'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
      import torch




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=14					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=True					,      lowerCAmelCase__=99					,      lowerCAmelCase__=32					,      lowerCAmelCase__=5					,      lowerCAmelCase__=4					,      lowerCAmelCase__=37					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=0.02					,      )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :int       =parent
        _UpperCamelCase  :int       =batch_size
        _UpperCamelCase  :str       =seq_length
        _UpperCamelCase  :int       =is_training
        _UpperCamelCase  :Dict       =use_input_mask
        _UpperCamelCase  :List[str]       =use_token_type_ids
        _UpperCamelCase  :int       =use_labels
        _UpperCamelCase  :Tuple       =vocab_size
        _UpperCamelCase  :Optional[Any]       =hidden_size
        _UpperCamelCase  :str       =num_hidden_layers
        _UpperCamelCase  :List[str]       =num_attention_heads
        _UpperCamelCase  :List[str]       =intermediate_size
        _UpperCamelCase  :Any       =hidden_act
        _UpperCamelCase  :Optional[Any]       =hidden_dropout_prob
        _UpperCamelCase  :int       =attention_probs_dropout_prob
        _UpperCamelCase  :str       =max_position_embeddings
        _UpperCamelCase  :Any       =initializer_range
        _UpperCamelCase  :Optional[Any]       =None
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Tuple       =vocab_size - 1



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Dict       =ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

        _UpperCamelCase  :Optional[int]       =None
        if self.use_input_mask:
             _UpperCamelCase  :List[Any]       =random_attention_mask([self.batch_size, self.seq_length]     )

        _UpperCamelCase  :Optional[Any]       =GPTaConfig(
            vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=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					,      )

        return (config, input_ids, input_mask)



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.prepare_config_and_inputs()

        _UpperCamelCase  :int       =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        _UpperCamelCase  :Union[str, Any]       =ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2     )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =20
        _UpperCamelCase  :Union[str, Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="""i4"""     )

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

        _UpperCamelCase  :int       =model(lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Dict       =20
        _UpperCamelCase  :List[str]       =model_class_name(lowerCAmelCase__     )

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

        _UpperCamelCase  :Tuple       =model(lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :int       =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__  (   __snake_case       ,				__snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   __UpperCAmelCase           =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =FlaxGPTaModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   @slow
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =GPTaTokenizer.from_pretrained("""gpt2"""					,      pad_token="""</s>"""					,      padding_side="""left"""     )
        _UpperCamelCase  :Dict       =tokenizer(["""Hello this is a long string""", """Hey"""]					,      return_tensors="""np"""					,      padding=lowerCAmelCase__					,      truncation=lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""     )
        _UpperCamelCase  :int       =False
        _UpperCamelCase  :Optional[int]       =model.config.eos_token_id

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

        _UpperCamelCase  :Dict       =jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]     ).sequences

        _UpperCamelCase  :Optional[int]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[
            """Hello this is a long string of words. I'm going to start with the first one.\n""",
            """Hey, I'm not sure if I'm going to be able to do""",
        ]

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   @is_pt_flax_cross_test
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =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  :Optional[Any]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Optional[int]       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

                  _UpperCamelCase  :Any       =convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      lowerCAmelCase__     )
                  _UpperCamelCase  :List[str]       =fx_state

                  with torch.no_grad():
                       _UpperCamelCase  :Optional[int]       =pt_model(**lowerCAmelCase__     ).to_tuple()

                  _UpperCamelCase  :List[Any]       =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  :Any       =model_class.from_pretrained(lowerCAmelCase__					,      from_pt=lowerCAmelCase__     )

                  _UpperCamelCase  :Optional[int]       =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  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =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  :Optional[int]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Tuple       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

                  _UpperCamelCase  :Tuple       =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  :Dict       =pt_model_class.from_pretrained(lowerCAmelCase__					,      from_flax=lowerCAmelCase__     )

                  with torch.no_grad():
                       _UpperCamelCase  :str       =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  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :Optional[int]       =model_class_name.from_pretrained("""gpt2"""					,      from_pt=lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =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
__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)





