import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Tuple							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Dict=1_4							,      lowerCamelCase							:		str=7							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Dict=False							,      lowerCamelCase							:		List[Any]=True							,      lowerCamelCase							:		int=9_9							,      lowerCamelCase							:		str=3_2							,      lowerCamelCase							:		Optional[Any]=4							,      lowerCamelCase							:		List[Any]=4							,      lowerCamelCase							:		List[str]=[[["global", "local"], 2]]							,      lowerCamelCase							:		str=3_7							,      lowerCamelCase							:		List[Any]="gelu"							,      lowerCamelCase							:		List[Any]=0.1							,      lowerCamelCase							:		Optional[int]=0.1							,      lowerCamelCase							:		Optional[Any]=5_1_2							,      lowerCamelCase							:		List[str]=7							,      lowerCamelCase							:		Optional[Any]=0.0_2							,      )			->				Optional[Any]:
        _UpperCamelCase  :							str	    =	parent
        _UpperCamelCase  :							int	    =	batch_size
        _UpperCamelCase  :							str	    =	seq_length
        _UpperCamelCase  :							List[str]	    =	is_training
        _UpperCamelCase  :							Any	    =	use_input_mask
        _UpperCamelCase  :							List[Any]	    =	use_token_type_ids
        _UpperCamelCase  :							Any	    =	use_labels
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size
        _UpperCamelCase  :							Dict	    =	hidden_size
        _UpperCamelCase  :							List[Any]	    =	num_hidden_layers
        _UpperCamelCase  :							Any	    =	num_attention_heads
        _UpperCamelCase  :							Tuple	    =	attention_types
        _UpperCamelCase  :							int	    =	intermediate_size
        _UpperCamelCase  :							Union[str, Any]	    =	hidden_act
        _UpperCamelCase  :							int	    =	hidden_dropout_prob
        _UpperCamelCase  :							Tuple	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							str	    =	max_position_embeddings
        _UpperCamelCase  :							Optional[Any]	    =	window_size
        _UpperCamelCase  :							Dict	    =	initializer_range
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size - 1
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size - 1
        _UpperCamelCase  :							List[str]	    =	vocab_size - 1

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Dict:
        _UpperCamelCase  :							int	    =	ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

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

        _UpperCamelCase  :							Optional[int]	    =	GPTNeoConfig(
            vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_layers=self.num_hidden_layers							,      num_heads=self.num_attention_heads							,      max_position_embeddings=self.max_position_embeddings							,      use_cache=lowerCamelCase							,      bos_token_id=self.bos_token_id							,      eos_token_id=self.eos_token_id							,      pad_token_id=self.pad_token_id							,      window_size=self.window_size							,      attention_types=self.attention_types							,      )

        return (config, input_ids, input_mask)

 def        __snake_case       (      self							:		Optional[int]	)			->				Tuple:
        _UpperCamelCase  :							Optional[int]	    =	self.prepare_config_and_inputs()
        _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	config_and_inputs
        _UpperCamelCase  :							Optional[Any]	    =	{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Tuple	)			->				Optional[Any]:
        _UpperCamelCase  :							Optional[int]	    =	2_0
        _UpperCamelCase  :							int	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							Tuple	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	jnp.ones((input_ids.shape[0], max_decoder_length)							,      dtype='i4'	)

        _UpperCamelCase  :							List[str]	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)
        _UpperCamelCase  :							Dict	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							str	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							str	    =	model(
            input_ids[:, -1:]							,      attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Any	    =	model(lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)

 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		int							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		str	)			->				int:
        _UpperCamelCase  :							Union[str, Any]	    =	2_0
        _UpperCamelCase  :							str	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]							,      axis=-1							,      )

        _UpperCamelCase  :							int	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							str	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)

        _UpperCamelCase  :							Union[str, Any]	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )
        _UpperCamelCase  :							Any	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							List[str]	    =	model(
            input_ids[:, -1:]							,      past_key_values=outputs_cache.past_key_values							,      attention_mask=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							List[str]	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)







@require_flax
class  __lowerCAmelCase	(							a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[str]					     =			(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 _lowerCamelCase    :						Optional[Any]					     =			(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

 def        __snake_case       (      self							:		Dict	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxGPTNeoModelTester(self	)

 def        __snake_case       (      self							:		Dict	)			->				Optional[Any]:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		int	)			->				Dict:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 @slow
 def        __snake_case       (      self							:		List[Any]	)			->				List[Any]:
        _UpperCamelCase  :							List[str]	    =	GPTaTokenizer.from_pretrained('gpt2'							,      pad_token='<|endoftext|>'							,      padding_side='left'	)
        _UpperCamelCase  :							str	    =	tokenizer(['Hello this is a long string', 'Hey']							,      return_tensors='np'							,      padding=lowerCamelCase							,      truncation=lowerCamelCase	)

