
"""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      __magic_name__     (     *_a      ,       **_a							):
             pass

@is_pipeline_test
@require_vision
@require_torch
class 						a__		( unittest.TestCase		):
   __lowerCAmelCase				=							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							int									=				pipeline(
              "zero-shot-object-detection"      ,       model="hf-internal-testing/tiny-random-owlvit-object-detection"							)

          lowercase       :							Any									=				[
              {
                  "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                  "candidate_labels": ["cat", "remote", "couch"],
              }
          ]
          return object_detector, examples
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							str									=				object_detector(examples[0]      ,       threshold=0.0							)

          lowercase       :							Tuple									=				len(_a							)
          self.assertGreater(_a      ,       0							)
          self.assertEqual(
              _a      ,       [
                  {
                      "score": ANY(_a							),
                      "label": ANY(_a							),
                      "box": {"xmin": ANY(_a							), "ymin": ANY(_a							), "xmax": ANY(_a							), "ymax": ANY(_a							)},
                  }
                  for i in range(_a							)
              ]      ,       )
   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"							)
   def      __magic_name__     (     self							):
          pass
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				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=0.6_4      ,       )

          self.assertEqual(
              nested_simplify(_a      ,       decimals=4							)      ,       [
                  {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                  {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                  {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                  {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                  {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                  {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                  {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                  {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                  {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
              ]      ,       )

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

          self.assertEqual(
              nested_simplify(_a      ,       decimals=4							)      ,       [
                  [
                      {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                      {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                      {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                  ]
              ]      ,       )
   @require_torch
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				pipeline("zero-shot-object-detection"							)

          lowercase       :							int									=				object_detector(
              "http://images.cocodataset.org/val2017/000000039769.jpg"      ,       candidate_labels=["cat", "remote", "couch"]      ,       )
          self.assertEqual(
              nested_simplify(_a      ,       decimals=4							)      ,       [
                  {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                  {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                  {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                  {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                  {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
              ]      ,       )

          lowercase       :							Tuple									=				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(_a      ,       decimals=4							)      ,       [
                  [
                      {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                      {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                      {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                      {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                      {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                  ],
                  [
                      {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                      {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                      {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                      {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                      {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                  ],
              ]      ,       )
   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"							)
   def      __magic_name__     (     self							):
          pass
   @require_torch
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				0.2
          lowercase       :							int									=				pipeline("zero-shot-object-detection"							)

          lowercase       :							List[Any]									=				object_detector(
              "http://images.cocodataset.org/val2017/000000039769.jpg"      ,       candidate_labels=["cat", "remote", "couch"]      ,       threshold=_a      ,       )
          self.assertEqual(
              nested_simplify(_a      ,       decimals=4							)      ,       [
                  {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                  {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                  {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
              ]      ,       )

   @require_torch
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							int									=				2
          lowercase       :							List[Any]									=				pipeline("zero-shot-object-detection"							)

          lowercase       :							Dict									=				object_detector(
              "http://images.cocodataset.org/val2017/000000039769.jpg"      ,       candidate_labels=["cat", "remote", "couch"]      ,       top_k=_a      ,       )
          self.assertEqual(
              nested_simplify(_a      ,       decimals=4							)      ,       [
                  {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                  {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
              ]      ,       )




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Optional[Any]									=				max_position_embeddings
          lowercase       :							str									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							List[Any]									=				intermediate_size
          lowercase       :							int									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Tuple									=				initializer_range
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				use_cache

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


