code: 
  '''simple docstring'''
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
    import tensorflow as tf
  
    from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			:
   __lowercase     	=       MarianConfig
   __lowercase     	=       {}
   __lowercase     	=       '''gelu'''
   def __init__(       self   :List[str]		, lowercase_   :List[str]		, lowercase_   :List[Any]=13		, lowercase_   :Dict=7		, lowercase_   :List[Any]=True		, lowercase_   :Tuple=False		, lowercase_   :List[str]=99		, lowercase_   :str=32		, lowercase_   :int=2		, lowercase_   :Optional[int]=4		, lowercase_   :Union[str, Any]=37		, lowercase_   :List[str]=0.1		, lowercase_   :List[Any]=0.1		, lowercase_   :List[str]=20		, lowercase_   :Union[str, Any]=2		, lowercase_   :Dict=1		, lowercase_   :Dict=0		, )->							Optional[int]:
       A__       =    parent
       A__       =    batch_size
       A__       =    seq_length
       A__       =    is_training
       A__       =    use_labels
       A__       =    vocab_size
       A__       =    hidden_size
       A__       =    num_hidden_layers
       A__       =    num_attention_heads
       A__       =    intermediate_size
  
       A__       =    hidden_dropout_prob
       A__       =    attention_probs_dropout_prob
       A__       =    max_position_embeddings
       A__       =    eos_token_id
       A__       =    pad_token_id
       A__       =    bos_token_id
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Any:
       A__       =    ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size							)
       A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		, 1							)
       A__       =    tf.concat([input_ids, eos_tensor]		, axis=1							)
  
       A__       =    ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size							)
  
       A__       =    self.config_cls(
           vocab_size=self.vocab_size		, d_model=self.hidden_size		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_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_ids=[2]		, bos_token_id=self.bos_token_id		, pad_token_id=self.pad_token_id		, decoder_start_token_id=self.pad_token_id		, **self.config_updates		, )
       A__       =    prepare_marian_inputs_dict(lowercase_		, lowercase_		, lowercase_							)
       return config, inputs_dict
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Any		, lowercase_   :Any							)->							Any:
       A__       =    TFMarianModel(config=lowercase_							).get_decoder()
       A__       =    inputs_dict["input_ids"]
  
       A__       =    input_ids[:1, :]
       A__       =    inputs_dict["attention_mask"][:1, :]
       A__       =    inputs_dict["head_mask"]
       A__       =    1
  
       # first forward pass
       A__       =    model(lowercase_		, attention_mask=lowercase_		, head_mask=lowercase_		, use_cache=lowercase_							)
  
       A__,			A__       =    outputs.to_tuple()
  
       # create hypothetical next token and extent to next_input_ids
       A__       =    ids_tensor((self.batch_size, 3)		, config.vocab_size							)
       A__       =    tf.cast(ids_tensor((self.batch_size, 3)		, 2							)		, tf.inta							)
  
       # append to next input_ids and
       A__       =    tf.concat([input_ids, next_tokens]		, axis=-1							)
       A__       =    tf.concat([attention_mask, next_attn_mask]		, axis=-1							)
  
       A__       =    model(lowercase_		, attention_mask=lowercase_							)[0]
       A__       =    model(lowercase_		, attention_mask=lowercase_		, past_key_values=lowercase_							)[0]
  
       self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]							)
  
       # select random slice
       A__       =    int(ids_tensor((1,)		, output_from_past.shape[-1]							)							)
       A__       =    output_from_no_past[:, -3:, random_slice_idx]
       A__       =    output_from_past[:, :, random_slice_idx]
  
       # test that outputs are equal for slice
       tf.debugging.assert_near(lowercase_		, lowercase_		, rtol=1E-3							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      str		, _lowerCamelCase      :      Tuple=None		, _lowerCamelCase      :      Optional[int]=None		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      str=None		, ):
  
  
  
  
  
  
  