        _UpperCamelCase  :							int	    =	FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'	)
        _UpperCamelCase  :							List[str]	    =	False
        _UpperCamelCase  :							List[str]	    =	model.config.eos_token_id

        _UpperCamelCase  :							Tuple	    =	jax.jit(model.generate	)

        _UpperCamelCase  :							Dict	    =	jit_generate(
            inputs['input_ids']							,      attention_mask=inputs['attention_mask']							,      pad_token_id=tokenizer.pad_token_id	).sequences

        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)

        _UpperCamelCase  :							int	    =	[
            'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
            'Hey, I\'m a little late to the party. I\'m going to',
        ]

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 @is_pt_flax_cross_test
 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      # prepare inputs
                      _UpperCamelCase  :							Tuple	    =	self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							Union[str, Any]	    =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      _UpperCamelCase  :							Dict	    =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      _UpperCamelCase  :							int	    =	getattr(lowerCamelCase							,      lowerCamelCase	)

                      _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	pt_inputs['input_ids'].shape
                      _UpperCamelCase  :							Optional[int]	    =	np.random.randint(0							,      seq_length - 1							,      size=(batch_size,)	)
                      for batch_idx, start_index in enumerate(lowerCamelCase	):
                             _UpperCamelCase  :							Optional[Any]	    =	0
                             _UpperCamelCase  :							str	    =	1
                             _UpperCamelCase  :							Union[str, Any]	    =	0
                             _UpperCamelCase  :							Dict	    =	1
                      _UpperCamelCase  :							Optional[int]	    =	pt_model_class(lowerCamelCase	).eval()
                      _UpperCamelCase  :							Dict	    =	model_class(lowerCamelCase							,      dtype=jnp.floataa	)

                      _UpperCamelCase  :							int	    =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,      lowerCamelCase	)
                      _UpperCamelCase  :							Dict	    =	fx_state

                      with torch.no_grad():
                             _UpperCamelCase  :							Dict	    =	pt_model(**lowerCamelCase	).to_tuple()

                      _UpperCamelCase  :							List[str]	    =	fx_model(**lowerCamelCase	).to_tuple()
                      self.assertEqual(len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(lowerCamelCase	)
                             _UpperCamelCase  :							Tuple	    =	model_class.from_pretrained(lowerCamelCase							,      from_pt=lowerCamelCase	)

                      _UpperCamelCase  :							List[Any]	    =	fx_model_loaded(**lowerCamelCase	).to_tuple()
                      self.assertEqual(
                          len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output_loaded, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output_loaded[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

 @is_pt_flax_cross_test
 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      # prepare inputs
                      _UpperCamelCase  :							Dict	    =	self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							List[str]	    =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      _UpperCamelCase  :							Dict	    =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      _UpperCamelCase  :							Any	    =	getattr(lowerCamelCase							,      lowerCamelCase	)

                      _UpperCamelCase  :							Union[str, Any]	    =	pt_model_class(lowerCamelCase	).eval()
                      _UpperCamelCase  :							Optional[Any]	    =	model_class(lowerCamelCase							,      dtype=jnp.floataa	)

                      _UpperCamelCase  :							Any	    =	load_flax_weights_in_pytorch_model(lowerCamelCase							,      fx_model.params	)
                      _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	pt_inputs['input_ids'].shape
                      _UpperCamelCase  :							Optional[Any]	    =	np.random.randint(0							,      seq_length - 1							,      size=(batch_size,)	)
                      for batch_idx, start_index in enumerate(lowerCamelCase	):
                             _UpperCamelCase  :							Optional[int]	    =	0
                             _UpperCamelCase  :							List[Any]	    =	1
                             _UpperCamelCase  :							Optional[int]	    =	0
                             _UpperCamelCase  :							int	    =	1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                             _UpperCamelCase  :							Tuple	    =	pt_model(**lowerCamelCase	).to_tuple()

                      _UpperCamelCase  :							Optional[int]	    =	fx_model(**lowerCamelCase	).to_tuple()
                      self.assertEqual(len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(lowerCamelCase	)
                             _UpperCamelCase  :							Tuple	    =	pt_model_class.from_pretrained(lowerCamelCase							,      from_flax=lowerCamelCase	)

                      with torch.no_grad():
                             _UpperCamelCase  :							Tuple	    =	pt_model_loaded(**lowerCamelCase	).to_tuple()

                      self.assertEqual(
                          len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

 @slow
 def        __snake_case       (      self							:		Optional[Any]	)			->				List[Any]:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase  :							Any	    =	model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'	)
               _UpperCamelCase  :							int	    =	model(np.ones((1, 1)	)	)
               self.assertIsNotNone(lowerCamelCase	)




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)



