






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

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


__snake_case     :     Optional[Any]       =  logging.get_logger(__name__)

__snake_case     :     Tuple       =  {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}


class      __SCREAMING_SNAKE_CASE       ( a_):
						_SCREAMING_SNAKE_CASE						:							Any				=       'bigbird_pegasus'
						_SCREAMING_SNAKE_CASE						:							int				=       ['past_key_values']
						_SCREAMING_SNAKE_CASE						:							int				=       {
						    'num_attention_heads': 'encoder_attention_heads',
						    'hidden_size': 'd_model',
						    'attention_probs_dropout_prob': 'attention_dropout',
						}






						def __init__(						self  ,    _UpperCamelCase=9_61_03  ,    _UpperCamelCase=40_96  ,    _UpperCamelCase=16  ,    _UpperCamelCase=40_96  ,    _UpperCamelCase=16  ,    _UpperCamelCase=16  ,    _UpperCamelCase=40_96  ,    _UpperCamelCase=16  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase="gelu_new"  ,    _UpperCamelCase=10_24  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=0.02  ,    _UpperCamelCase=2  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=True  ,    _UpperCamelCase=0  ,    _UpperCamelCase=2  ,    _UpperCamelCase=1  ,    _UpperCamelCase="block_sparse"  ,    _UpperCamelCase=64  ,    _UpperCamelCase=3  ,    _UpperCamelCase=False  ,    **_UpperCamelCase  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    vocab_size
												lowerCAmelCase__         =    max_position_embeddings
												lowerCAmelCase__         =    d_model
												lowerCAmelCase__         =    encoder_ffn_dim
												lowerCAmelCase__         =    encoder_layers
												lowerCAmelCase__         =    encoder_attention_heads
												lowerCAmelCase__         =    decoder_ffn_dim
												lowerCAmelCase__         =    decoder_layers
												lowerCAmelCase__         =    decoder_attention_heads
												lowerCAmelCase__         =    dropout
												lowerCAmelCase__         =    attention_dropout
												lowerCAmelCase__         =    activation_dropout
												lowerCAmelCase__         =    activation_function
												lowerCAmelCase__         =    init_std
												lowerCAmelCase__         =    encoder_layerdrop
												lowerCAmelCase__         =    decoder_layerdrop
												lowerCAmelCase__         =    classifier_dropout
												lowerCAmelCase__         =    use_cache
												lowerCAmelCase__         =    encoder_layers
												lowerCAmelCase__         =    scale_embedding  # scale factor will be sqrt(d_model) if True

												# extra config
												lowerCAmelCase__         =    attention_type
												lowerCAmelCase__         =    block_size
												lowerCAmelCase__         =    num_random_blocks
												lowerCAmelCase__         =    use_bias

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




class      __SCREAMING_SNAKE_CASE       ( a_):






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__         =    OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ] )

																		if self.use_past:
																								lowerCAmelCase__         =    {0: 'batch'}
																								lowerCAmelCase__         =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																		else:
																								lowerCAmelCase__         =    {0: 'batch', 1: 'decoder_sequence'}
																								lowerCAmelCase__         =    {0: 'batch', 1: 'decoder_sequence'}

																		if self.use_past:
																								self.fill_with_past_key_values_(_UpperCamelCase  ,    direction='inputs' )
												elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		lowerCAmelCase__         =    OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ] )
																		if self.use_past:
																								lowerCAmelCase__         =    self.num_layers
																								for i in range(_UpperCamelCase ):
																														lowerCAmelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
																														lowerCAmelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
												else:
																		lowerCAmelCase__         =    OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																		        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																		    ] )

												return common_inputs






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__         =    super().outputs
												else:
																		lowerCAmelCase__         =    super(_UpperCamelCase  ,    self ).outputs
																		if self.use_past:
																								lowerCAmelCase__         =    self.num_layers
																								for i in range(_UpperCamelCase ):
																														lowerCAmelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
																														lowerCAmelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
												return common_outputs






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												# Generate decoder inputs
												lowerCAmelCase__         =    seq_length if not self.use_past else 1
												lowerCAmelCase__         =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )
												lowerCAmelCase__         =    {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												lowerCAmelCase__         =    dict(**_UpperCamelCase  ,    **_UpperCamelCase )

