
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  lowercase						:      List[str]							=							"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=None      ,			SCREAMING_SNAKE_CASE__=None       )				->					Any:
       if attention_mask is None:
              lowercase							:  Optional[Any] 			=   np.where(input_ids != config.pad_token_id      ,			1      ,			0       )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }

@require_flax
class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=13	,snake_case=7	,snake_case=True	,snake_case=False	,snake_case=99	,snake_case=16	,snake_case=2	,snake_case=4	,snake_case=4	,snake_case="gelu"	,snake_case=0.1	,snake_case=0.1	,snake_case=20	,snake_case=2	,snake_case=1	,snake_case=0	,snake_case=16	,snake_case=16	,snake_case=0.02	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   parent
           lowercase							:  List[str] 			=   batch_size
           lowercase							:  Any 			=   seq_length
           lowercase							:  Dict 			=   is_training
           lowercase							:  Dict 			=   use_labels
           lowercase							:  str 			=   vocab_size
           lowercase							:  List[str] 			=   hidden_size
           lowercase							:  Dict 			=   num_hidden_layers
           lowercase							:  str 			=   num_attention_heads
           lowercase							:  Dict 			=   intermediate_size
           lowercase							:  int 			=   hidden_act
           lowercase							:  int 			=   hidden_dropout_prob
           lowercase							:  Optional[Any] 			=   attention_probs_dropout_prob
           lowercase							:  Optional[int] 			=   max_position_embeddings
           lowercase							:  Union[str, Any] 			=   eos_token_id
           lowercase							:  List[str] 			=   pad_token_id
           lowercase							:  Union[str, Any] 			=   bos_token_id
           lowercase							:  str 			=   embed_dim
           lowercase							:  Union[str, Any] 			=   word_embed_proj_dim
           lowercase							:  Tuple 			=   initializer_range
           lowercase							:  List[Any] 			=   False
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  str 			=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,self.vocab_size   )	,3	,self.vocab_size   )
           lowercase							:  Dict 			=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,dtype=np.intaa   ))	,-1   )

           lowercase							:  Tuple 			=   OPTConfig(
               vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,ffn_dim=self.intermediate_size	,dropout=self.hidden_dropout_prob	,attention_dropout=self.attention_probs_dropout_prob	,max_position_embeddings=self.max_position_embeddings	,eos_token_id=self.eos_token_id	,bos_token_id=self.bos_token_id	,pad_token_id=self.pad_token_id	,embed_dim=self.embed_dim	,is_encoder_decoder=snake_case	,word_embed_proj_dim=self.word_embed_proj_dim	,initializer_range=self.initializer_range	,use_cache=snake_case	,)
           lowercase							:  Optional[Any] 			=   prepare_opt_inputs_dict(snake_case	,snake_case   )
           return config, inputs_dict
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.prepare_config_and_inputs()
           return config, inputs_dict
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   20
           lowercase							:  Any 			=   model_class_name(snake_case   )

           lowercase							:  Optional[int] 			=   inputs_dict["""input_ids"""]
           lowercase							:  str 			=   inputs_dict["""attention_mask"""]

           lowercase							:  Any 			=   model.init_cache(input_ids.shape[0]	,snake_case   )
           lowercase							:  Any 			=   jnp.ones((input_ids.shape[0], max_length)	,dtype="""i4"""   )

           lowercase							:  Optional[int] 			=   jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1   )[None, :]	,(input_ids.shape[0], input_ids.shape[-1] - 1)	,)
           lowercase							:  str 			=   model(
               input_ids[:, :-1]	,attention_mask=snake_case	,past_key_values=snake_case	,position_ids=snake_case	,)

           lowercase							:  Union[str, Any] 			=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,dtype="""i4"""   )
           lowercase							:  Optional[int] 			=   model(
               input_ids[:, -1:]	,attention_mask=snake_case	,past_key_values=outputs_cache.past_key_values	,position_ids=snake_case	,)

           lowercase							:  int 			=   model(snake_case   )

           lowercase							:  Tuple 			=   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    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   20
           lowercase							:  List[str] 			=   model_class_name(snake_case   )

           lowercase							:  str 			=   (
               inputs_dict["""input_ids"""],
               inputs_dict["""attention_mask"""],
           )

           lowercase							:  Tuple 			=   jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])   ),
               ]	,axis=-1	,)

