
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()
lowercase						:      Tuple							=							logging.get_logger(__name__)

lowercase						:      Dict							=							"""Hello world! cécé herlolip"""







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Dict:
       lowercase							:  List[Any] 			=   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
       roberta.eval()  # disable dropout
       lowercase							:  Optional[int] 			=   roberta.model.encoder.sentence_encoder
       lowercase							:  List[str] 			=   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=514      ,			type_vocab_size=1      ,			layer_norm_eps=1e-5      ,			)
       if classification_head:
              lowercase							:  int 			=   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
       print("""Our BERT config:"""      ,			SCREAMING_SNAKE_CASE__       )

       lowercase							:  Optional[int] 			=   RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__       )
       model.eval()

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

       for i in range(config.num_hidden_layers       ):
              # Encoder: start of layer
              lowercase							:  BertLayer 			=   model.roberta.encoder.layer[i]
              lowercase							:  TransformerSentenceEncoderLayer 			=   roberta_sent_encoder.layers[i]

              # self attention
              lowercase							:  BertSelfAttention 			=   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)       )
              )

              lowercase							:  Dict 			=   roberta_layer.self_attn.q_proj.weight
              lowercase							:  int 			=   roberta_layer.self_attn.q_proj.bias
              lowercase							:  Union[str, Any] 			=   roberta_layer.self_attn.k_proj.weight
              lowercase							:  List[Any] 			=   roberta_layer.self_attn.k_proj.bias
              lowercase							:  Tuple 			=   roberta_layer.self_attn.v_proj.weight
              lowercase							:  str 			=   roberta_layer.self_attn.v_proj.bias

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

              # intermediate
              lowercase							:  BertIntermediate 			=   layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              lowercase							:  List[Any] 			=   roberta_layer.fca.weight
              lowercase							:  List[Any] 			=   roberta_layer.fca.bias

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

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

       # Let's check that we get the same results.
       lowercase							:  torch.Tensor 			=   roberta.encode(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )  # batch of size 1

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

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


if __name__ == "__main__":
  lowercase						:      Any							=							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."""
  )
  lowercase						:      List[str]							=							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
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class __snake_case	:
    def __init__(						self	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   arg

           assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
           assert outputfile is not None and not os.path.isfile(snake_case   ), f"{outputfile}"
           if subset_list is not None:
                  with open(os.path.realpath(snake_case   )   ) as f:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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(snake_case   )   )
                  if len(snake_case   ) == self.batch:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         yield list(map(snake_case	,zip(*snake_case   )   )   )

           for i in range(1   ):
                  yield list(map(snake_case	,zip(*snake_case   )   )   )
    def __call__(						self   ):



           '''simple docstring'''







           if not TEST:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   self.model(
                      snake_case	,snake_case	,scales_yx=snake_case	,padding="""max_detections"""	,max_detections=self.config.MAX_DETECTIONS	,pad_value=0	,return_tensors="""np"""	,location="""cpu"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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