


'''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()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)

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


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str			):
 __A  :		Tuple       =			{
     '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  :		Dict       =			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  :		Tuple       =			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  :		Optional[int]       =			'openwebtext_ccnews_stories_books_cased'

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

 __A  :		List[Any]       =			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  :		Tuple       =			original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __A  :		Optional[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(UpperCamelCase__			),
 }

 __A  :		Union[str, Any]       =			BertConfig.from_dict(UpperCamelCase__			)
 __A  :		Dict       =			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__       :					List[Any]			) -> 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__       :					int,				UpperCamelCase__       :					str			):
  __A  :		List[Any]       =			hf_param.shape

  __A  :		Optional[Any]       =			to_torch(params[gluon_param]			)
  __A  :		Dict       =			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  :		List[Any]       =			check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight,				'word_embed.0.weight'			)
 __A  :		Optional[Any]       =			check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight,				'encoder.position_weight'			)
 __A  :		Optional[int]       =			check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias,				'encoder.layer_norm.beta'			)
 __A  :		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)
 __A  :		List[Any]       =			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[int]       =			check_and_map_params(
      self_attn.key.bias.data,				f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""			)

  __A  :		Optional[Any]       =			check_and_map_params(
      self_attn.key.weight.data,				f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""			)
  __A  :		List[str]       =			check_and_map_params(
      self_attn.query.bias.data,				f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""			)
  __A  :		Any       =			check_and_map_params(
      self_attn.query.weight.data,				f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""			)
  __A  :		str       =			check_and_map_params(
      self_attn.value.bias.data,				f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""			)
  __A  :		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  :		List[Any]       =			check_and_map_params(
      self_output.dense.bias,				f"""encoder.transformer_cells.{i}.proj.bias"""			)
  __A  :		Optional[int]       =			check_and_map_params(
      self_output.dense.weight,				f"""encoder.transformer_cells.{i}.proj.weight"""			)
  __A  :		str       =			check_and_map_params(
      self_output.LayerNorm.bias,				f"""encoder.transformer_cells.{i}.layer_norm.beta"""			)
  __A  :		int       =			check_and_map_params(
      self_output.LayerNorm.weight,				f"""encoder.transformer_cells.{i}.layer_norm.gamma"""			)

  # intermediate
  __A  :		BertIntermediate       =			layer.intermediate

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

  # output
  __A  :		BertOutput       =			layer.output

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

 __A  :		Any       =			tokenizer.encode_plus(UpperCamelCase__			)['input_ids']

 # Get gluon output
 __A  :		List[Any]       =			mx.nd.array([input_ids]			)
 __A  :		Optional[int]       =			original_bort(inputs=UpperCamelCase__,				token_types=[]			)

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

 __A  :		List[Any]       =			tokenizer.encode_plus(UpperCamelCase__,				return_tensors='pt'			)
 __A  :		Optional[Any]       =			hf_bort_model(**UpperCamelCase__			)[0]

 __A  :		Optional[int]       =			output_gluon[0].asnumpy()
 __A  :		Dict       =			output_hf[0].detach().numpy()

 __A  :		Optional[Any]       =			np.max(np.abs(hf_layer - gluon_layer			)			).item()
 __A  :		List[str]       =			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__":
 UpperCAmelCase_ :     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.'
 )
 UpperCAmelCase_ :     Optional[Any]								=      parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






