




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

_snake_case : Tuple  				=						'The Nymphenburg Palace is a beautiful palace in Munich!'
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{
							    '''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,
							}

							_a      	=							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
							_a      	=							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=UpperCamelCase					,		output_all_encodings=UpperCamelCase					,		use_residual=predefined_args['''use_residual''']					,		activation=predefined_args.get('''activation'''					,		'''gelu''' )					,		layer_norm_eps=predefined_args.get('''layer_norm_eps'''					,		UpperCamelCase )					,		)

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

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

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

							original_bort.load_parameters(UpperCamelCase					,		cast_dtype=UpperCamelCase					,		ignore_extra=UpperCamelCase )
							_a      	=							original_bort._collect_params_with_prefix()

							# Build our config 🤗
							_a      	=							{
							    '''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(UpperCamelCase ),
							}

							_a      	=							BertConfig.from_dict(UpperCamelCase )
							_a      	=							BertForMaskedLM(UpperCamelCase )
							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(UpperCamelCase			:    Tuple ) -> 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(UpperCamelCase			:    Any					,		UpperCamelCase			:    List[str] ):
														_a      	=							hf_param.shape

														_a      	=							to_torch(params[gluon_param] )
														_a      	=							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

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

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

														# self attention
														_a      	=							layer.attention.self

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

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

														# self attention output
														_a      	=							layer.attention.output

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

														# intermediate
														_a      	=							layer.intermediate

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

														# output
														_a      	=							layer.output

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

							_a      	=							tokenizer.encode_plus(UpperCamelCase )['''input_ids''']

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

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

							_a      	=							tokenizer.encode_plus(UpperCamelCase					,		return_tensors='''pt''' )
							_a      	=							hf_bort_model(**UpperCamelCase )[0]

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

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


if __name__ == "__main__":
				_snake_case : Tuple  				=						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.'
				)
				_snake_case : Any  				=						parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Tuple   )     ->				np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							make_list_of_images(lowerCAmelCase_   )

														if not valid_images(lowerCAmelCase_   ):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

														if do_resize and size is None:
																					raise ValueError('''Size must be specified if do_resize is True.'''   )

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

														# All transformations expect numpy arrays.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

														if do_resize:
																					_a      	=							[self.resize(image=lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_   ) for image in images]

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

														if do_rescale:
																					_a      	=							[self.rescale(image=lowerCAmelCase_				,     scale=lowerCAmelCase_   ) for image in images]

														if do_normalize:
																					_a      	=							[self.normalize(image=lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_   ) for image in images]

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

