


import glob
import os
import random
from string import ascii_lowercase, digits

import cva
snake_case__				:	int					       =					''
snake_case__				:	Optional[Any]					       =					''
snake_case__				:	Dict					       =					''
snake_case__				:	Union[str, Any]					       =					1  # (0 is vertical, 1 is horizontal)


def    __lowerCamelCase   (      )     ->    None:
   lowerCamelCase_,	lowerCamelCase_						:   Tuple		= get_dataset(A__						,						A__					)
   print("""Processing..."""					)
   lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   int		= update_image_and_anno(A__						,						A__						,						A__					)

   for index, image in enumerate(A__					):
      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      lowerCamelCase_						:   Any		= random_chars(32					)
      lowerCamelCase_						:   Dict		= paths[index].split(os.sep					)[-1].rsplit("""."""						,						1					)[0]
      lowerCamelCase_						:   Dict		= f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
      cva.imwrite(f'''/{file_root}.jpg'''						,						A__						,						[cva.IMWRITE_JPEG_QUALITY, 85]					)
      print(f'''Success {index+1}/{len(A__					)} with {file_name}'''					)
      lowerCamelCase_						:   Optional[int]		= []
      for anno in new_annos[index]:
         lowerCamelCase_						:   Union[str, Any]		= f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
         annos_list.append(A__					)
      with open(f'''/{file_root}.txt'''						,						"""w"""					) as outfile:
         outfile.write("""\n""".join(line for line in annos_list					)					)


def    __lowerCamelCase   (      A__						: str						,						A__						: str					)     ->    tuple[list, list]:
   lowerCamelCase_						:   Dict		= []
   lowerCamelCase_						:   str		= []
   for label_file in glob.glob(os.path.join(A__						,						"""*.txt"""					)					):
      lowerCamelCase_						:   Optional[Any]		= label_file.split(os.sep					)[-1].rsplit("""."""						,						1					)[0]
      with open(A__					) as in_file:
         lowerCamelCase_						:   List[Any]		= in_file.readlines()
      lowerCamelCase_						:   Dict		= os.path.join(A__						,						f'''{label_name}.jpg'''					)

      lowerCamelCase_						:   Optional[Any]		= []
      for obj_list in obj_lists:
         lowerCamelCase_						:   Union[str, Any]		= obj_list.rstrip("""\n"""					).split(""" """					)
         boxes.append(
             [
                 int(obj[0]					),
                 float(obj[1]					),
                 float(obj[2]					),
                 float(obj[3]					),
                 float(obj[4]					),
             ]					)
      if not boxes:
         continue
      img_paths.append(A__					)
      labels.append(A__					)
   return img_paths, labels


def    __lowerCamelCase   (      A__						: list						,						A__						: list						,						A__						: int = 1					)     ->    tuple[list, list, list]:
   lowerCamelCase_						:   Union[str, Any]		= []
   lowerCamelCase_						:   str		= []
   lowerCamelCase_						:   Any		= []
   for idx in range(len(A__					)					):
      lowerCamelCase_						:   Optional[int]		= []
      lowerCamelCase_						:   List[Any]		= img_list[idx]
      path_list.append(A__					)
      lowerCamelCase_						:   Tuple		= anno_list[idx]
      lowerCamelCase_						:   Optional[Any]		= cva.imread(A__					)
      if flip_type == 1:
         lowerCamelCase_						:   Tuple		= cva.flip(A__						,						A__					)
         for bbox in img_annos:
            lowerCamelCase_						:   Any		= 1 - bbox[1]
            new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]					)
      elif flip_type == 0:
         lowerCamelCase_						:   str		= cva.flip(A__						,						A__					)
         for bbox in img_annos:
            lowerCamelCase_						:   Any		= 1 - bbox[2]
            new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]					)
      new_annos_lists.append(A__					)
      new_imgs_list.append(A__					)
   return new_imgs_list, new_annos_lists, path_list


def    __lowerCamelCase   (      A__						: int = 32					)     ->    str:
   assert number_char > 1, "The number of character should greater than 1"
   lowerCamelCase_						:   List[str]		= ascii_lowercase + digits
   return "".join(random.choice(A__					) for _ in range(A__					)					)


if __name__ == "__main__":
      main()
      print('DONE ✅')









import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertModel,
      )





