



"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXLMForMultipleChoice,
         TFXLMForQuestionAnsweringSimple,
         TFXLMForSequenceClassification,
         TFXLMForTokenClassification,
         TFXLMModel,
         TFXLMWithLMHeadModel,
         XLMConfig,
     )


class 	a__		:


   def __init__(				self   :				List[str],  lowerCAmelCase   :				Dict,  )     ->							Optional[int]:
         lowercase	:   Optional[int]											= parent
         lowercase	:   List[str]											= 13
         lowercase	:   Optional[int]											= 7
         lowercase	:   Dict											= True
         lowercase	:   Union[str, Any]											= True
         lowercase	:   Any											= True
         lowercase	:   Union[str, Any]											= True
         lowercase	:   Tuple											= True
         lowercase	:   Union[str, Any]											= False
         lowercase	:   int											= False
         lowercase	:   Tuple											= False
         lowercase	:   Union[str, Any]											= 2
         lowercase	:   Union[str, Any]											= 99
         lowercase	:   int											= 0
         lowercase	:   Tuple											= 32
         lowercase	:   List[Any]											= 2
         lowercase	:   int											= 4
         lowercase	:   str											= 0.1
         lowercase	:   int											= 0.1
         lowercase	:   int											= 512
         lowercase	:   int											= 16
         lowercase	:   str											= 2
         lowercase	:   List[Any]											= 0.02
         lowercase	:   List[str]											= 3
         lowercase	:   Optional[int]											= 4
         lowercase	:   Any											= """last"""
         lowercase	:   List[Any]											= True
         lowercase	:   List[str]											= None
         lowercase	:   Optional[Any]											= 0


   def 							lowercase					(				self   :				str      )     ->							int:
         lowercase	:   Union[str, Any]											= ids_tensor([self.batch_size, self.seq_length],  self.vocab_size      )
         lowercase	:   List[Any]											= random_attention_mask([self.batch_size, self.seq_length],  dtype=tf.floataa      )

         lowercase	:   List[Any]											= None
         if self.use_input_lengths:
               lowercase	:   Optional[int]											= (
                   ids_tensor([self.batch_size],  vocab_size=2      ) + self.seq_length - 2
               )  # small variation of seq_length

         lowercase	:   Optional[Any]											= None
         if self.use_token_type_ids:
               lowercase	:   Any											= ids_tensor([self.batch_size, self.seq_length],  self.n_langs      )

         lowercase	:   List[Any]											= None
         lowercase	:   Any											= None
         lowercase	:   List[str]											= None
         if self.use_labels:
               lowercase	:   str											= ids_tensor([self.batch_size],  self.type_sequence_label_size      )
               lowercase	:   int											= ids_tensor([self.batch_size, self.seq_length],  self.num_labels      )
               lowercase	:   Union[str, Any]											= ids_tensor([self.batch_size],  2,  dtype=tf.floataa      )
               lowercase	:   str											= ids_tensor([self.batch_size],  self.num_choices      )

         lowercase	:   Union[str, Any]											= XLMConfig(
             vocab_size=self.vocab_size,  n_special=self.n_special,  emb_dim=self.hidden_size,  n_layers=self.num_hidden_layers,  n_heads=self.num_attention_heads,  dropout=self.hidden_dropout_prob,  attention_dropout=self.attention_probs_dropout_prob,  gelu_activation=self.gelu_activation,  sinusoidal_embeddings=self.sinusoidal_embeddings,  asm=self.asm,  causal=self.causal,  n_langs=self.n_langs,  max_position_embeddings=self.max_position_embeddings,  initializer_range=self.initializer_range,  summary_type=self.summary_type,  use_proj=self.use_proj,  bos_token_id=self.bos_token_id,  )

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				int,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				int,  lowerCAmelCase   :				str,  )     ->							List[str]:
         lowercase	:   Dict											= TFXLMModel(config=lowerCamelCase_      )
         lowercase	:   List[Any]											= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
         lowercase	:   Optional[Any]											= model(lowerCamelCase_      )

         lowercase	:   Any											= [input_ids, input_mask]
         lowercase	:   int											= model(lowerCamelCase_      )
         self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)      )


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Any,  lowerCAmelCase   :				str,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				str,  )     ->							Tuple:
         lowercase	:   Tuple											= TFXLMWithLMHeadModel(lowerCamelCase_      )

         lowercase	:   Optional[int]											= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
         lowercase	:   Any											= model(lowerCamelCase_      )

         lowercase	:   Any											= outputs

         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)      )


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				int,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Dict,  )     ->							List[Any]:
         lowercase	:   List[str]											= TFXLMForQuestionAnsweringSimple(lowerCamelCase_      )

         lowercase	:   str											= {"""input_ids""": input_ids, """lengths""": input_lengths}

         lowercase	:   Tuple											= model(lowerCamelCase_      )

         self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)      )
         self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)      )


