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 __snake_case    :




      @staticmethod
      def      __a						(   *_lowercase      :    List[Any]	,     **_lowercase      :    List[Any]				):



        """simple docstring"""






        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case    (						unittest.TestCase		):
   lowerCAmelCase_														=							MODEL_FOR_OBJECT_DETECTION_MAPPING




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Dict	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     image_processor=_lowercase				)
     return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




   def      __a						(   self      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	,     threshold=0.0				)

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

     import datasets

     SCREAMING_SNAKE_CASE__      							=				datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""	,     """image"""	,     split="""test"""				)

     SCREAMING_SNAKE_CASE__      							=				[
         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"""],
     ]
     SCREAMING_SNAKE_CASE__      							=				object_detector(_lowercase	,     threshold=0.0				)

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




   @require_tf
   @unittest.skip("""Object detection not implemented in TF"""				)
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     pass




   @require_torch
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""hf-internal-testing/tiny-detr-mobilenetsv3"""

     SCREAMING_SNAKE_CASE__      							=				AutoModelForObjectDetection.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     feature_extractor=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,     threshold=0.0				)

     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]	,     threshold=0.0	,     )

     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
             [
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				AutoModelForObjectDetection.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     feature_extractor=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				0.99_85
     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,     threshold=_lowercase				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )




   @require_torch
   @require_pytesseract
   @slow
   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""Narsil/layoutlmv3-finetuned-funsd"""
     SCREAMING_SNAKE_CASE__      							=				0.99_93

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase	,     threshold=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
             {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
         ]	,     )

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :			Optional[int]							       =		{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[Any]							       =		['''WhisperTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''WhisperForConditionalGeneration''',
       '''WhisperModel''',
       '''WhisperPreTrainedModel''',
       '''WhisperForAudioClassification''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Tuple							       =		[
       '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWhisperForConditionalGeneration''',
       '''TFWhisperModel''',
       '''TFWhisperPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			List[Any]							       =		[
       '''FlaxWhisperForConditionalGeneration''',
       '''FlaxWhisperModel''',
       '''FlaxWhisperPreTrainedModel''',
       '''FlaxWhisperForAudioClassification''',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			Tuple							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
