= mx.nd.array([input_ids]	)
		lowerCAmelCase__       :      List[str]   					= original_bort(inputs=_a     ,       token_types=[]	)

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(_a	)
		lowerCAmelCase__       :      Optional[int]   					= BertModel.from_pretrained(_a	)
		hf_bort_model.eval()

		lowerCAmelCase__       :      Tuple   					= tokenizer.encode_plus(_a     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      Optional[Any]   					= hf_bort_model(**_a	)[0]

		lowerCAmelCase__       :      str   					= output_gluon[0].asnumpy()
		lowerCAmelCase__       :      Optional[Any]   					= output_hf[0].detach().numpy()

		lowerCAmelCase__       :      str   					= np.max(np.abs(hf_layer - gluon_layer	)	).item()
		lowerCAmelCase__       :      int   					= np.allclose(_a     ,       _a     ,       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:'''     ,       _a	)


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




