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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase						:      List[str]							=							logging.get_logger(__name__)

lowercase						:      List[str]							=							{
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class __snake_case	(   lowerCAmelCase  ):
    _a	:		int=       "m2m_100"
    _a	:		Dict=       ["past_key_values"]
    _a	:		List[str]=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(						self	,snake_case=128112	,snake_case=1024	,snake_case=12	,snake_case=4096	,snake_case=16	,snake_case=12	,snake_case=4096	,snake_case=16	,snake_case=0.05	,snake_case=0.05	,snake_case=True	,snake_case=True	,snake_case="relu"	,snake_case=1024	,snake_case=0.1	,snake_case=0.1	,snake_case=0.0	,snake_case=0.02	,snake_case=2	,snake_case=True	,snake_case=1	,snake_case=0	,snake_case=2	,**snake_case	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   vocab_size
           lowercase							:  Union[str, Any] 			=   max_position_embeddings
           lowercase							:  List[str] 			=   d_model
           lowercase							:  Optional[Any] 			=   encoder_ffn_dim
           lowercase							:  int 			=   encoder_layers
           lowercase							:  str 			=   encoder_attention_heads
           lowercase							:  Optional[int] 			=   decoder_ffn_dim
           lowercase							:  Any 			=   decoder_layers
           lowercase							:  Tuple 			=   decoder_attention_heads
           lowercase							:  Dict 			=   dropout
           lowercase							:  int 			=   attention_dropout
           lowercase							:  int 			=   activation_dropout
           lowercase							:  str 			=   activation_function
           lowercase							:  Any 			=   init_std
           lowercase							:  Tuple 			=   encoder_layerdrop
           lowercase							:  List[str] 			=   decoder_layerdrop
           lowercase							:  List[str] 			=   use_cache
           lowercase							:  Any 			=   encoder_layers
           lowercase							:  Dict 			=   scale_embedding  # scale factor will be sqrt(d_model) if True

           super().__init__(
               pad_token_id=snake_case	,bos_token_id=snake_case	,eos_token_id=snake_case	,is_encoder_decoder=snake_case	,decoder_start_token_id=snake_case	,**snake_case	,)






class __snake_case	(   lowerCAmelCase  ):
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]   )

           if self.use_past:
                  lowercase							:  Tuple 			=   {0: """batch"""}
                  lowercase							:  List[str] 			=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                  lowercase							:  List[Any] 			=   {0: """batch""", 1: """decoder_sequence"""}
                  lowercase							:  Tuple 			=   {0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                  self.fill_with_past_key_values_(snake_case	,direction="""inputs"""   )
           return common_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = -1	,snake_case = -1	,snake_case = False	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   compute_effective_axis_dimension(
               snake_case	,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
           lowercase							:  Optional[int] 			=   tokenizer.num_special_tokens_to_add(snake_case   )
           lowercase							:  Optional[int] 			=   compute_effective_axis_dimension(
               snake_case	,fixed_dimension=OnnxConfig.default_fixed_sequence	,num_token_to_add=snake_case   )

           # Generate dummy inputs according to compute batch and sequence
           lowercase							:  int 			=   [""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
           lowercase							:  List[str] 			=   dict(tokenizer(snake_case	,return_tensors=snake_case   )   )
           return common_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = -1	,snake_case = -1	,snake_case = False	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  str 			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case	,snake_case	,snake_case	,snake_case	,snake_case   )

           # Generate decoder inputs
           lowercase							:  Dict 			=   seq_length if not self.use_past else 1
           lowercase							:  Optional[int] 			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case	,snake_case	,snake_case	,snake_case	,snake_case   )
           lowercase							:  Optional[Any] 			=   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
           lowercase							:  List[Any] 			=   dict(**snake_case	,**snake_case   )

           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
                  else:
                         import torch
                  lowercase							:  Tuple 			=   common_inputs["""input_ids"""].shape
                  lowercase							:  Union[str, Any] 			=   common_inputs["""decoder_input_ids"""].shape[1]
                  lowercase							:  List[str] 			=   self.num_attention_heads
                  lowercase							:  List[str] 			=   (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowercase							:  Union[str, Any] 			=   decoder_seq_length + 3
                  lowercase							:  Optional[Any] 			=   (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  lowercase							:  Union[str, Any] 			=   torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case	,snake_case   )]	,dim=1   )

                  lowercase							:  Any 			=   []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowercase							:  Tuple 			=   self.num_layers
                  lowercase							:  Tuple 			=   min(snake_case	,snake_case   )
                  lowercase							:  Dict 			=   max(snake_case	,snake_case   ) - min_num_layers
                  lowercase							:  List[Any] 			=   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                  for _ in range(snake_case   ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                             )   )
                  # TODO: test this.
                  lowercase							:  int 			=   encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                  for _ in range(snake_case	,snake_case   ):
                         common_inputs["past_key_values"].append((torch.zeros(snake_case   ), torch.zeros(snake_case   ))   )
           return common_inputs
    _a	:		str=       _generate_dummy_inputs_for_default_and_seqaseq_lm


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)