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

           lowercase							:  Any 			=   model(
               input_ids[:, :-1]	,attention_mask=snake_case	,past_key_values=snake_case	,position_ids=snake_case	,)
           lowercase							:  Tuple 			=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,dtype="""i4"""   )
           lowercase							:  List[Any] 			=   model(
               input_ids[:, -1:]	,past_key_values=outputs_cache.past_key_values	,attention_mask=snake_case	,position_ids=snake_case	,)

           lowercase							:  Optional[Any] 			=   model(snake_case	,attention_mask=snake_case   )

           lowercase							:  Tuple 			=   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 __snake_case	(   lowerCAmelCase      ,			unittest.TestCase      ,			lowerCAmelCase  ):
    _a	:		Tuple=       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    _a	:		Optional[int]=       () if is_flax_available() else ()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   FlaxOPTModelTester(self   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  str 			=   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(snake_case	,snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward_with_attn_mask(snake_case	,snake_case	,snake_case   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           for model_class_name in self.all_model_classes:
                  lowercase							:  str 			=   model_class_name.from_pretrained("""facebook/opt-125m"""   )
                  lowercase							:  str 			=   np.ones((1, 1)   ) * model.config.eos_token_id
                  lowercase							:  Tuple 			=   model(snake_case   )
                  self.assertIsNotNone(snake_case   )






@require_sentencepiece
@require_flax
class __snake_case	(   unittest.TestCase  ):
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   FlaxOPTModel.from_pretrained("""facebook/opt-350m"""   )
           lowercase							:  List[Any] 			=   jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   )
           lowercase							:  Optional[Any] 			=   model(input_ids=snake_case   ).last_hidden_state
           lowercase							:  str 			=   (1, 11, 512)
           self.assertEqual(output.shape	,snake_case   )
           lowercase							:  Optional[Any] 			=   jnp.array(
               [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]   )
           self.assertTrue(jnp.allclose(output[:, :3, :3]	,snake_case	,atol=4e-2   )   )






@require_flax
@slow
class __snake_case	(   unittest.TestCase  ):
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().setUp()
           lowercase							:  List[str] 			=   """facebook/opt-350m"""
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   FlaxOPTForCausalLM.from_pretrained(self.path_model   )
           lowercase							:  Optional[Any] 			=   GPTaTokenizer.from_pretrained(self.path_model   )

           lowercase							:  str 			=   [
               """Today is a beautiful day and I want to""",
               """In the city of""",
               """Paris is the capital of France and""",
               """Computers and mobile phones have taken""",
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           lowercase							:  Optional[int] 			=   tokenizer(snake_case	,return_tensors="""jax"""	,padding=snake_case	,add_special_tokens=snake_case   )
           lowercase							:  Dict 			=   model(inputs.input_ids	,attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
           lowercase							:  List[str] 			=   jnp.array(
               [
                   [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                   [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                   [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                   [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
               ]   )
           self.assertTrue(jnp.allclose(snake_case	,snake_case	,atol=4e-2   )   )

           lowercase							:  Optional[int] 			=   jax.jit(snake_case   )
           lowercase							:  int 			=   model(inputs.input_ids	,attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
           self.assertTrue(jnp.allclose(snake_case	,snake_case	,atol=4e-2   )   )






@require_flax
@slow
class __snake_case	(   unittest.TestCase  ):
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   """facebook/opt-125m"""

           lowercase							:  Dict 			=   [
               """Today is a beautiful day and I want to""",
               """In the city of New York, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowercase							:  List[Any] 			=   []

           lowercase							:  str 			=   FlaxOPTForCausalLM.from_pretrained(snake_case   )
           lowercase							:  Dict 			=   GPTaTokenizer.from_pretrained(snake_case   )

           for prompt in self.prompts:
                  lowercase							:  Any 			=   tokenizer(snake_case	,return_tensors="""jax"""   ).input_ids

                  lowercase							:  Tuple 			=   model.generate(snake_case	,max_length=10   )
                  lowercase							:  int 			=   generated_ids[0]

