

"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
    )






class  __a				:





       """simple docstring"""


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





        """simple docstring"""

        lowerCAmelCase__      :							int    =							parent
        lowerCAmelCase__      :							Optional[Any]    =							13
        lowerCAmelCase__      :							Optional[Any]    =							7
        lowerCAmelCase__      :							int    =							True
        lowerCAmelCase__      :							Any    =							True
        lowerCAmelCase__      :							str    =							True
        lowerCAmelCase__      :							Any    =							True
        lowerCAmelCase__      :							str    =							99
        lowerCAmelCase__      :							List[Any]    =							32
        lowerCAmelCase__      :							int    =							2
        lowerCAmelCase__      :							Optional[int]    =							4
        lowerCAmelCase__      :							Tuple    =							37
        lowerCAmelCase__      :							Any    =							"gelu"
        lowerCAmelCase__      :							int    =							0.1
        lowerCAmelCase__      :							List[str]    =							0.1
        lowerCAmelCase__      :							Tuple    =							512
        lowerCAmelCase__      :							Tuple    =							16
        lowerCAmelCase__      :							Optional[Any]    =							False
        lowerCAmelCase__      :							Any    =							-1
        lowerCAmelCase__      :							Union[str, Any]    =							True
        lowerCAmelCase__      :							str    =							2
        lowerCAmelCase__      :							Union[str, Any]    =							0.02
        lowerCAmelCase__      :							Any    =							3
        lowerCAmelCase__      :							Any    =							4
        lowerCAmelCase__      :							str    =							None






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

        lowerCAmelCase__      :							int    =							None
        if self.use_input_mask:
         lowerCAmelCase__      :							Union[str, Any]    =							random_attention_mask([self.batch_size, self.seq_length]      )

        lowerCAmelCase__      :							int    =							None
        if self.use_token_type_ids:
         lowerCAmelCase__      :							List[Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size      )

        lowerCAmelCase__      :							Any    =							None
        lowerCAmelCase__      :							List[Any]    =							None
        lowerCAmelCase__      :							List[Any]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							Tuple    =							ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
         lowerCAmelCase__      :							Dict    =							ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )

        lowerCAmelCase__      :							Dict    =							DebertaConfig(
            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 ,				relative_attention=self.relative_attention ,				max_relative_positions=self.max_relative_positions ,				position_biased_input=self.position_biased_input ,				initializer_range=self.initializer_range ,				return_dict=snake_case ,				)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							TFDebertaModel(config=snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

        lowerCAmelCase__      :							str    =							[input_ids, input_mask]
        lowerCAmelCase__      :							str    =							model(snake_case      )

        lowerCAmelCase__      :							Tuple    =							model(snake_case      )

        self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							TFDebertaForMaskedLM(config=snake_case      )
        lowerCAmelCase__      :							List[Any]    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__      :							Optional[int]    =							model(snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							self.num_labels
        lowerCAmelCase__      :							Optional[int]    =							TFDebertaForSequenceClassification(config=snake_case      )
        lowerCAmelCase__      :							Dict    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        lowerCAmelCase__      :							Any    =							model(snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							self.num_labels
        lowerCAmelCase__      :							int    =							TFDebertaForTokenClassification(config=snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__      :							Optional[Any]    =							model(snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							TFDebertaForQuestionAnswering(config=snake_case      )
        lowerCAmelCase__      :							str    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        lowerCAmelCase__      :							List[str]    =							model(snake_case      )
        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  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






@require_tf
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Union[str, Any]          =	(
           (
               TFDebertaModel,
               TFDebertaForMaskedLM,
               TFDebertaForQuestionAnswering,
               TFDebertaForSequenceClassification,
               TFDebertaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       __UpperCamelCase				:			Union[str, Any]          =	(
           {
               'feature-extraction': TFDebertaModel,
               'fill-mask': TFDebertaForMaskedLM,
               'question-answering': TFDebertaForQuestionAnswering,
               'text-classification': TFDebertaForSequenceClassification,
               'token-classification': TFDebertaForTokenClassification,
               'zero-shot': TFDebertaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __UpperCamelCase				:			int          =	False
       __UpperCamelCase				:			Optional[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							TFDebertaModelTester(self      )
        lowerCAmelCase__      :							Optional[int]    =							ConfigTester(self ,				config_class=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							TFDebertaModel.from_pretrained("kamalkraj/deberta-base"      )
        self.assertIsNotNone(snake_case      )






@require_tf
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @unittest.skip(reason="Model not available yet"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							TFDebertaModel.from_pretrained("kamalkraj/deberta-base"      )
        lowerCAmelCase__      :							List[str]    =							tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      )
        lowerCAmelCase__      :							str    =							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
        lowerCAmelCase__      :							Tuple    =							model(snake_case ,				attention_mask=snake_case      )[0]

        lowerCAmelCase__      :							List[Any]    =							tf.constant(
            [
                [
                    [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                    [1.4_484_025, -0.93_483_794, -0.80_593_085],
                    [0.3_122_741, 0.00_316_059, -1.4_131_377],
                ]
            ]      )
        tf.debugging.assert_near(output[:, 1:4, 1:4] ,				snake_case ,				atol=1e-4      )









"""simple docstring"""

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, 2_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class  __a				:





       """simple docstring"""


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





        """simple docstring"""

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

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          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:
         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__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							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" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							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)






