
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 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self  , A  , A=13  , A=7  , A=True  , A=True  , A=True  , A=True  , A=99  , A=32  , A=2  , A=4  , A=37  , A="gelu"  , A=0.1  , A=0.1  , A=5_12  , A=16  , A=2  , A=0.02  , A=3  , A=4  , A=None  , )  ->Union[str, Any]:
									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    13
									UpperCAmelCase__		:Optional[Any] 		=    7
									UpperCAmelCase__		:Dict 		=    True
									UpperCAmelCase__		:List[Any] 		=    True
									UpperCAmelCase__		:Optional[int] 		=    True
									UpperCAmelCase__		:Union[str, Any] 		=    True
									UpperCAmelCase__		:Any 		=    99
									UpperCAmelCase__		:str 		=    32
									UpperCAmelCase__		:List[Any] 		=    2
									UpperCAmelCase__		:Any 		=    4
									UpperCAmelCase__		:Dict 		=    37
									UpperCAmelCase__		:Union[str, Any] 		=    'gelu'
									UpperCAmelCase__		:Tuple 		=    0.1
									UpperCAmelCase__		:int 		=    0.1
									UpperCAmelCase__		:Optional[Any] 		=    5_12
									UpperCAmelCase__		:Dict 		=    16
									UpperCAmelCase__		:Union[str, Any] 		=    False
									UpperCAmelCase__		:Any 		=    -1
									UpperCAmelCase__		:List[Any] 		=    True
									UpperCAmelCase__		:Tuple 		=    2
									UpperCAmelCase__		:Union[str, Any] 		=    0.02
									UpperCAmelCase__		:List[Any] 		=    3
									UpperCAmelCase__		:List[str] 		=    4
									UpperCAmelCase__		:List[Any] 		=    None


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:List[Any] 		=    ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size	)

									UpperCAmelCase__		:int 		=    None
									if self.use_input_mask:
												UpperCAmelCase__		:int 		=    random_attention_mask([self.batch_size, self.seq_length]	)

									UpperCAmelCase__		:Any 		=    None
									if self.use_token_type_ids:
												UpperCAmelCase__		:Optional[Any] 		=    ids_tensor([self.batch_size, self.seq_length]  , self.type_vocab_size	)

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

									UpperCAmelCase__		:str 		=    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=A  , )

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


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    TFDebertaModel(config=A	)
									UpperCAmelCase__		:int 		=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									UpperCAmelCase__		:str 		=    [input_ids, input_mask]
									UpperCAmelCase__		:int 		=    model(A	)

									UpperCAmelCase__		:Optional[Any] 		=    model(A	)

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


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A	)  ->Any:
									UpperCAmelCase__		:Tuple 		=    TFDebertaForMaskedLM(config=A	)
									UpperCAmelCase__		:int 		=    {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__		:Union[str, Any] 		=    model(A	)
									self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.vocab_size)	)


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.num_labels
									UpperCAmelCase__		:Union[str, Any] 		=    TFDebertaForSequenceClassification(config=A	)
									UpperCAmelCase__		:Optional[int] 		=    {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									UpperCAmelCase__		:Optional[Any] 		=    model(A	)
									self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_labels)	)


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A	)  ->List[Any]:
									UpperCAmelCase__		:Optional[int] 		=    self.num_labels
									UpperCAmelCase__		:int 		=    TFDebertaForTokenClassification(config=A	)
									UpperCAmelCase__		:Any 		=    {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__		:Union[str, Any] 		=    model(A	)
									self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.num_labels)	)


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A	)  ->List[Any]:
									UpperCAmelCase__		:Any 		=    TFDebertaForQuestionAnswering(config=A	)
									UpperCAmelCase__		:Optional[Any] 		=    {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									UpperCAmelCase__		:Dict 		=    model(A	)
									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    A__       (  self	)  ->List[str]:
									UpperCAmelCase__		:List[Any] 		=    self.prepare_config_and_inputs()
									(
									    (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       (
									    UpperCAmelCase__
									)		,       
									)		:List[str] 		=    config_and_inputs
									UpperCAmelCase__		:Tuple 		=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict