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				str=13		,				__a				:				Tuple=7		,				__a				:				int=True		,				__a				:				int=True		,				__a				:				Dict=True		,				__a				:				str=True		,				__a				:				List[str]=99		,				__a				:				Dict=64		,				__a				:				Optional[Any]=32		,				__a				:				List[Any]=5		,				__a				:				Optional[int]=4		,				__a				:				str=37		,				__a				:				str="gelu"		,				__a				:				Optional[Any]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				int=512		,				__a				:				Optional[Any]=16		,				__a				:				Any=2		,				__a				:				Dict=0.02		,				__a				:				str=3		,				__a				:				List[Any]=4		,				__a				:				List[str]=None		,				)					->Optional[int]:
          lowerCamelCase_						:   Dict		= parent
          lowerCamelCase_						:   Optional[Any]		= batch_size
          lowerCamelCase_						:   Any		= seq_length
          lowerCamelCase_						:   Union[str, Any]		= is_training
          lowerCamelCase_						:   int		= use_input_mask
          lowerCamelCase_						:   int		= use_token_type_ids
          lowerCamelCase_						:   int		= use_labels
          lowerCamelCase_						:   List[str]		= vocab_size
          lowerCamelCase_						:   Any		= hidden_size
          lowerCamelCase_						:   Any		= embedding_size
          lowerCamelCase_						:   int		= num_hidden_layers
          lowerCamelCase_						:   Union[str, Any]		= num_attention_heads
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   Optional[int]		= hidden_act
          lowerCamelCase_						:   Dict		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Optional[int]		= max_position_embeddings
          lowerCamelCase_						:   Dict		= type_vocab_size
          lowerCamelCase_						:   List[str]		= type_sequence_label_size
          lowerCamelCase_						:   Any		= initializer_range
          lowerCamelCase_						:   Union[str, Any]		= num_labels
          lowerCamelCase_						:   List[Any]		= num_choices
          lowerCamelCase_						:   Dict		= scope



       def 				_lowerCAmelCase							(						self				:				int							)					->str:
          lowerCamelCase_						:   Dict		= ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

          lowerCamelCase_						:   Tuple		= None
          if self.use_input_mask:
             lowerCamelCase_						:   Any		= random_attention_mask([self.batch_size, self.seq_length]							)

          lowerCamelCase_						:   List[Any]		= None
          if self.use_token_type_ids:
             lowerCamelCase_						:   Any		= ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size							)

          lowerCamelCase_						:   List[Any]		= None
          lowerCamelCase_						:   Tuple		= None
          lowerCamelCase_						:   Optional[int]		= None
          if self.use_labels:
             lowerCamelCase_						:   str		= ids_tensor([self.batch_size]		,				self.type_sequence_label_size							)
             lowerCamelCase_						:   Optional[int]		= ids_tensor([self.batch_size, self.seq_length]		,				self.num_labels							)
             lowerCamelCase_						:   Any		= ids_tensor([self.batch_size]		,				self.num_choices							)

          lowerCamelCase_						:   str		= self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[Any]:
          return MobileBertConfig(
              vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				embedding_size=self.embedding_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				is_decoder=__a		,				initializer_range=self.initializer_range		,				)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				int		,				__a				:				List[str]		,				__a				:				Tuple		,				__a				:				Any		,				__a				:				Union[str, Any]		,				__a				:				int		,				__a				:				Any							)					->Tuple:
          lowerCamelCase_						:   Tuple		= MobileBertModel(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Dict		= model(__a		,				attention_mask=__a		,				token_type_ids=__a							)
          lowerCamelCase_						:   Optional[int]		= model(__a		,				token_type_ids=__a							)
          lowerCamelCase_						:   Tuple		= model(__a							)

          self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)
          self.parent.assertEqual(result.pooler_output.shape		,				(self.batch_size, self.hidden_size)							)



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				str		,				__a				:				Union[str, Any]		,				__a				:				int		,				__a				:				List[Any]		,				__a				:				Dict		,				__a				:				List[Any]		,				__a				:				List[str]							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= MobileBertForMaskedLM(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Union[str, Any]		= model(__a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Tuple		,				__a				:				Tuple		,				__a				:				Dict		,				__a				:				Tuple		,				__a				:				Optional[Any]		,				__a				:				Union[str, Any]		,				__a				:				Dict							)					->int:
          lowerCamelCase_						:   Tuple		= MobileBertForNextSentencePrediction(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Any		= model(
              __a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a		,				)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, 2)							)



       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				Optional[Any]		,				__a				:				int		,				__a				:				Optional[int]		,				__a				:				Optional[int]		,				__a				:				Optional[int]		,				__a				:				Optional[int]		,				__a				:				Dict							)					->List[Any]:
          lowerCamelCase_						:   Optional[int]		= MobileBertForPreTraining(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   List[Any]		= model(
              __a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a		,				next_sentence_label=__a		,				)
          self.parent.assertEqual(result.prediction_logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)
          self.parent.assertEqual(result.seq_relationship_logits.shape		,				(self.batch_size, 2)							)



