'''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 		__A       :






          @staticmethod
          def 			_snake_case						(      *UpperCamelCase_				,     **UpperCamelCase_      ):
                 pass






@is_pipeline_test
@require_vision
@require_torch
class 		__A       (unittest.TestCase   ):
       snake_case       :Optional[Any]				    =					MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		pipeline(
                  "zero-shot-object-detection"				,     model="hf-internal-testing/tiny-random-owlvit-object-detection"      )

              __UpperCAmelCase  :				Optional[Any]       				=		[
                  {
                      "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                      "candidate_labels": ["cat", "remote", "couch"],
                  }
              ]
              return object_detector, examples






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		object_detector(examples[0]				,     threshold=0.0      )

              __UpperCAmelCase  :				Optional[int]       				=		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 			_snake_case						(      self      ):
              pass






       @require_torch
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		pipeline(
                  "zero-shot-object-detection"				,     model="hf-internal-testing/tiny-random-owlvit-object-detection"      )

              __UpperCAmelCase  :				Dict       				=		object_detector(
                  "./tests/fixtures/tests_samples/COCO/000000039769.png"				,     candidate_labels=["cat", "remote", "couch"]				,     threshold=0.6_4				,     )

              self.assertEqual(
                  nested_simplify(UpperCamelCase_				,     decimals=4      )				,     [
                      {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                      {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                      {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                      {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                      {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                      {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                      {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 4_94, "ymin": 1_05, "xmax": 5_21, "ymax": 1_27}},
                      {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 2_74, "xmax": 93, "ymax": 2_97}},
                      {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 4_94, "ymin": 1_05, "xmax": 5_21, "ymax": 1_27}},
                  ]				,     )

              __UpperCAmelCase  :				int       				=		object_detector(
                  [
                      {
                          "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                          "candidate_labels": ["cat", "remote", "couch"],
                      }
                  ]				,     threshold=0.6_4				,     )

              self.assertEqual(
                  nested_simplify(UpperCamelCase_				,     decimals=4      )				,     [
                      [
                          {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                          {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                          {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 2_04, "ymin": 1_67, "xmax": 2_32, "ymax": 1_90}},
                          {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                          {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                          {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 5_71, "ymin": 83, "xmax": 5_98, "ymax": 1_03}},
                          {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 4_94, "ymin": 1_05, "xmax": 5_21, "ymax": 1_27}},
                          {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 2_74, "xmax": 93, "ymax": 2_97}},
                          {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 4_94, "ymin": 1_05, "xmax": 5_21, "ymax": 1_27}},
                      ]
                  ]				,     )






       @require_torch
       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		pipeline("zero-shot-object-detection"      )

              __UpperCAmelCase  :				List[str]       				=		object_detector(
                  "http://images.cocodataset.org/val2017/000000039769.jpg"				,     candidate_labels=["cat", "remote", "couch"]				,     )
              self.assertEqual(
                  nested_simplify(UpperCamelCase_				,     decimals=4      )				,     [
                      {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 3_24, "ymin": 20, "xmax": 6_40, "ymax": 3_73}},
                      {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 1_77, "ymax": 1_15}},
                      {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 3_15, "ymax": 4_72}},
                      {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 3_35, "ymin": 74, "xmax": 3_71, "ymax": 1_87}},
                      {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_42, "ymax": 4_76}},
                  ]				,     )

              __UpperCAmelCase  :				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": 0.2_8_6_8, "label": "cat", "box": {"xmin": 3_24, "ymin": 20, "xmax": 6_40, "ymax": 3_73}},
                          {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 1_77, "ymax": 1_15}},
                          {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 3_15, "ymax": 4_72}},
                          {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 3_35, "ymin": 74, "xmax": 3_71, "ymax": 1_87}},
                          {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_42, "ymax": 4_76}},
                      ],
                      [
                          {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 3_24, "ymin": 20, "xmax": 6_40, "ymax": 3_73}},
                          {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 1_77, "ymax": 1_15}},
                          {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 3_15, "ymax": 4_72}},
                          {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 3_35, "ymin": 74, "xmax": 3_71, "ymax": 1_87}},
                          {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_42, "ymax": 4_76}},
                      ],
                  ]				,     )






       @require_tf
       @unittest.skip("Zero Shot Object Detection not implemented in TF"      )
       def 			_snake_case						(      self      ):
              pass






       @require_torch
       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		0.2
              __UpperCAmelCase  :				Union[str, Any]       				=		pipeline("zero-shot-object-detection"      )

              __UpperCAmelCase  :				int       				=		object_detector(
                  "http://images.cocodataset.org/val2017/000000039769.jpg"				,     candidate_labels=["cat", "remote", "couch"]				,     threshold=UpperCamelCase_				,     )
              self.assertEqual(
                  nested_simplify(UpperCamelCase_				,     decimals=4      )				,     [
                      {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 3_24, "ymin": 20, "xmax": 6_40, "ymax": 3_73}},
                      {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 1_77, "ymax": 1_15}},
                      {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 3_15, "ymax": 4_72}},
                  ]				,     )







       @require_torch
       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		2
              __UpperCAmelCase  :				Optional[int]       				=		pipeline("zero-shot-object-detection"      )

              __UpperCAmelCase  :				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": 0.2_8_6_8, "label": "cat", "box": {"xmin": 3_24, "ymin": 20, "xmax": 6_40, "ymax": 3_73}},
                      {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 1_77, "ymax": 1_15}},
                  ]				,     )







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