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				int,  )     ->							Dict:
         lowercase	:   Optional[int]											= TFXLMForSequenceClassification(lowerCamelCase_      )

         lowercase	:   List[str]											= {"""input_ids""": input_ids, """lengths""": input_lengths}

         lowercase	:   Tuple											= model(lowerCamelCase_      )

         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)      )


   def 							lowercase					(				self   :				int,  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				int,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Any,  )     ->							Dict:
         lowercase	:   List[Any]											= self.num_labels
         lowercase	:   Optional[Any]											= TFXLMForTokenClassification(config=lowerCamelCase_      )
         lowercase	:   Tuple											= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         lowercase	:   int											= model(lowerCamelCase_      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)      )


   def 							lowercase					(				self   :				int,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				str,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				str,  )     ->							int:
         lowercase	:   str											= self.num_choices
         lowercase	:   Optional[int]											= TFXLMForMultipleChoice(config=lowerCamelCase_      )
         lowercase	:   Union[str, Any]											= tf.tile(tf.expand_dims(lowerCamelCase_,  1      ),  (1, self.num_choices, 1)      )
         lowercase	:   List[str]											= tf.tile(tf.expand_dims(lowerCamelCase_,  1      ),  (1, self.num_choices, 1)      )
         lowercase	:   Optional[Any]											= tf.tile(tf.expand_dims(lowerCamelCase_,  1      ),  (1, self.num_choices, 1)      )
         lowercase	:   List[str]											= {
             """input_ids""": multiple_choice_inputs_ids,
             """attention_mask""": multiple_choice_input_mask,
             """token_type_ids""": multiple_choice_token_type_ids,
         }
         lowercase	:   int											= model(lowerCamelCase_      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)      )






   def 							lowercase					(				self   :				Optional[Any]      )     ->							int:
         lowercase	:   Dict											= self.prepare_config_and_inputs()
         (
             lowercase 
         )	:   Tuple											= config_and_inputs
         lowercase	:   Dict											= {
             """input_ids""": input_ids,
             """token_type_ids""": token_type_ids,
             """langs""": token_type_ids,
             """lengths""": input_lengths,
         }
         return config, inputs_dict




@require_tf
class 	a__		(    __snake_case,	__snake_case,	unittest.TestCase  ):
   _lowerCamelCase       				=					(
       (
           TFXLMModel,
           TFXLMWithLMHeadModel,
           TFXLMForSequenceClassification,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForTokenClassification,
           TFXLMForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   _lowerCamelCase       				=					(
       (TFXLMWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   _lowerCamelCase       				=					(
       {
           "feature-extraction": TFXLMModel,
           "fill-mask": TFXLMWithLMHeadModel,
           "question-answering": TFXLMForQuestionAnsweringSimple,
           "text-classification": TFXLMForSequenceClassification,
           "text-generation": TFXLMWithLMHeadModel,
           "token-classification": TFXLMForTokenClassification,
           "zero-shot": TFXLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				int,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Optional[int]      )     ->							Union[str, Any]:
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('Fast'      )
         ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

         return False


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Dict:
         lowercase	:   Dict											= TFXLMModelTester(self      )
         lowercase	:   Dict											= ConfigTester(self,  config_class=lowerCamelCase_,  emb_dim=37      )


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							Union[str, Any]:
         self.config_tester.run_common_tests()


   def 							lowercase					(				self   :				int      )     ->							Dict:
         lowercase	:   Dict											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*lowerCamelCase_      )


   def 							lowercase					(				self   :				int      )     ->							Dict:
         lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase_      )


   def 							lowercase					(				self   :				List[str]      )     ->							Dict:
         lowercase	:   Optional[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_qa(*lowerCamelCase_      )


   def 							lowercase					(				self   :				List[str]      )     ->							Any:
         lowercase	:   List[str]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase_      )


   def 							lowercase					(				self   :				int      )     ->							Optional[Any]:
         lowercase	:   Optional[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_token_classification(*lowerCamelCase_      )


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							Dict:
         lowercase	:   Union[str, Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase_      )






   @slow
   def 							lowercase					(				self   :				Optional[int]      )     ->							Optional[Any]:
         for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase	:   Tuple											= TFXLMModel.from_pretrained(lowerCamelCase_      )
               self.assertIsNotNone(lowerCamelCase_      )




@require_tf
class 	a__		(    unittest.TestCase  ):


   @slow
   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[str]:
         lowercase	:   Tuple											= TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
         lowercase	:   Union[str, Any]											= tf.convert_to_tensor([[14, 447]],  dtype=tf.intaa      )  # the president
         lowercase	:   List[Any]											= [
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
             14,
             447,
         ]  # the president the president the president the president the president the president the president the president the president the president
         # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
         lowercase	:   Optional[int]											= model.generate(lowerCamelCase_,  do_sample=lowerCamelCase_      )
         self.assertListEqual(output_ids[0].numpy().tolist(),  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
UpperCamelCase_									=   False
UpperCamelCase_									=   Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCamelCase_									=   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				:






   '''simple docstring'''






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

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


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       int  		=					[]
       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:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

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






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







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					stream.read().split("""\n"""    )
    return data


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