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				Tuple		,				__a				:				int		,				__a				:				Optional[Any]		,				__a				:				Optional[Any]		,				__a				:				Tuple		,				__a				:				Optional[Any]		,				__a				:				Any							)					->List[str]:
          lowerCamelCase_						:   Dict		= MobileBertForQuestionAnswering(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Tuple		= model(
              __a		,				attention_mask=__a		,				token_type_ids=__a		,				start_positions=__a		,				end_positions=__a		,				)
          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 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				List[Any]		,				__a				:				str		,				__a				:				int		,				__a				:				Dict		,				__a				:				Dict		,				__a				:				List[Any]		,				__a				:				str							)					->Tuple:
          lowerCamelCase_						:   Dict		= self.num_labels
          lowerCamelCase_						:   Optional[Any]		= MobileBertForSequenceClassification(__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   str		= model(__a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				str		,				__a				:				List[str]		,				__a				:				Optional[int]		,				__a				:				int		,				__a				:				str		,				__a				:				str		,				__a				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   int		= self.num_labels
          lowerCamelCase_						:   List[str]		= MobileBertForTokenClassification(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Optional[Any]		= model(__a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				Any		,				__a				:				Tuple		,				__a				:				Dict		,				__a				:				Dict		,				__a				:				List[Any]		,				__a				:				Optional[int]		,				__a				:				Optional[Any]							)					->List[str]:
          lowerCamelCase_						:   Any		= self.num_choices
          lowerCamelCase_						:   int		= MobileBertForMultipleChoice(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Tuple		= input_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
          lowerCamelCase_						:   Tuple		= token_type_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
          lowerCamelCase_						:   Any		= input_mask.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
          lowerCamelCase_						:   Tuple		= model(
              __a		,				attention_mask=__a		,				token_type_ids=__a		,				labels=__a		,				)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)							)




       def 				_lowerCAmelCase							(						self				:				List[str]							)					->int:
          lowerCamelCase_						:   Optional[Any]		= self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	(
              lowerCamelCase_
          ),	
          )						:   Optional[Any]		= config_and_inputs
          lowerCamelCase_						:   List[Any]		= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict







@require_torch
class   SCREAMING_SNAKE_CASE_			(a__						,       a__						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							(
           (
               MobileBertModel,
               MobileBertForMaskedLM,
               MobileBertForMultipleChoice,
               MobileBertForNextSentencePrediction,
               MobileBertForPreTraining,
               MobileBertForQuestionAnswering,
               MobileBertForSequenceClassification,
               MobileBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       _a   							=							(
           {
               "feature-extraction": MobileBertModel,
               "fill-mask": MobileBertForMaskedLM,
               "question-answering": MobileBertForQuestionAnswering,
               "text-classification": MobileBertForSequenceClassification,
               "token-classification": MobileBertForTokenClassification,
               "zero-shot": MobileBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _a   							=							True



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				Union[str, Any]		,				__a				:				Dict		,				__a				:				str=False							)					->Any:
          lowerCamelCase_						:   Optional[int]		= super()._prepare_for_class(__a		,				__a		,				return_labels=__a							)

          if return_labels:
             if model_class in get_values(__a							):
                lowerCamelCase_						:   str		= torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length)		,				dtype=torch.long		,				device=__a							)
                lowerCamelCase_						:   str		= torch.zeros(
                    self.model_tester.batch_size		,				dtype=torch.long		,				device=__a							)
          return inputs_dict



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   List[Any]		= MobileBertModelTester(self							)
          lowerCamelCase_						:   int		= ConfigTester(self		,				config_class=__a		,				hidden_size=37							)



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



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          lowerCamelCase_						:   Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*__a							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Tuple:
          lowerCamelCase_						:   List[str]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a							)



       def 				_lowerCAmelCase							(						self				:				Any							)					->List[Any]:
          lowerCamelCase_						:   Any		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->str:
          lowerCamelCase_						:   List[str]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*__a							)



       def 				_lowerCAmelCase							(						self				:				str							)					->Optional[Any]:
          lowerCamelCase_						:   int		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*__a							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->int:
          lowerCamelCase_						:   Dict		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a							)




       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Dict		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*__a							)







def    __lowerCamelCase   (      A__						: List[str]					)     ->    Optional[int]:
   return torch.tensor(
       A__						,						dtype=torch.long						,						device=A__						,						)


snake_case__				:	List[str]					       =					1e-3





@require_torch
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_			(unittest.TestCase						):




       '''simple docstring'''



       @slow
       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[str]:
          lowerCamelCase_						:   Any		= MobileBertModel.from_pretrained("""google/mobilebert-uncased"""							).to(__a							)
          lowerCamelCase_						:   int		= _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]							)
          with torch.no_grad():
             lowerCamelCase_						:   Optional[Any]		= model(__a							)[0]
          lowerCamelCase_						:   Any		= torch.Size((1, 9, 512)							)
          self.assertEqual(output.shape		,				__a							)
          lowerCamelCase_						:   str		= torch.tensor(
              [
                  [
                      [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
                      [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
                      [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
                  ]
              ]		,				device=__a		,				)

          # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
          # ~1 difference, it's therefore not a good idea to measure using addition.
          # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
          # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
          lowerCamelCase_						:   str		= torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE							)
          lowerCamelCase_						:   Tuple		= torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE							)

          self.assertTrue(lower_bound and upper_bound							)





