"""simple docstring"""







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE    	=logging.getLogger(__name__)







@dataclass
class        UpperCamelCase		:
   lowercase							=  42
   lowercase							=  42
   lowercase							=  42







@dataclass
class        UpperCamelCase		:
   lowercase							=  42
   lowercase							=  42
   lowercase							=  None
   lowercase							=  None







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'train'
   lowercase							=  'dev'
   lowercase							=  'test'







class        UpperCamelCase		:
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase     ,__UpperCamelCase			)	->      List[InputExample]:


        '''simple docstring'''


        raise NotImplementedError
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        raise NotImplementedError







   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase=False     ,__UpperCamelCase="[CLS]"     ,__UpperCamelCase=1     ,__UpperCamelCase="[SEP]"     ,__UpperCamelCase=False     ,__UpperCamelCase=False     ,__UpperCamelCase=0     ,__UpperCamelCase=0     ,__UpperCamelCase=-100     ,__UpperCamelCase=0     ,__UpperCamelCase=True     ,)	->      List[InputFeatures]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	{label: i for i, label in enumerate(__UpperCamelCase			)}

        lowercase_	:				Optional[Any]													=	[]
        for ex_index, example in enumerate(__UpperCamelCase			):
             if ex_index % 1_0000 == 0:
                  logger.info('Writing example %d of %d'     ,__UpperCamelCase     ,len(__UpperCamelCase			)			)

             lowercase_	:				Dict													=	[]
             lowercase_	:				str													=	[]
             for word, label in zip(example.words     ,example.labels			):
                  lowercase_	:				Optional[int]													=	tokenizer.tokenize(__UpperCamelCase			)

                  # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                  if len(__UpperCamelCase			) > 0:
                       tokens.extend(__UpperCamelCase			)
                       # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                       label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__UpperCamelCase			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
             lowercase_	:				Dict													=	tokenizer.num_special_tokens_to_add()
             if len(__UpperCamelCase			) > max_seq_length - special_tokens_count:
                  lowercase_	:				Tuple													=	tokens[: (max_seq_length - special_tokens_count)]
                  lowercase_	:				Optional[int]													=	label_ids[: (max_seq_length - special_tokens_count)]

             # The convention in BERT is:
             # (a) For sequence pairs:
             #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
             #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
             # (b) For single sequences:
             #  tokens:   [CLS] the dog is hairy . [SEP]
             #  type_ids:   0   0   0   0  0     0   0
             #
             # Where "type_ids" are used to indicate whether this is the first
             # sequence or the second sequence. The embedding vectors for `type=0` and
             # `type=1` were learned during pre-training and are added to the wordpiece
             # embedding vector (and position vector). This is not *strictly* necessary
             # since the [SEP] token unambiguously separates the sequences, but it makes
             # it easier for the model to learn the concept of sequences.
             #
             # For classification tasks, the first vector (corresponding to [CLS]) is
             # used as the "sentence vector". Note that this only makes sense because
             # the entire model is fine-tuned.
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
             if sep_token_extra:
                  # roberta uses an extra separator b/w pairs of sentences
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
             lowercase_	:				Optional[Any]													=	[sequence_a_segment_id] * len(__UpperCamelCase			)

             if cls_token_at_end:
                  tokens += [cls_token]
                  label_ids += [pad_token_label_id]
                  segment_ids += [cls_token_segment_id]
             else:
                  lowercase_	:				str													=	[cls_token] + tokens
                  lowercase_	:				List[str]													=	[pad_token_label_id] + label_ids
                  lowercase_	:				Dict													=	[cls_token_segment_id] + segment_ids

             lowercase_	:				Optional[Any]													=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)

             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             lowercase_	:				int													=	[1 if mask_padding_with_zero else 0] * len(__UpperCamelCase			)

