


'''simple docstring'''





import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    ErnieMForInformationExtraction,
			    ErnieMForMultipleChoice,
			    ErnieMForQuestionAnswering,
			    ErnieMForSequenceClassification,
			    ErnieMForTokenClassification,
			    ErnieMModel,
			)
			from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=13     ,      _UpperCAmelCase=7     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=99     ,      _UpperCAmelCase=32     ,      _UpperCAmelCase=5     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=37     ,      _UpperCAmelCase="gelu"     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=512     ,      _UpperCAmelCase=16     ,      _UpperCAmelCase=2     ,      _UpperCAmelCase=0.02     ,      _UpperCAmelCase=3     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=None     ,      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      parent
										__magic_name__				:							Any				=      batch_size
										__magic_name__				:							Dict				=      seq_length
										__magic_name__				:							List[str]				=      is_training
										__magic_name__				:							Any				=      use_input_mask
										__magic_name__				:							List[str]				=      use_labels
										__magic_name__				:							Union[str, Any]				=      vocab_size
										__magic_name__				:							Any				=      hidden_size
										__magic_name__				:							List[str]				=      num_hidden_layers
										__magic_name__				:							Tuple				=      num_attention_heads
										__magic_name__				:							Optional[Any]				=      intermediate_size
										__magic_name__				:							Optional[int]				=      hidden_act
										__magic_name__				:							List[Any]				=      hidden_dropout_prob
										__magic_name__				:							Dict				=      attention_probs_dropout_prob
										__magic_name__				:							str				=      max_position_embeddings
										__magic_name__				:							Tuple				=      type_vocab_size
										__magic_name__				:							List[Any]				=      type_sequence_label_size
										__magic_name__				:							Optional[int]				=      initializer_range
										__magic_name__				:							Union[str, Any]				=      num_labels
										__magic_name__				:							Any				=      num_choices
										__magic_name__				:							Optional[int]				=      scope
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size      )

										__magic_name__				:							List[str]				=      None
										if self.use_input_mask:
																__magic_name__				:							List[str]				=      random_attention_mask([self.batch_size, self.seq_length]      )

										__magic_name__				:							Tuple				=      None
										__magic_name__				:							str				=      None
										__magic_name__				:							Union[str, Any]				=      None
										if self.use_labels:
																__magic_name__				:							Any				=      ids_tensor([self.batch_size]     ,      self.type_sequence_label_size      )
																__magic_name__				:							Any				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels      )
																__magic_name__				:							Union[str, Any]				=      ids_tensor([self.batch_size]     ,      self.num_choices      )

										__magic_name__				:							Tuple				=      self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size      )

										__magic_name__				:							int				=      None
										if self.use_input_mask:
																__magic_name__				:							Dict				=      random_attention_mask([self.batch_size, self.seq_length]      )
										__magic_name__				:							List[Any]				=      self.get_config()

										return config, input_ids, input_mask
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return ErnieMConfig(
										    vocab_size=self.vocab_size     ,      hidden_size=self.hidden_size     ,      num_hidden_layers=self.num_hidden_layers     ,      num_attention_heads=self.num_attention_heads     ,      intermediate_size=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      hidden_dropout_prob=self.hidden_dropout_prob     ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      type_vocab_size=self.type_vocab_size     ,      initializer_range=self.initializer_range     ,      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      ErnieMModel(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase     ,      return_dict=_UpperCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      ErnieMForQuestionAnswering(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Optional[int]				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      start_positions=_UpperCAmelCase     ,      end_positions=_UpperCAmelCase     ,      )
										self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      ErnieMForInformationExtraction(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Any				=      torch.ones_like(_UpperCAmelCase     ,      dtype=torch.floataa      )
										__magic_name__				:							Tuple				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      start_positions=_UpperCAmelCase     ,      end_positions=_UpperCAmelCase     ,      )
										self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.num_labels
										__magic_name__				:							Optional[Any]				=      ErnieMForSequenceClassification(_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Any				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      labels=_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      self.num_labels
										__magic_name__				:							Optional[Any]				=      ErnieMForTokenClassification(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										input_ids.to(_UpperCAmelCase      )
										input_mask.to(_UpperCAmelCase      )
										token_labels.to(_UpperCAmelCase      )