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

																		lowerCAmelCase__         =    torch.cat(
																		    [common_inputs['decoder_attention_mask'], torch.ones(_UpperCamelCase  ,    _UpperCamelCase )]  ,    dim=1 )

																		lowerCAmelCase__         =    []
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		lowerCAmelCase__         =    self.num_layers
																		lowerCAmelCase__         =    min(_UpperCamelCase  ,    _UpperCamelCase )
																		lowerCAmelCase__         =    max(_UpperCamelCase  ,    _UpperCamelCase ) - min_num_layers
																		lowerCAmelCase__         =    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																		for _ in range(_UpperCamelCase ):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(_UpperCamelCase ),
																								        torch.zeros(_UpperCamelCase ),
																								        torch.zeros(_UpperCamelCase ),
																								        torch.zeros(_UpperCamelCase ),
																								    ) )
																		# TODO: test this.
																		lowerCAmelCase__         =    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																		for _ in range(_UpperCamelCase  ,    _UpperCamelCase ):
																								common_inputs["past_key_values"].append((torch.zeros(_UpperCamelCase ), torch.zeros(_UpperCamelCase )) )
												return common_inputs






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
																		else:
																								import torch
																		lowerCAmelCase__         =    common_inputs['input_ids'].shape
																		# Not using the same length for past_key_values
																		lowerCAmelCase__         =    seqlen + 2
																		lowerCAmelCase__         =    self.num_layers
																		lowerCAmelCase__         =    self.num_attention_heads
																		lowerCAmelCase__         =    (
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		lowerCAmelCase__         =    common_inputs['attention_mask'].dtype
																		lowerCAmelCase__         =    torch.cat(
																		    [common_inputs['attention_mask'], torch.ones(_UpperCamelCase  ,    _UpperCamelCase  ,    dtype=_UpperCamelCase )]  ,    dim=1 )
																		lowerCAmelCase__         =    [
																		    (torch.zeros(_UpperCamelCase ), torch.zeros(_UpperCamelCase )) for _ in range(_UpperCamelCase )
																		]
												return common_inputs






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    ):






												"""simple docstring"""
												# Copied from OnnxConfig.generate_dummy_inputs
												# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												lowerCAmelCase__         =    compute_effective_axis_dimension(
												    _UpperCamelCase  ,    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
												lowerCAmelCase__         =    tokenizer.num_special_tokens_to_add(_UpperCamelCase )
												lowerCAmelCase__         =    compute_effective_axis_dimension(
												    _UpperCamelCase  ,    fixed_dimension=OnnxConfig.default_fixed_sequence  ,    num_token_to_add=_UpperCamelCase )

												# Generate dummy inputs according to compute batch and sequence
												lowerCAmelCase__         =    [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
												lowerCAmelCase__         =    dict(tokenizer(_UpperCamelCase  ,    return_tensors=_UpperCamelCase ) )
												return common_inputs






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__         =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    _UpperCamelCase  ,    batch_size=_UpperCamelCase  ,    seq_length=_UpperCamelCase  ,    is_pair=_UpperCamelCase  ,    framework=_UpperCamelCase )

												elif self.task == "causal-lm":
																		lowerCAmelCase__         =    self._generate_dummy_inputs_for_causal_lm(
																		    _UpperCamelCase  ,    batch_size=_UpperCamelCase  ,    seq_length=_UpperCamelCase  ,    is_pair=_UpperCamelCase  ,    framework=_UpperCamelCase )
												else:
																		lowerCAmelCase__         =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																		    _UpperCamelCase  ,    batch_size=_UpperCamelCase  ,    seq_length=_UpperCamelCase  ,    is_pair=_UpperCamelCase  ,    framework=_UpperCamelCase )

												return common_inputs







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if self.task in ["default", "seq2seq-lm"]:
																		lowerCAmelCase__         =    super()._flatten_past_key_values_(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )
												else:
																		lowerCAmelCase__         =    super(_UpperCamelCase  ,    self )._flatten_past_key_values_(
																		    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )






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
UpperCAmelCase      		=					False
UpperCAmelCase      		=					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase      		=					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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class  __lowerCAmelCase	:



 '''simple docstring'''

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

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

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

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

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







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	stream.read().split('\n'					)
       return data


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