             # Zero-pad up to the sequence length.
             lowercase_	:				Optional[int]													=	max_seq_length - len(__UpperCamelCase			)
             if pad_on_left:
                  lowercase_	:				str													=	([pad_token] * padding_length) + input_ids
                  lowercase_	:				List[Any]													=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                  lowercase_	:				List[str]													=	([pad_token_segment_id] * padding_length) + segment_ids
                  lowercase_	:				Tuple													=	([pad_token_label_id] * padding_length) + label_ids
             else:
                  input_ids += [pad_token] * padding_length
                  input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                  segment_ids += [pad_token_segment_id] * padding_length
                  label_ids += [pad_token_label_id] * padding_length

             assert len(__UpperCamelCase			) == max_seq_length
             assert len(__UpperCamelCase			) == max_seq_length
             assert len(__UpperCamelCase			) == max_seq_length
             assert len(__UpperCamelCase			) == max_seq_length

             if ex_index < 5:
                  logger.info('*** Example ***'			)
                  logger.info('guid: %s'     ,example.guid			)
                  logger.info('tokens: %s'     ,' '.join([str(__UpperCamelCase			) for x in tokens]			)			)
                  logger.info('input_ids: %s'     ,' '.join([str(__UpperCamelCase			) for x in input_ids]			)			)
                  logger.info('input_mask: %s'     ,' '.join([str(__UpperCamelCase			) for x in input_mask]			)			)
                  logger.info('segment_ids: %s'     ,' '.join([str(__UpperCamelCase			) for x in segment_ids]			)			)
                  logger.info('label_ids: %s'     ,' '.join([str(__UpperCamelCase			) for x in label_ids]			)			)

             if "token_type_ids" not in tokenizer.model_input_names:
                  lowercase_	:				Optional[int]													=	None

             features.append(
                 InputFeatures(
                     input_ids=__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,token_type_ids=__UpperCamelCase     ,label_ids=__UpperCamelCase			)			)
        return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset







       class        UpperCamelCase		(			lowercase_  ):
          lowercase							=  42
          lowercase							=  nn.CrossEntropyLoss().ignore_index
          def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase=False     ,__UpperCamelCase = Split.train     ,)	->      Tuple:


               '''simple docstring'''


               lowercase_	:				int													=	os.path.join(
                   __UpperCamelCase     ,'cached_{}_{}_{}'.format(mode.value     ,tokenizer.__class__.__name__     ,str(__UpperCamelCase			)			)     ,)

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               lowercase_	:				List[Any]													=	cached_features_file + '.lock'
               with FileLock(__UpperCamelCase			):
                    if os.path.exists(__UpperCamelCase			) and not overwrite_cache:
                         logger.info(f'''Loading features from cached file {cached_features_file}'''			)
                         lowercase_	:				Union[str, Any]													=	torch.load(__UpperCamelCase			)
                    else:
                         logger.info(f'''Creating features from dataset file at {data_dir}'''			)
                         lowercase_	:				Union[str, Any]													=	token_classification_task.read_examples_from_file(__UpperCamelCase     ,__UpperCamelCase			)
                         # TODO clean up all this to leverage built-in features of tokenizers
                         lowercase_	:				Any													=	token_classification_task.convert_examples_to_features(
                             __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,cls_token_at_end=bool(model_type in ['xlnet']			)     ,cls_token=tokenizer.cls_token     ,cls_token_segment_id=2 if model_type in ['xlnet'] else 0     ,sep_token=tokenizer.sep_token     ,sep_token_extra=__UpperCamelCase     ,pad_on_left=bool(tokenizer.padding_side == 'left'			)     ,pad_token=tokenizer.pad_token_id     ,pad_token_segment_id=tokenizer.pad_token_type_id     ,pad_token_label_id=self.pad_token_label_id     ,)
                         logger.info(f'''Saving features into cached file {cached_features_file}'''			)
                         torch.save(self.features     ,__UpperCamelCase			)
          def __len__(			self			)	->      str:


               '''simple docstring'''


               return len(self.features			)







          def __getitem__(			self     ,__UpperCamelCase			)	->      InputFeatures:


