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    :  Tuple										= False
__lowerCAmelCase    :  Tuple										= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__lowerCAmelCase    :  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 				lowerCamelCase			:






      def __init__(      self  ,		__lowerCamelCase=sys.argv[1:]						)     ->				Optional[Any]:


          '''simple docstring'''







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

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






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Tuple         =   int(file.split("""."""						)[0].split("""_"""						)[-1]						)
          snake_case:						List[Any]         =   os.path.join(self.inputdir  ,		__lowerCamelCase						)
          return (img_id, filepath)






      @property
      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Dict         =   []
          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:
                  snake_case:						int         =   batch
                  snake_case:						Any         =   []
                  yield list(map(__lowerCamelCase  ,		zip(*__lowerCamelCase						)						)						)

          for i in range(1						):
              yield list(map(__lowerCamelCase  ,		zip(*__lowerCamelCase						)						)						)






      def __call__(      self						)     ->				Optional[int]:


          '''simple docstring'''







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





def   a_					(_lowerCAmelCase  :   Any       )->				Tuple:
    try:
        snake_case:						Dict         =   json.load(_lowerCAmelCase       )
        try:
            snake_case:						List[str]         =   list(data.keys()       )
        except Exception:
            snake_case:						Union[str, Any]         =   [d["""img_id"""] for d in data]
    except Exception:
        try:
            snake_case:						int         =   eval(stream.read()       )
        except Exception:
            snake_case:						Dict         =   stream.read().split("""\n"""       )
    return data


if __name__ == "__main__":
    __lowerCAmelCase    :  Dict										= 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)



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 				lowerCamelCase			:
      __lowerCamelCase							=					MarianConfig
      __lowerCamelCase							=					{}
      __lowerCamelCase							=					'gelu'






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase=13  ,		__lowerCamelCase=7  ,		__lowerCamelCase=True  ,		__lowerCamelCase=False  ,		__lowerCamelCase=99  ,		__lowerCamelCase=32  ,		__lowerCamelCase=2  ,		__lowerCamelCase=4  ,		__lowerCamelCase=37  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=20  ,		__lowerCamelCase=2  ,		__lowerCamelCase=1  ,		__lowerCamelCase=0  ,		)     ->				Any:


          '''simple docstring'''







          snake_case:						Any         =   parent
          snake_case:						List[Any]         =   batch_size
          snake_case:						int         =   seq_length
          snake_case:						Tuple         =   is_training
          snake_case:						Union[str, Any]         =   use_labels
          snake_case:						Union[str, Any]         =   vocab_size
          snake_case:						Dict         =   hidden_size
          snake_case:						Any         =   num_hidden_layers
          snake_case:						List[Any]         =   num_attention_heads
          snake_case:						str         =   intermediate_size

          snake_case:						Union[str, Any]         =   hidden_dropout_prob
          snake_case:						List[Any]         =   attention_probs_dropout_prob
          snake_case:						Tuple         =   max_position_embeddings
          snake_case:						Optional[int]         =   eos_token_id
          snake_case:						Optional[Any]         =   pad_token_id
          snake_case:						Optional[Any]         =   bos_token_id






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case:						Tuple         =   ids_tensor([self.batch_size, self.seq_length - 1]  ,		self.vocab_size						)
          snake_case:						Union[str, Any]         =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)  ,		1						)
          snake_case:						int         =   tf.concat([input_ids, eos_tensor]  ,		axis=1						)

          snake_case:						Optional[int]         =   ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

          snake_case:						List[str]         =   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  ,		)
          snake_case:						Tuple         =   prepare_marian_inputs_dict(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          return config, inputs_dict






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				List[str]:


          '''simple docstring'''







          snake_case:						Any         =   TFMarianModel(config=__lowerCamelCase						).get_decoder()
          snake_case:						str         =   inputs_dict["""input_ids"""]

          snake_case:						Union[str, Any]         =   input_ids[:1, :]
          snake_case:						Union[str, Any]         =   inputs_dict["""attention_mask"""][:1, :]
          snake_case:						str         =   inputs_dict["""head_mask"""]
          snake_case:						Optional[Any]         =   1

          # first forward pass
          snake_case:						int         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		head_mask=__lowerCamelCase  ,		use_cache=__lowerCamelCase						)

          snake_case    , snake_case:						List[str]         =   outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          snake_case:						Optional[int]         =   ids_tensor((self.batch_size, 3)  ,		config.vocab_size						)
          snake_case:						List[Any]         =   tf.cast(ids_tensor((self.batch_size, 3)  ,		2						)  ,		tf.inta						)

