






'''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 	SCREAMING_SNAKE_CASE	:




          @staticmethod
          def 					a_					(					*A__      : Any	,    **A__      : int					):


               """simple docstring"""







               pass







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




       def 					a_					(					self      : Optional[Any]	,    A__      : Union[str, Any]	,    A__      : Any	,    A__      : Optional[Any]					):


            """simple docstring"""







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

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




       def 					a_					(					self      : Any	,    A__      : Tuple	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				object_detector(examples[0]	,    threshold=0.0					)

            __lowerCamelCase						:       Any				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







            pass




       @require_torch
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				pipeline(
                """zero-shot-object-detection"""	,    model="""hf-internal-testing/tiny-random-owlvit-object-detection"""					)

            __lowerCamelCase						:       Dict				   =				object_detector(
                """./tests/fixtures/tests_samples/COCO/000000039769.png"""	,    candidate_labels=["""cat""", """remote""", """couch"""]	,    threshold=0.64	,    )

            self.assertEqual(
                nested_simplify(A__	,    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}},
                ]	,    )

            __lowerCamelCase						:       List[str]				   =				object_detector(
                [
                    {
                        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                        """candidate_labels""": ["""cat""", """remote""", """couch"""],
                    }
                ]	,    threshold=0.64	,    )

            self.assertEqual(
                nested_simplify(A__	,    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 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				pipeline("""zero-shot-object-detection"""					)

            __lowerCamelCase						:       Any				   =				object_detector(
                """http://images.cocodataset.org/val2017/000000039769.jpg"""	,    candidate_labels=["""cat""", """remote""", """couch"""]	,    )
            self.assertEqual(
                nested_simplify(A__	,    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}},
                ]	,    )

            __lowerCamelCase						:       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""": 0.2868, """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 					a_					(					self      : Tuple					):


            """simple docstring"""







            pass




       @require_torch
       @slow
       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				0.2
            __lowerCamelCase						:       Any				   =				pipeline("""zero-shot-object-detection"""					)

            __lowerCamelCase						:       Optional[int]				   =				object_detector(
                """http://images.cocodataset.org/val2017/000000039769.jpg"""	,    candidate_labels=["""cat""", """remote""", """couch"""]	,    threshold=A__	,    )
            self.assertEqual(
                nested_simplify(A__	,    decimals=4					)	,    [
                    {"""score""": 0.2868, """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 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				2
            __lowerCamelCase						:       Dict				   =				pipeline("""zero-shot-object-detection"""					)

            __lowerCamelCase						:       Any				   =				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""": 0.2868, """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}},
                ]	,    )











'''simple docstring'''

from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				[]
     for part_id in partition_order:
          __lowerCamelCase						:       Union[str, Any]				   =				df.where(f"SPARK_PARTITION_ID() = {part_id}"     ).collect()
          for row_idx, row in enumerate(_lowercase     ):
               expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict())     )
     return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Optional[int]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       str				   =				Spark(_lowercase     )
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16     )
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(10     ).repartition(2     )
     __lowerCamelCase						:       List[Any]				   =				[1, 0]
     __lowerCamelCase						:       Any				   =				_generate_iterable_examples(_lowercase,						_lowercase     )  # Reverse the partitions.
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						_lowercase     )

     for i, (row_id, row_dict) in enumerate(generate_fn()     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Union[str, Any]				   =				spark.range(10     ).repartition(1     )
     __lowerCamelCase						:       Dict				   =				SparkExamplesIterable(_lowercase     )
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          assert row_id == f"0_{i}"
          assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Optional[Any]				   =				spark.range(30     ).repartition(3     )
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""     ) as generator_mock:
          __lowerCamelCase						:       Dict				   =				lambda _lowercase     : x.reverse()
          __lowerCamelCase						:       Union[str, Any]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[2, 1, 0]     )

          __lowerCamelCase						:       Tuple				   =				SparkExamplesIterable(_lowercase     ).shuffle_data_sources(_lowercase     )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase     ):
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(20     ).repartition(4     )

     # Partitions 0 and 2
     __lowerCamelCase						:       Union[str, Any]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=0,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       Optional[int]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[0, 2]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __lowerCamelCase						:       List[str]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=1,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[1, 3]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       Any				   =				Spark(_lowercase     )
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1     )
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 100