               '''simple docstring'''


               return self.features[i]


if is_tf_available():
       import tensorflow as tf







       class        UpperCamelCase		:
          lowercase							=  42
          lowercase							=  -1_0_0
          def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase=False     ,__UpperCamelCase = Split.train     ,)	->      Optional[int]:


               '''simple docstring'''


               lowercase_	:				Optional[int]													=	token_classification_task.read_examples_from_file(__UpperCamelCase     ,__UpperCamelCase			)
               # TODO clean up all this to leverage built-in features of tokenizers
               lowercase_	:				str													=	token_classification_task.convert_examples_to_features(
                   __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,cls_token_at_end=bool(model_type in ['xlnet']			)     ,cls_token=tokenizer.cls_token     ,cls_token_segment_id=2 if model_type in ['xlnet'] else 0     ,sep_token=tokenizer.sep_token     ,sep_token_extra=__UpperCamelCase     ,pad_on_left=bool(tokenizer.padding_side == 'left'			)     ,pad_token=tokenizer.pad_token_id     ,pad_token_segment_id=tokenizer.pad_token_type_id     ,pad_token_label_id=self.pad_token_label_id     ,)

               def gen():
                    for ex in self.features:
                         if ex.token_type_ids is None:
                              yield (
                                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                  ex.label_ids,
                              )
                         else:
                              yield (
                                  {
                                      "input_ids": ex.input_ids,
                                      "attention_mask": ex.attention_mask,
                                      "token_type_ids": ex.token_type_ids,
                                  },
                                  ex.label_ids,
                              )

               if "token_type_ids" not in tokenizer.model_input_names:
                    lowercase_	:				str													=	tf.data.Dataset.from_generator(
                        __UpperCamelCase     ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)     ,(
                            {'input_ids': tf.TensorShape([None]			), 'attention_mask': tf.TensorShape([None]			)},
                            tf.TensorShape([None]			),
                        )     ,)
               else:
                    lowercase_	:				Dict													=	tf.data.Dataset.from_generator(
                        __UpperCamelCase     ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)     ,(
                            {
                                'input_ids': tf.TensorShape([None]			),
                                'attention_mask': tf.TensorShape([None]			),
                                'token_type_ids': tf.TensorShape([None]			),
                            },
                            tf.TensorShape([None]			),
                        )     ,)
          def 			_UpperCAmelCase   (			self			)	->      Dict:


               '''simple docstring'''


               lowercase_	:				int													=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

               return self.dataset
          def __len__(			self			)	->      Optional[int]:


               '''simple docstring'''


               return len(self.features			)







          def __getitem__(			self     ,__UpperCamelCase			)	->      InputFeatures:


               '''simple docstring'''


               return self.features[i]


"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:







       class        UpperCamelCase		:
          @staticmethod
          def 			_UpperCAmelCase   (			*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


               '''simple docstring'''


               pass







@is_pipeline_test
@require_vision
@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				int													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	[
            {
                'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                'candidate_labels': ['cat', 'remote', 'couch'],
            }
        ]
        return object_detector, examples
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	object_detector(examples[0]     ,threshold=0.0			)

        lowercase_	:				Any													=	len(__UpperCamelCase			)
        self.assertGreater(__UpperCamelCase     ,0			)
        self.assertEqual(
            __UpperCamelCase     ,[
                {
                    'score': ANY(__UpperCamelCase			),
                    'label': ANY(__UpperCamelCase			),
                    'box': {'xmin': ANY(__UpperCamelCase			), 'ymin': ANY(__UpperCamelCase			), 'xmax': ANY(__UpperCamelCase			), 'ymax': ANY(__UpperCamelCase			)},
                }
                for i in range(__UpperCamelCase			)
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        pass
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	object_detector(
            './tests/fixtures/tests_samples/COCO/000000039769.png'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                }
            ]     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                    {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                    {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                ]
            ]     ,)
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[str]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
            ]     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        pass
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	0.2
        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				str													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
            ]     ,)







   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Tuple													=	2
        lowercase_	:				Optional[Any]													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[Any]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,top_k=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
            ]     ,)