          # append to next input_ids and
          snake_case:						Optional[int]         =   tf.concat([input_ids, next_tokens]  ,		axis=-1						)
          snake_case:						Dict         =   tf.concat([attention_mask, next_attn_mask]  ,		axis=-1						)

          snake_case:						Optional[Any]         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase						)[0]
          snake_case:						Any         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		past_key_values=__lowerCamelCase						)[0]

          self.parent.assertEqual(next_tokens.shape[1]  ,		output_from_past.shape[1]						)

          # select random slice
          snake_case:						str         =   int(ids_tensor((1,)  ,		output_from_past.shape[-1]						)						)
          snake_case:						List[Any]         =   output_from_no_past[:, -3:, random_slice_idx]
          snake_case:						Optional[int]         =   output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(__lowerCamelCase  ,		__lowerCamelCase  ,		rtol=1e-3						)





def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Tuple=None			,	_lowerCAmelCase  :   Optional[int]=None			,	_lowerCAmelCase  :   Optional[int]=None			,	_lowerCAmelCase  :   Optional[Any]=None			,	_lowerCAmelCase  :   List[str]=None			,	)->				Any:
    if attention_mask is None:
        snake_case:						str         =   tf.cast(tf.math.not_equal(_lowerCAmelCase			,	config.pad_token_id       )			,	tf.inta       )
    if decoder_attention_mask is None:
        snake_case:						Dict         =   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:
        snake_case:						Union[str, Any]         =   tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        snake_case:						Optional[Any]         =   tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        snake_case:						Optional[int]         =   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 				lowerCamelCase			(							__snake_case				,     __snake_case				,     unittest.TestCase					):
      __lowerCamelCase							=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      __lowerCamelCase							=					(TFMarianMTModel,) if is_tf_available() else ()
      __lowerCamelCase							=					(
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      __lowerCamelCase							=					True
      __lowerCamelCase							=					False
      __lowerCamelCase							=					False






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						int         =   TFMarianModelTester(self						)
          snake_case:						Tuple         =   ConfigTester(self  ,		config_class=__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          self.config_tester.run_common_tests()






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case:						int         =   self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase						)





@require_tf
class 				lowerCamelCase			(							unittest.TestCase					):
      __lowerCamelCase							=					1_000  # show more chars for failing integration tests






      @classmethod
      def 			lowerCAmelCase_   (      cls						)     ->				None:


          '''simple docstring'''







          snake_case:						Any         =   F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls






      @cached_property
      def 			lowerCAmelCase_   (      self						)     ->				MarianTokenizer:


          '''simple docstring'''







          return AutoTokenizer.from_pretrained(self.model_name						)






      @property
      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          return self.tokenizer.eos_token_id






      @cached_property
      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          warnings.simplefilter("""error"""						)
          snake_case:						TFMarianMTModel         =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
          assert isinstance(__lowerCamelCase  ,		__lowerCamelCase						)
          snake_case:						Optional[Any]         =   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 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   self.translate_src_text(**__lowerCamelCase						)
          self.assertListEqual(self.expected_text  ,		__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Any         =   self.tokenizer(self.src_text  ,		**__lowerCamelCase  ,		padding=__lowerCamelCase  ,		return_tensors="""tf"""						)
          snake_case:						int         =   self.model.generate(
              model_inputs.input_ids  ,		attention_mask=model_inputs.attention_mask  ,		num_beams=2  ,		max_length=1_28						)
          snake_case:						Any         =   self.tokenizer.batch_decode(generated_ids.numpy()  ,		skip_special_tokens=__lowerCamelCase						)
          return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'mt'
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      __lowerCamelCase							=					['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					'zh'
      __lowerCamelCase							=					['My name is Wolfgang and I live in Berlin']
      __lowerCamelCase							=					['我叫沃尔夫冈 我住在柏林']






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					'ROMANCE'
      __lowerCamelCase							=					[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      __lowerCamelCase							=					[
          '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 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						List[str]         =   TranslationPipeline(self.model  ,		self.tokenizer  ,		framework="""tf"""						)
          snake_case:						List[Any]         =   pipeline(self.src_text						)
          self.assertEqual(self.expected_text  ,		[x["""translation_text"""] for x in output]						)


