

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_a			=       logging.get_logger(__name__)

_a			=       "Hello world! cécé herlolip"







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      Tuple		)   ->   Union[str, Any]:

	"""simple docstring"""






	__lowerCAmelCase:       Tuple            =				FairseqRobertaModel.from_pretrained(_A		)
	roberta.eval()  # disable dropout
	__lowerCAmelCase:       Tuple            =				roberta.model.encoder.sentence_encoder
	__lowerCAmelCase:       List[Any]            =				RobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   , hidden_size=roberta.args.encoder_embed_dim   , num_hidden_layers=roberta.args.encoder_layers   , num_attention_heads=roberta.args.encoder_attention_heads   , intermediate_size=roberta.args.encoder_ffn_embed_dim   , max_position_embeddings=5_14   , type_vocab_size=1   , layer_norm_eps=1E-5   , )
	if classification_head:
		__lowerCAmelCase:       Union[str, Any]            =				roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
	print('Our BERT config:'   , _A		)

	__lowerCAmelCase:       List[str]            =				RobertaForSequenceClassification(_A		) if classification_head else RobertaForMaskedLM(_A		)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	__lowerCAmelCase:       str            =				roberta_sent_encoder.embed_tokens.weight
	__lowerCAmelCase:       Union[str, Any]            =				roberta_sent_encoder.embed_positions.weight
	__lowerCAmelCase:       int            =				torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
	__lowerCAmelCase:       Optional[Any]            =				roberta_sent_encoder.emb_layer_norm.weight
	__lowerCAmelCase:       List[Any]            =				roberta_sent_encoder.emb_layer_norm.bias

	for i in range(config.num_hidden_layers		):
		# Encoder: start of layer
		__lowerCAmelCase:       str            =				model.roberta.encoder.layer[i]
		__lowerCAmelCase:       List[str]            =				roberta_sent_encoder.layers[i]

		# self attention
		__lowerCAmelCase:       int            =				layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)		)
		)

		__lowerCAmelCase:       Any            =				roberta_layer.self_attn.q_proj.weight
		__lowerCAmelCase:       Tuple            =				roberta_layer.self_attn.q_proj.bias
		__lowerCAmelCase:       Union[str, Any]            =				roberta_layer.self_attn.k_proj.weight
		__lowerCAmelCase:       str            =				roberta_layer.self_attn.k_proj.bias
		__lowerCAmelCase:       str            =				roberta_layer.self_attn.v_proj.weight
		__lowerCAmelCase:       Tuple            =				roberta_layer.self_attn.v_proj.bias

		# self-attention output
		__lowerCAmelCase:       List[str]            =				layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		__lowerCAmelCase:       Tuple            =				roberta_layer.self_attn.out_proj.weight
		__lowerCAmelCase:       str            =				roberta_layer.self_attn.out_proj.bias
		__lowerCAmelCase:       Dict            =				roberta_layer.self_attn_layer_norm.weight
		__lowerCAmelCase:       Any            =				roberta_layer.self_attn_layer_norm.bias

		# intermediate
		__lowerCAmelCase:       List[Any]            =				layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		__lowerCAmelCase:       Optional[Any]            =				roberta_layer.fca.weight
		__lowerCAmelCase:       Tuple            =				roberta_layer.fca.bias

		# output
		__lowerCAmelCase:       Tuple            =				layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		__lowerCAmelCase:       Optional[Any]            =				roberta_layer.fca.weight
		__lowerCAmelCase:       int            =				roberta_layer.fca.bias
		__lowerCAmelCase:       str            =				roberta_layer.final_layer_norm.weight
		__lowerCAmelCase:       Tuple            =				roberta_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		__lowerCAmelCase:       int            =				roberta.model.classification_heads['mnli'].dense.weight
		__lowerCAmelCase:       List[Any]            =				roberta.model.classification_heads['mnli'].dense.bias
		__lowerCAmelCase:       str            =				roberta.model.classification_heads['mnli'].out_proj.weight
		__lowerCAmelCase:       str            =				roberta.model.classification_heads['mnli'].out_proj.bias
	else:
		# LM Head
		__lowerCAmelCase:       int            =				roberta.model.encoder.lm_head.dense.weight
		__lowerCAmelCase:       Union[str, Any]            =				roberta.model.encoder.lm_head.dense.bias
		__lowerCAmelCase:       int            =				roberta.model.encoder.lm_head.layer_norm.weight
		__lowerCAmelCase:       Dict            =				roberta.model.encoder.lm_head.layer_norm.bias
		__lowerCAmelCase:       Optional[Any]            =				roberta.model.encoder.lm_head.weight
		__lowerCAmelCase:       Union[str, Any]            =				roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	__lowerCAmelCase:       Union[str, Any]            =				roberta.encode(_A		).unsqueeze(0		)  # batch of size 1

