





'''simple docstring'''




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )


class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=13						,	_UpperCAmelCase=7						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=99						,	_UpperCAmelCase=32						,	_UpperCAmelCase=5						,	_UpperCAmelCase=4						,	_UpperCAmelCase=37						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=16						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=4						,	):

          '''simple docstring'''






          __A						: List[str]      =       parent
          __A						: List[str]      =       batch_size
          __A						: Union[str, Any]      =       seq_length
          __A						: int      =       is_training
          __A						: List[Any]      =       use_attention_mask
          __A						: List[Any]      =       use_token_type_ids
          __A						: Dict      =       use_labels
          __A						: int      =       vocab_size
          __A						: Tuple      =       hidden_size
          __A						: str      =       num_hidden_layers
          __A						: str      =       num_attention_heads
          __A						: Union[str, Any]      =       intermediate_size
          __A						: List[str]      =       hidden_act
          __A						: int      =       hidden_dropout_prob
          __A						: str      =       attention_probs_dropout_prob
          __A						: List[Any]      =       max_position_embeddings
          __A						: Tuple      =       type_vocab_size
          __A						: int      =       type_sequence_label_size
          __A						: Optional[int]      =       initializer_range
          __A						: Union[str, Any]      =       num_choices


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: Any      =       None
          if self.use_attention_mask:
               __A						: Optional[Any]      =       random_attention_mask([self.batch_size, self.seq_length])

          __A						: Optional[int]      =       None
          if self.use_token_type_ids:
               __A						: Optional[int]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

          __A						: Dict      =       RoFormerConfig(
              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						,	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=_UpperCAmelCase						,	initializer_range=self.initializer_range						,	)

          return config, input_ids, token_type_ids, attention_mask


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.prepare_config_and_inputs()
          __A ,__A ,__A ,__A						: Union[str, Any]      =       config_and_inputs
          __A						: Any      =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




@require_flax
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       True

     lowerCAmelCase					=       (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       FlaxRoFormerModelTester(self)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A						: Optional[int]      =       model_class_name.from_pretrained('junnyu/roformer_chinese_small'						,	from_pt=_UpperCAmelCase)
               __A						: Tuple      =       model(np.ones((1, 1)))
               self.assertIsNotNone(_UpperCAmelCase)




@require_flax
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
          __A						: Optional[Any]      =       jnp.array([[0, 1, 2, 3, 4, 5]])
          __A						: Optional[Any]      =       model(_UpperCAmelCase)[0]

          __A						: Dict      =       5_0000

          __A						: List[str]      =       (1, 6, vocab_size)
          self.assertEqual(output.shape						,	_UpperCAmelCase)

          __A						: str      =       jnp.array(
              [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

          self.assertTrue(jnp.allclose(output[:, :3, :3]						,	_UpperCAmelCase						,	atol=1e-4))






'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:


  class  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       MODEL_FOR_OBJECT_DETECTION_MAPPING


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'						,	threshold=0.0)

          self.assertGreater(len(_UpperCAmelCase)						,	0)
          for detected_object in outputs:
               self.assertEqual(
                   _UpperCAmelCase						,	{
                       'score': ANY(_UpperCAmelCase),
                       'label': ANY(_UpperCAmelCase),
                       'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                   }						,	)

          import datasets

          __A						: Tuple      =       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,	'image'						,	split='test')

          __A						: List[str]      =       [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              # RGBA
              dataset[0]['file'],
              # LA
              dataset[1]['file'],
              # L
              dataset[2]['file'],
          ]
          __A						: Union[str, Any]      =       object_detector(_UpperCAmelCase						,	threshold=0.0)

          self.assertEqual(len(_UpperCAmelCase)						,	len(_UpperCAmelCase))
          for outputs in batch_outputs:
               self.assertGreater(len(_UpperCAmelCase)						,	0)
               for detected_object in outputs:
                    self.assertEqual(
                        _UpperCAmelCase						,	{
                            'score': ANY(_UpperCAmelCase),
                            'label': ANY(_UpperCAmelCase),
                            'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                        }						,	)


     @require_tf
     @unittest.skip('Object detection not implemented in TF')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       'hf-internal-testing/tiny-detr-mobilenetsv3'

          __A						: Any      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: int      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: Any      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Dict      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=0.0)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
              ]						,	)

          __A						: Optional[Any]      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ]						,	threshold=0.0						,	)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       'facebook/detr-resnet-50'

          __A						: Union[str, Any]      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: Tuple      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: List[Any]      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Union[str, Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: Tuple      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: Any      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: str      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       0.9985
          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: List[str]      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: List[Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=_UpperCAmelCase)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)


     @require_torch
     @require_pytesseract
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       'Narsil/layoutlmv3-finetuned-funsd'
          __A						: Tuple      =       0.9993

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase						,	threshold=_UpperCAmelCase)

          __A						: Optional[int]      =       object_detector(
              'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
              ]						,	)