@require_tf
class 	UpperCamelCase__       (  UpperCAmelCase__						,			UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Optional[Any]												=			(
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						__a     :    List[Any]												=			(
						    {
						        """feature-extraction""": TFDebertaModel,
						        """fill-mask""": TFDebertaForMaskedLM,
						        """question-answering""": TFDebertaForQuestionAnswering,
						        """text-classification""": TFDebertaForSequenceClassification,
						        """token-classification""": TFDebertaForTokenClassification,
						        """zero-shot""": TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						__a     :    List[Any]												=			False
						__a     :    Optional[Any]												=			False


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:Union[str, Any] 		=    TFDebertaModelTester(self	)
									UpperCAmelCase__		:List[Any] 		=    ConfigTester(self  , config_class=A  , hidden_size=37	)


						def    A__       (  self	)  ->Optional[int]:
									self.config_tester.run_common_tests()


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A	)


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:str 		=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*A	)


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Any 		=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*A	)


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:List[str] 		=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*A	)


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*A	)


						@slow
						def    A__       (  self	)  ->List[str]:
									UpperCAmelCase__		:int 		=    TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
									self.assertIsNotNone(A	)


@require_tf
class 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						@unittest.skip(reason='Model not available yet'	)
						def    A__       (  self	)  ->Optional[Any]:
									pass


						@slow
						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:str 		=    TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
									UpperCAmelCase__		:str 		=    tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	)
									UpperCAmelCase__		:Optional[Any] 		=    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
									UpperCAmelCase__		:str 		=    model(A  , attention_mask=A	)[0]

									UpperCAmelCase__		:Dict 		=    tf.constant(
									    [
									        [
									            [-0.5985_5896, -0.8055_2566, -0.846_2135],
									            [1.448_4025, -0.9348_3794, -0.8059_3085],
									            [0.312_2741, 0.0031_6059, -1.413_1377],
									        ]
									    ]	)
									tf.debugging.assert_near(output[:, 1:4, 1:4]  , A  , atol=1e-4	)







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					:							Optional[int]           =							False
__snake_case					:							Union[str, Any]           =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__snake_case					:							Optional[int]           =							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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class 	UpperCamelCase__       :

						'''simple docstring'''



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

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

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


						def    A__       (  self  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:List[str] 		=    int(file.split('.'	)[0].split('_'	)[-1]	)
									UpperCAmelCase__		:Any 		=    os.path.join(self.inputdir  , A	)
									return (img_id, filepath)


						@property
						def    A__       (  self	)  ->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(A	)	)
												if len(A	) == self.batch:
															UpperCAmelCase__		:Optional[Any] 		=    batch
															UpperCAmelCase__		:Tuple 		=    []
															yield list(map(A  , zip(*A	)	)	)

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


						def __call__(  self	)  ->List[Any]:
									# make writer
									if not TEST:
												UpperCAmelCase__		:List[Any] 		=    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(A	)
												UpperCAmelCase__		:Tuple 		=    self.model(
												    A  , A  , scales_yx=A  , padding='max_detections'  , max_detections=self.config.MAX_DETECTIONS  , pad_value=0  , return_tensors='np'  , location='cpu'  , )
												UpperCAmelCase__		:Dict 		=    output_dict.pop('normalized_boxes'	)
												if not TEST:
															UpperCAmelCase__		:Union[str, Any] 		=    np.array(A	)
															UpperCAmelCase__		:Any 		=    self.schema.encode_batch(A	)
															writer.write_batch(A	)
												if TEST:
															break
												# finalizer the writer
									if not TEST:
												UpperCAmelCase__		,       UpperCAmelCase__		:Optional[Any] 		=    writer.finalize()
												print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""	)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			try:
						UpperCAmelCase__		:List[Any] 		=    json.load(SCREAMING_SNAKE_CASE							)
						try:
									UpperCAmelCase__		:Optional[Any] 		=    list(data.keys()							)
						except Exception:
									UpperCAmelCase__		:List[str] 		=    [d['img_id'] for d in data]
			except Exception:
						try:
									UpperCAmelCase__		:str 		=    eval(stream.read()							)
						except Exception:
									UpperCAmelCase__		:Optional[Any] 		=    stream.read().split('\n'							)
			return data


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





