

'''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()
lowercase__         =      logging.get_logger(__name__)

lowercase__         =      "The Nymphenburg Palace is a beautiful palace in Munich!"




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, 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,
 }

 UpperCAmelCase	:     Optional[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
 UpperCAmelCase	:     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=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
 UpperCAmelCase	:     List[str]						 =			'openwebtext_ccnews_stories_books_cased'

 # Specify download folder to Gluonnlp's vocab
 UpperCAmelCase	:     Dict						 =			os.path.join(get_home_dir()  ,	'models'     )
 UpperCAmelCase	:     int						 =			_load_vocab(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	cls=UpperCAmelCase_     )

 UpperCAmelCase	:     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_     )
 UpperCAmelCase	:     Union[str, Any]						 =			original_bort._collect_params_with_prefix()

 # Build our config 🤗
 UpperCAmelCase	:     Optional[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(UpperCAmelCase_     ),
 }

 UpperCAmelCase	:     int						 =			BertConfig.from_dict(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			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_     ) -> 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_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			hf_param.shape

  UpperCAmelCase	:     str						 =			to_torch(params[gluon_param]     )
  UpperCAmelCase	:     Optional[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

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

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

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

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

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

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

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

  # intermediate
  UpperCAmelCase	:     BertIntermediate						 =			layer.intermediate

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

  # output
  UpperCAmelCase	:     BertOutput						 =			layer.output

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

 UpperCAmelCase	:     Optional[int]						 =			tokenizer.encode_plus(UpperCAmelCase_     )['input_ids']

 # Get gluon output
 UpperCAmelCase	:     Optional[int]						 =			mx.nd.array([input_ids]     )
 UpperCAmelCase	:     Tuple						 =			original_bort(inputs=UpperCAmelCase_  ,	token_types=[]     )

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			BertModel.from_pretrained(UpperCAmelCase_     )
 hf_bort_model.eval()

 UpperCAmelCase	:     str						 =			tokenizer.encode_plus(UpperCAmelCase_  ,	return_tensors='pt'     )
 UpperCAmelCase	:     Optional[Any]						 =			hf_bort_model(**UpperCAmelCase_     )[0]

 UpperCAmelCase	:     List[Any]						 =			output_gluon[0].asnumpy()
 UpperCAmelCase	:     Optional[int]						 =			output_hf[0].detach().numpy()

 UpperCAmelCase	:     Optional[Any]						 =			np.max(np.abs(hf_layer - gluon_layer     )     ).item()
 UpperCAmelCase	:     int						 =			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__":
 lowercase__         =      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."
 )
 lowercase__         =      parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0	)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase_	)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