										__magic_name__				:							Tuple				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      labels=_UpperCAmelCase      )

										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.num_choices
										__magic_name__				:							int				=      ErnieMForMultipleChoice(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							int				=      input_ids.unsqueeze(1      ).expand(-1     ,      self.num_choices     ,      -1      ).contiguous()
										__magic_name__				:							Any				=      input_mask.unsqueeze(1      ).expand(-1     ,      self.num_choices     ,      -1      ).contiguous()
										__magic_name__				:							List[Any]				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      labels=_UpperCAmelCase     ,      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.prepare_config_and_inputs()
										(
										    __magic_name__ 
										)				:							List[str]				=      config_and_inputs
										__magic_name__				:							Union[str, Any]				=      {'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict






@require_torch
class     snake_case__  (      _A						,       _A						,       unittest.TestCase  ):
				__lowerCAmelCase		=      (
				    (
				        ErnieMModel,
				        ErnieMForMultipleChoice,
				        ErnieMForQuestionAnswering,
				        ErnieMForSequenceClassification,
				        ErnieMForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__lowerCAmelCase		=      ()
				__lowerCAmelCase		=      (
				    {
				        '''feature-extraction''': ErnieMModel,
				        '''question-answering''': ErnieMForQuestionAnswering,
				        '''text-classification''': ErnieMForSequenceClassification,
				        '''token-classification''': ErnieMForTokenClassification,
				        '''zero-shot''': ErnieMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__lowerCAmelCase		=      False
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if pipeline_test_casse_name == "QAPipelineTests":
																return True

										return False
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      ErnieMModelTester(self      )
										__magic_name__				:							Dict				=      ConfigTester(self     ,      config_class=_UpperCAmelCase     ,      hidden_size=37      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.config_tester.run_common_tests()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__magic_name__				:							Optional[Any]				=      type
																self.model_tester.create_and_check_model(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_information_extraction(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase      )





				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__				:							Optional[Any]				=      ErnieMModel.from_pretrained(_UpperCAmelCase      )
																self.assertIsNotNone(_UpperCAmelCase      )






@require_torch
class     snake_case__  (      unittest.TestCase  ):
				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'      )
										model.eval()
										__magic_name__				:							int				=      torch.tensor([[0, 1, 2, 3, 4, 5]]      )
										__magic_name__				:							List[str]				=      model(_UpperCAmelCase      )[0]

										# TODO Replace vocab size
										__magic_name__				:							List[Any]				=      768

										__magic_name__				:							int				=      torch.Size((1, 6, hidden_size)      )
										self.assertEqual(output.shape     ,      _UpperCAmelCase      )

										__magic_name__				:							int				=      torch.tensor(
										    [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]     ,      _UpperCAmelCase     ,      atol=1E-3      )      )






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
snake_case__     							= 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     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      None
										__magic_name__				:							int				=      None
										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							Optional[int]				=      1
										__magic_name__		, __magic_name__				:							int				=      getopt.getopt(_UpperCAmelCase     ,      'i:o:b:s'     ,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
										for opt, arg in opts:
																if opt in ("-i", "--inputdir"):
																						__magic_name__				:							List[str]				=      arg
																elif opt in ("-o", "--outfile"):
																						__magic_name__				:							Union[str, Any]				=      arg
																elif opt in ("-b", "--batch_size"):
																						__magic_name__				:							Any				=      int(_UpperCAmelCase      )
																elif opt in ("-s", "--subset_list"):
																						__magic_name__				:							List[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:
																						__magic_name__				:							str				=      {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase      )}
										else:
																__magic_name__				:							Optional[int]				=      None

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




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      int(file.split('.'      )[0].split('_'      )[-1]      )
										__magic_name__				:							Union[str, Any]				=      os.path.join(self.inputdir     ,      _UpperCAmelCase      )
										return (img_id, filepath)
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							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(_UpperCAmelCase      )      )
																if len(_UpperCAmelCase      ) == self.batch:
																						__magic_name__				:							int				=      batch
																						__magic_name__				:							Union[str, Any]				=      []
																						yield list(map(_UpperCAmelCase     ,      zip(*_UpperCAmelCase      )      )      )

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





				def __call__(  self      ):




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