      if attention_mask is None:
          A__       =    tf.cast(tf.math.not_equal(lowercase__		, config.pad_token_id )		, tf.inta )
      if decoder_attention_mask is None:
          A__       =    tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape		, dtype=tf.inta ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		, config.pad_token_id )		, tf.inta ),
              ]		, axis=-1		, )
      if head_mask is None:
          A__       =    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
          A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
          A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			( A_    , A_    , unittest.TestCase       ):
   __lowercase     	=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   __lowercase     	=       (TFMarianMTModel,) if is_tf_available() else ()
   __lowercase     	=       (
       {
           '''conversational''': TFMarianMTModel,
           '''feature-extraction''': TFMarianModel,
           '''summarization''': TFMarianMTModel,
           '''text2text-generation''': TFMarianMTModel,
           '''translation''': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   __lowercase     	=       True
   __lowercase     	=       False
   __lowercase     	=       False
   def   UpperCAmelCase_							(       self   :List[Any]							)->							List[Any]:
       A__       =    TFMarianModelTester(self							)
       A__       =    ConfigTester(self		, config_class=lowercase_							)
   def   UpperCAmelCase_							(       self   :Dict							)->							List[str]:
       self.config_tester.run_common_tests()
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple							)->							List[str]:
       A__       =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*lowercase_							)
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       1000  # show more chars for failing integration tests
   @classmethod
   def   UpperCAmelCase_							(       cls   :Any							)->							Any:
       A__       =    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
       return cls
   @cached_property
   def   UpperCAmelCase_							(       self   :Any							)->							Dict:
       return AutoTokenizer.from_pretrained(self.model_name							)
   @property
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Tuple:
       return self.tokenizer.eos_token_id
   @cached_property
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Dict:
       warnings.simplefilter("error"							)
       A__       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       assert isinstance(lowercase_		, lowercase_							)
       A__       =    model.config
       self.assertListEqual(c.bad_words_ids		, [[c.pad_token_id]]							)
       self.assertEqual(c.max_length		, 5_12							)
       self.assertEqual(c.decoder_start_token_id		, c.pad_token_id							)
       return model
   def   UpperCAmelCase_							(       self   :Tuple		, **lowercase_   :Dict							)->							Optional[int]:
       A__       =    self.translate_src_text(**lowercase_							)
       self.assertListEqual(self.expected_text		, lowercase_							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[int]		, **lowercase_   :Dict							)->							Optional[Any]:
       A__       =    self.tokenizer(self.src_text		, **lowercase_		, padding=lowercase_		, return_tensors="tf"							)
       A__       =    self.model.generate(
           model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, max_length=1_28							)
       A__       =    self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=lowercase_							)
       return generated_words
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( A_       ):
   __lowercase     	=       '''mt'''
   __lowercase     	=       '''en'''
   __lowercase     	=       ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
   __lowercase     	=       ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
       self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( A_       ):
   __lowercase     	=       '''en'''
   __lowercase     	=       '''zh'''
   __lowercase     	=       ['''My name is Wolfgang and I live in Berlin''']
   __lowercase     	=       ['''我叫沃尔夫冈 我住在柏林''']
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							List[str]:
       self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( A_       ):
   __lowercase     	=       '''en'''
   __lowercase     	=       '''ROMANCE'''
   __lowercase     	=       [
       '''>>fr<< Don\'t spend so much time watching TV.''',
       '''>>pt<< Your message has been sent.''',
       '''>>es<< He\'s two years older than me.''',
   ]
   __lowercase     	=       [
       '''Ne passez pas autant de temps à regarder la télé.''',
       '''A sua mensagem foi enviada.''',
       '''Es dos años más viejo que yo.''',
   ]
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Tuple							)->							Dict:
       self._assert_generated_batch_equal_expected()
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Tuple							)->							Optional[int]:
       A__       =    TranslationPipeline(self.model		, self.tokenizer		, framework="tf"							)
       A__       =    pipeline(self.src_text							)
       self.assertEqual(self.expected_text		, [x["translation_text"] for x in output]							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import getopt
  import json
  import os
  
  # import numpy as np
  import sys
  from collections import OrderedDict
  
  import datasets
  import numpy as np
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  
  from utils import Config
  __lowerCAmelCase							:			str				=False
  __lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  __lowerCAmelCase							:			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, 2048), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
       A__       =    None
       A__       =    None
       A__       =    None
       A__       =    1
       A__,			A__       =    getopt.getopt(lowercase_		, "i:o:b:s"		, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               A__       =    arg
           elif opt in ("-o", "--outfile"):
               A__       =    arg
           elif opt in ("-b", "--batch_size"):
               A__       =    int(lowercase_							)
           elif opt in ("-s", "--subset_list"):
               A__       =    arg
  
       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(lowercase_							), F"{outputfile}"
       if subset_list is not None:
           with open(os.path.realpath(lowercase_							)							) as f:
               A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
       else:
           A__       =    None
  
       A__       =    CONFIG
       if torch.cuda.is_available():
           A__       =    "cuda"
       A__       =    os.path.realpath(lowercase_							)
       A__       =    os.path.realpath(lowercase_							)
       A__       =    Preprocess(self.config							)
       A__       =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"		, config=self.config							)
       A__       =    batch_size if batch_size != 0 else 1
       A__       =    DEFAULT_SCHEMA
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
       A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
       A__       =    os.path.join(self.inputdir		, lowercase_							)
       return (img_id, filepath)
   @property
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
       A__       =    []
       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(lowercase_							)							)
           if len(lowercase_							) == self.batch:
               A__       =    batch
               A__       =    []
               yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
       for i in range(1							):
           yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
  
  
   def __call__(       self   :Optional[int]							)->							List[str]:
       # make writer
       if not TEST:
           A__       =    datasets.ArrowWriter(features=self.schema		, path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           A__,			A__,			A__       =    self.preprocess(lowercase_							)
           A__       =    self.model(
               lowercase_		, lowercase_		, scales_yx=lowercase_		, padding="max_detections"		, max_detections=self.config.MAX_DETECTIONS		, pad_value=0		, return_tensors="np"		, location="cpu"		, )
           A__       =    output_dict.pop("normalized_boxes"							)
           if not TEST:
               A__       =    np.array(lowercase_							)
               A__       =    self.schema.encode_batch(lowercase_							)
               writer.write_batch(lowercase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           A__,			A__       =    writer.finalize()
           print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      try:
          A__       =    json.load(_lowerCamelCase )
          try:
              A__       =    list(data.keys() )
          except Exception:
              A__       =    [d["img_id"] for d in data]
      except Exception:
          try:
              A__       =    eval(stream.read() )
          except Exception:
              A__       =    stream.read().split("\n" )
      return data
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			int				=Extract(sys.argv[1:])
    extract()
    if not TEST:
      __lowerCAmelCase							:			Optional[Any]				=datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  