

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from transformers.modeling_outputs import BaseModelOutput

    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_UpperCAmelCase       : List[str]			     =       logging.get_logger(__name__)

@add_end_docstrings(_snake_case    )
class      lowerCAmelCase							(      _snake_case    ):
 def __init__(   self						:   Any    ,  **UpperCAmelCase						:   Any			)    ->			int:
     super().__init__(**UpperCAmelCase			)

     if self.framework == "tf":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch."""			)

     requires_backends(self    ,  'vision'			)
     self.check_model_type(UpperCAmelCase			)
 def __call__(   self						:   List[str]    ,  UpperCAmelCase						:   Union[str, "Image.Image", List[Dict[str, Any]]]    ,  UpperCAmelCase						:   Union[str, List[str]] = None    ,  **UpperCAmelCase						:   Optional[Any]    ,  )    ->			Any:

     if "text_queries" in kwargs:
         lowerCamelCase__					:   List[Any]      				=	kwargs.pop('text_queries'			)

     if isinstance(UpperCAmelCase    ,  (str, Image.Image)			):
         lowerCamelCase__					:   Optional[int]      				=	{'image': image, 'candidate_labels': candidate_labels}
     else:
         lowerCamelCase__					:   int      				=	image
     lowerCamelCase__					:   int      				=	super().__call__(UpperCAmelCase    ,  **UpperCAmelCase			)
     return results
 def   A_			(   self						:   Any    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     lowerCamelCase__					:   Any      				=	{}
     if "threshold" in kwargs:
         lowerCamelCase__					:   Union[str, Any]      				=	kwargs['threshold']
     if "top_k" in kwargs:
         lowerCamelCase__					:   List[str]      				=	kwargs['top_k']
     return {}, {}, postprocess_params
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   List[Any]			)    ->			List[Any]:
     lowerCamelCase__					:   int      				=	load_image(inputs['image']			)
     lowerCamelCase__					:   List[str]      				=	inputs['candidate_labels']
     if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
         lowerCamelCase__					:   Union[str, Any]      				=	candidate_labels.split(','			)

     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([[image.height, image.width]]    ,  dtype=torch.intaa			)
     for i, candidate_label in enumerate(UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase    ,  return_tensors=self.framework			)
         lowerCamelCase__					:   Union[str, Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=self.framework			)
         yield {
             "is_last": i == len(UpperCAmelCase			) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Union[str, Any]			)    ->			int:
     lowerCamelCase__					:   int      				=	model_inputs.pop('target_size'			)
     lowerCamelCase__					:   int      				=	model_inputs.pop('candidate_label'			)
     lowerCamelCase__					:   Union[str, Any]      				=	model_inputs.pop('is_last'			)

     lowerCamelCase__					:   int      				=	self.model(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
     return model_outputs
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Any=0.1    ,  UpperCAmelCase						:   Optional[int]=None			)    ->			Optional[Any]:
     lowerCamelCase__					:   Dict      				=	[]
     for model_output in model_outputs:
         lowerCamelCase__					:   List[str]      				=	model_output['candidate_label']
         lowerCamelCase__					:   List[Any]      				=	BaseModelOutput(UpperCAmelCase			)
         lowerCamelCase__					:   Tuple      				=	self.image_processor.post_process_object_detection(
             outputs=UpperCAmelCase    ,  threshold=UpperCAmelCase    ,  target_sizes=model_output['target_size']			)[0]

         for index in outputs["scores"].nonzero():
             lowerCamelCase__					:   List[str]      				=	outputs['scores'][index].item()
             lowerCamelCase__					:   Dict      				=	self._get_bounding_box(outputs['boxes'][index][0]			)

             lowerCamelCase__					:   int      				=	{'score': score, 'label': label, 'box': box}
             results.append(UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	sorted(UpperCAmelCase    ,  key=lambda UpperCAmelCase			: x["score"]    ,  reverse=UpperCAmelCase			)
     if top_k:
         lowerCamelCase__					:   int      				=	results[:top_k]

     return results







 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   "torch.Tensor"			)    ->			Optional[int]:

     if self.framework != "pt":
         raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'			)
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	box.int().tolist()
     lowerCamelCase__					:   Tuple      				=	{
         'xmin': xmin,
         'ymin': ymin,
         'xmax': xmax,
         'ymax': ymax,
     }
     return bbox












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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   Any      				=	[]
    for part_id in partition_order:
        lowerCamelCase__					:   List[str]      				=	df.where(F"""SPARK_PARTITION_ID() = {part_id}"""				).collect()
        for row_idx, row in enumerate(_UpperCAmelCase				):
            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  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   List[str]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Any      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   str      				=	Spark(_UpperCAmelCase				)
    # 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  SCREAMING_SNAKE_CASE   ( )    ->  Tuple:
    lowerCamelCase__					:   Any      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Optional[int]      				=	spark.range(10				).repartition(2				)
    lowerCamelCase__					:   Dict      				=	[1, 0]
    lowerCamelCase__					:   str      				=	_generate_iterable_examples(_UpperCAmelCase		,			_UpperCAmelCase				)  # Reverse the partitions.
    lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			_UpperCAmelCase				)

    for i, (row_id, row_dict) in enumerate(generate_fn()				):
        lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Dict      				=	spark.range(10				).repartition(1				)
    lowerCamelCase__					:   List[str]      				=	SparkExamplesIterable(_UpperCAmelCase				)
    assert it.n_shards == 1
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        assert row_id == F"""0_{i}"""
        assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(30				).repartition(3				)
    # Mock the generator so that shuffle reverses the partition indices.
    with patch('numpy.random.Generator'				) as generator_mock:
        lowerCamelCase__					:   int      				=	lambda _UpperCAmelCase				: x.reverse()
        lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[2, 1, 0]				)

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

    # Partitions 0 and 2
    lowerCamelCase__					:   Optional[Any]      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=0		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   List[str]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[0, 2]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        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__					:   Dict      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=1		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   Optional[int]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[1, 3]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    lowerCamelCase__					:   int      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   Dict      				=	Spark(_UpperCAmelCase				)
    # 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





