
'''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]							)










'''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)