	__lowerCAmelCase:       str            =				model(_A		)[0]
	if classification_head:
		__lowerCAmelCase:       Union[str, Any]            =				roberta.model.classification_heads['mnli'](roberta.extract_features(_A		)		)
	else:
		__lowerCAmelCase:       int            =				roberta.model(_A		)[0]
	print(our_output.shape   , their_output.shape		)
	__lowerCAmelCase:       int            =				torch.max(torch.abs(our_output - their_output		)		).item()
	print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
	__lowerCAmelCase:       Tuple            =				torch.allclose(_A   , _A   , atol=1E-3		)
	print('Do both models output the same tensors?'   , '🔥' if success else '💩'		)
	if not success:
		raise Exception('Something went wRoNg'		)

	pathlib.Path(_A		).mkdir(parents=_A   , exist_ok=_A		)
	print(f'''Saving model to {pytorch_dump_folder_path}'''		)
	model.save_pretrained(_A		)


if __name__ == "__main__":
		_a			=       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		_a			=       parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)








import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_a			=       False
_a			=       Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a			=       datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)






class 							A_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		str=sys.argv[1:] )							->     Optional[int]:
      __lowerCAmelCase:       List[Any]            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       Any            =				1
      __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				getopt.getopt(UpperCAmelCase      ,  'i:o:b:s'      ,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        __lowerCAmelCase:       Any            =				arg
       elif opt in ("-o", "--outfile"):
        __lowerCAmelCase:       List[Any]            =				arg
       elif opt in ("-b", "--batch_size"):
        __lowerCAmelCase:       List[Any]            =				int(UpperCAmelCase )
       elif opt in ("-s", "--subset_list"):
        __lowerCAmelCase:       Union[str, Any]            =				arg

      assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
      assert outputfile is not None and not os.path.isfile(UpperCAmelCase ), F'''{outputfile}'''
      if subset_list is not None:
       with open(os.path.realpath(UpperCAmelCase ) ) as f:
        __lowerCAmelCase:       Any            =				{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase )}
      else:
       __lowerCAmelCase:       Union[str, Any]            =				None

      __lowerCAmelCase:       List[str]            =				CONFIG
      if torch.cuda.is_available():
       __lowerCAmelCase:       Optional[Any]            =				'cuda'
      __lowerCAmelCase:       Union[str, Any]            =				os.path.realpath(UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				os.path.realpath(UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				Preprocess(self.config )
      __lowerCAmelCase:       int            =				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'      ,  config=self.config )
      __lowerCAmelCase:       Tuple            =				batch_size if batch_size != 0 else 1
      __lowerCAmelCase:       Optional[int]            =				DEFAULT_SCHEMA


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Dict )							->     Dict:
      __lowerCAmelCase:       str            =				int(file.split('.' )[0].split('_' )[-1] )
      __lowerCAmelCase:       Union[str, Any]            =				os.path.join(self.inputdir      ,  UpperCAmelCase )
      return (img_id, filepath)


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __lowerCAmelCase:       Any            =				[]
      for i, file in enumerate(os.listdir(self.inputdir ) ):
       if self.subset_list is not None and i not in self.subset_list:
        continue
       batch.append(self._vqa_file_split(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

      for i in range(1 ):
       yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      if not TEST:
       __lowerCAmelCase:       Tuple            =				datasets.ArrowWriter(features=self.schema      ,  path=self.outputfile )
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator ):
       __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				stream.read().split('\n'		)
 return data


if __name__ == "__main__":
  _a			=       Extract(sys.argv[1:])
  extract()
  if not TEST:
    _a			=       datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)





