





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor





class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=3     ,					_A=30     ,					_A=400     ,					_A=True     ,					_A=None     ,					_A=0.9     ,					_A=None     ,					_A=True     ,					_A=[0.5, 0.5, 0.5]     ,					_A=[0.5, 0.5, 0.5]     ,					):
									__A	:						Any       =				size if size is not None else {'shortest_edge': 30}
									__A	:						Optional[Any]       =				crop_size if crop_size is not None else {'height': 30, 'width': 30}
									__A	:						str       =				parent
									__A	:						Optional[int]       =				batch_size
									__A	:						Tuple       =				num_channels
									__A	:						List[Any]       =				min_resolution
									__A	:						Union[str, Any]       =				max_resolution
									__A	:						Tuple       =				do_resize_and_center_crop
									__A	:						List[Any]       =				size
									__A	:						str       =				crop_pct
									__A	:						Dict       =				crop_size
									__A	:						Optional[int]       =				do_normalize
									__A	:						Any       =				image_mean
									__A	:						Union[str, Any]       =				image_std





				def 					UpperCAmelCase_		(			self     ):
									return {
									    "size": self.size,
									    "do_resize_and_center_crop": self.do_resize_and_center_crop,
									    "crop_pct": self.crop_pct,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Union[str, Any]					    =	PoolFormerImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				PoolFormerImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(_A     ,					'do_resize_and_center_crop'     )     )
									self.assertTrue(hasattr(_A     ,					'size'     )     )
									self.assertTrue(hasattr(_A     ,					'crop_pct'     )     )
									self.assertTrue(hasattr(_A     ,					'do_normalize'     )     )
									self.assertTrue(hasattr(_A     ,					'image_mean'     )     )
									self.assertTrue(hasattr(_A     ,					'image_std'     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 30}     )
									self.assertEqual(image_processor.crop_size     ,					{'height': 30, 'width': 30}     )

									__A	:						Optional[Any]       =				self.image_processing_class.from_dict(self.image_processor_dict     ,					size=42     ,					crop_size=84     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 42}     )
									self.assertEqual(image_processor.crop_size     ,					{'height': 84, 'width': 84}     )



				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Optional[int]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__A	:						Union[str, Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					Image.Image     )

									# Test not batched input
									__A	:						int       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									self.assertEqual(
									    encoded_images.shape     ,					(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)

									# Test batched
									__A	:						Optional[int]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Union[str, Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__A	:						Union[str, Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					numpify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					np.ndarray     )

									# Test not batched input
									__A	:						int       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)

									# Test batched
									__A	:						Union[str, Any]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)





				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						str       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Any       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

									# Test not batched input
									__A	:						Any       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)

									# Test batched
									__A	:						int       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,					)











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()
UpperCAmelCase						:   List[Any]			  =     logging.get_logger(__name__)

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







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Optional[Any]:
					__A	:						Any       =				{
					    'attention_cell': 'multi_head',
					    'num_layers': 4,
					    'units': 10_24,
					    'hidden_size': 7_68,
					    'max_length': 5_12,
					    'num_heads': 8,
					    'scaled': True,
					    'dropout': 0.1,
					    'use_residual': True,
					    'embed_size': 10_24,
					    'embed_dropout': 0.1,
					    'word_embed': None,
					    'layer_norm_eps': 1e-5,
					    'token_type_vocab_size': 2,
					}

					__A	:						str       =				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	:						Optional[int]       =				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
					__A	:						Union[str, Any]       =				'openwebtext_ccnews_stories_books_cased'

					# Specify download folder to Gluonnlp's vocab
					__A	:						Any       =				os.path.join(get_home_dir()					,						'models'  )
					__A	:						List[Any]       =				_load_vocab(a					,						a					,						a					,						cls=a  )

					__A	:						Dict       =				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  )
					__A	:						Union[str, Any]       =				original_bort._collect_params_with_prefix()

					# Build our config 🤗
					__A	:						Any       =				{
					    '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  ),
					}

					__A	:						int       =				BertConfig.from_dict(a  )
					__A	:						Union[str, 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  ):
										__A	:						Tuple       =				hf_param.shape

										__A	:						str       =				to_torch(params[gluon_param]  )
										__A	:						Union[str, 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

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

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

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

										__A	:						Optional[Any]       =				check_and_map_params(
										    self_attn.key.bias.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""  )

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

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

										__A	:						Tuple       =				check_and_map_params(
										    self_output.dense.bias					,						F"""encoder.transformer_cells.{i}.proj.bias"""  )
										__A	:						int       =				check_and_map_params(
										    self_output.dense.weight					,						F"""encoder.transformer_cells.{i}.proj.weight"""  )
										__A	:						List[Any]       =				check_and_map_params(
										    self_output.LayerNorm.bias					,						F"""encoder.transformer_cells.{i}.layer_norm.beta"""  )
										__A	:						str       =				check_and_map_params(
										    self_output.LayerNorm.weight					,						F"""encoder.transformer_cells.{i}.layer_norm.gamma"""  )

										# intermediate
										__A	:						BertIntermediate       =				layer.intermediate

										__A	:						int       =				check_and_map_params(
										    intermediate.dense.bias					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""  )
										__A	:						List[Any]       =				check_and_map_params(
										    intermediate.dense.weight					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""  )

										# output
										__A	:						BertOutput       =				layer.output

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

					__A	:						List[str]       =				tokenizer.encode_plus(a  )['input_ids']

					# Get gluon output
					__A	:						List[str]       =				mx.nd.array([input_ids]  )
					__A	:						Union[str, Any]       =				original_bort(inputs=a					,						token_types=[]  )

					# Get Transformer output (save and reload model again)
					hf_bort_model.save_pretrained(a  )
					__A	:						Optional[Any]       =				BertModel.from_pretrained(a  )
					hf_bort_model.eval()

					__A	:						Tuple       =				tokenizer.encode_plus(a					,						return_tensors='pt'  )
					__A	:						Any       =				hf_bort_model(**a  )[0]

					__A	:						Union[str, Any]       =				output_gluon[0].asnumpy()
					__A	:						Tuple       =				output_hf[0].detach().numpy()

					__A	:						int       =				np.max(np.abs(hf_layer - gluon_layer  )  ).item()
					__A	:						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__":
				UpperCAmelCase						:   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.'''
				)
				UpperCAmelCase						:   Dict			  =     parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