                  lowercase							:  List[str] 			=   tokenizer.batch_decode(snake_case	,skip_special_tokens=snake_case   )
                  predicted_outputs += generated_string

           self.assertListEqual(snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   """facebook/opt-350m"""

           lowercase							:  int 			=   [
               """Today is a beautiful day and I want to""",
               """In the city of San Francisco, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowercase							:  Dict 			=   []
           lowercase							:  Dict 			=   FlaxOPTForCausalLM.from_pretrained(snake_case   )
           lowercase							:  List[Any] 			=   GPTaTokenizer.from_pretrained(snake_case   )

           for prompt in self.prompts:
                  lowercase							:  Tuple 			=   tokenizer(snake_case	,return_tensors="""jax"""   ).input_ids

                  lowercase							:  int 			=   model.generate(snake_case	,max_length=10   )
                  lowercase							:  Optional[Any] 			=   generated_ids[0]

                  lowercase							:  str 			=   tokenizer.batch_decode(snake_case	,skip_special_tokens=snake_case   )
                  predicted_outputs += generated_string

           self.assertListEqual(snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   """facebook/opt-125m"""
           lowercase							:  Dict 			=   [
               """Today is a beautiful day and I want to thank""",
               """In the city of Rome Canaver Canaver Canaver Canaver""",
           ]
           lowercase							:  List[str] 			=   FlaxOPTForCausalLM.from_pretrained(snake_case   )
           lowercase							:  List[Any] 			=   GPTaTokenizer.from_pretrained(snake_case   )
           lowercase							:  Any 			=   tokenizer(
               [
                   """Today is a beautiful day and I want to""",
                   """In the city of""",
               ]	,return_tensors="""jax"""	,padding=snake_case	,)

           lowercase							:  Optional[int] 			=   jax.jit(model.generate   )

           lowercase							:  str 			=   jit_generate(inputs["""input_ids"""]	,attention_mask=inputs["""attention_mask"""]   ).sequences

           lowercase							:  Any 			=   tokenizer.batch_decode(snake_case	,skip_special_tokens=snake_case   )

           self.assertIsNotNone(snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   """facebook/opt-350m"""

           lowercase							:  int 			=   GPTaTokenizer.from_pretrained(snake_case   )
           lowercase							:  Tuple 			=   FlaxOPTForCausalLM.from_pretrained(snake_case   )

           lowercase							:  Optional[Any] 			=   """left"""

           # use different length sentences to test batching
           lowercase							:  Any 			=   [
               """Hello, my dog is a little""",
               """Today, I""",
           ]

           lowercase							:  int 			=   tokenizer(snake_case	,return_tensors="""jax"""	,padding=snake_case   )
           lowercase							:  Dict 			=   inputs["""input_ids"""]

           lowercase							:  Dict 			=   model.generate(input_ids=snake_case	,attention_mask=inputs["""attention_mask"""]	,trace=snake_case   )

           lowercase							:  int 			=   tokenizer(sentences[0]	,return_tensors="""jax"""   ).input_ids
           lowercase							:  Tuple 			=   model.generate(input_ids=snake_case   )

           lowercase							:  List[str] 			=   inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
           lowercase							:  Tuple 			=   tokenizer(sentences[1]	,return_tensors="""jax"""   ).input_ids
           lowercase							:  Optional[Any] 			=   model.generate(input_ids=snake_case	,max_length=model.config.max_length - num_paddings   )

           lowercase							:  Tuple 			=   tokenizer.batch_decode(outputs[0]	,skip_special_tokens=snake_case   )
           lowercase							:  List[Any] 			=   tokenizer.decode(output_non_padded[0][0]	,skip_special_tokens=snake_case   )
           lowercase							:  str 			=   tokenizer.decode(output_padded[0][0]	,skip_special_tokens=snake_case   )

           lowercase							:  str 			=   [
               """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
               """Today, I was in the middle of a conversation with a friend about the""",
           ]
           self.assertListEqual(snake_case	,snake_case   )
           self.assertListEqual(snake_case	,[non_padded_sentence, padded_sentence]   )


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
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







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

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[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(snake_case   )   )
                  if len(snake_case   ) == self.batch:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         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:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   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"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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