






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)

class 		_snake_case  (  A__					):






			def __init__(       self		,					a=0.01		,					a=1000)      ->       Any:
						SCREAMING_SNAKE_CASE						= p_stop
						SCREAMING_SNAKE_CASE						= max_length







			def __iter__(       self)      ->       int:
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= False
						while not stop and count < self.max_length:
									yield count
									count += 1
									SCREAMING_SNAKE_CASE						= random.random() < self.p_stop



class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=False		,					a=True)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= [
						    BatchSamplerShard(a		,					2		,					a		,					split_batches=a		,					even_batches=a)
						    for i in range(2)
						]
						SCREAMING_SNAKE_CASE						= [list(a) for batch_sampler_shard in batch_sampler_shards]
						if not split_batches:
									self.assertListEqual([len(a) for shard in batch_sampler_shards]		,					[len(a) for e in expected])
						self.assertListEqual(a		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						# Check the shards when the dataset is a round multiple of total batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
						]
						self.check_batch_sampler_shards(a		,					a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=3		,					drop_last=a)
						# Expected shouldn't change
						self.check_batch_sampler_shards(a		,					a)

						# Check the shards when the dataset is a round multiple of batch size but not total batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
						]
						self.check_batch_sampler_shards(a		,					a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a)

						# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
						# num_processes batch.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
						]
						self.check_batch_sampler_shards(a		,					a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a)

						# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
						# num_processes batch.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(20)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
						]
						self.check_batch_sampler_shards(a		,					a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(20)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[[0, 1, 0]], [[1, 0, 1]]]
						self.check_batch_sampler_shards(a		,					a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[], []]
						self.check_batch_sampler_shards(a		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Check the shards when the dataset is a round multiple of batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=4		,					drop_last=a)
						# Expected shouldn't change
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size or num_processes.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[[0, 1]], [[0, 1]]]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[], []]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						# Check the shards when the dataset is a round multiple of total batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=3		,					drop_last=a)
						# Expected shouldn't change
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						# Check the shards when the dataset is a round multiple of batch size but not total batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
						# num_processes batch.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
						# num_processes batch.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(20)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(20)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
						]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[[0, 1]], []]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=3		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[], []]
						self.check_batch_sampler_shards(a		,					a		,					even_batches=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Check the shards when the dataset is a round multiple of batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(24)		,					batch_size=4		,					drop_last=a)
						# Expected shouldn't change
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(22)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						# Check the shards when the dataset is not a round multiple of batch size or num_processes.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(21)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [
						    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
						    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
						]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[[0, 1]], []]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)

						SCREAMING_SNAKE_CASE						= BatchSampler(range(2)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= [[], []]
						self.check_batch_sampler_shards(a		,					a		,					split_batches=a		,					even_batches=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
						SCREAMING_SNAKE_CASE						= [BatchSamplerShard(a		,					2		,					a		,					even_batches=a) for i in range(2)]

						self.assertEqual(len(batch_sampler_shards[0])		,					3)
						self.assertEqual(len(batch_sampler_shards[1])		,					2)

						self.assertListEqual(list(batch_sampler_shards[0])		,					[[0, 1, 2], [5, 6, 7, 8], [12, 13]])
						self.assertListEqual(list(batch_sampler_shards[1])		,					[[3, 4], [9, 10, 11]])






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a=False		,					a=2		,					a=False)      ->       Dict:
						random.seed(a)
						SCREAMING_SNAKE_CASE						= list(a)

						SCREAMING_SNAKE_CASE						= [
						    IterableDatasetShard(
						        a		,					batch_size=a		,					drop_last=a		,					num_processes=a		,					process_index=a		,					split_batches=a		,					)
						    for i in range(a)
						]
						SCREAMING_SNAKE_CASE						= []
						for iterable_dataset_shard in iterable_dataset_shards:
									# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
									random.seed(a)
									iterable_dataset_lists.append(list(a))

						SCREAMING_SNAKE_CASE						= batch_size // num_processes if split_batches else batch_size
						# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
						SCREAMING_SNAKE_CASE						= iterable_dataset_lists[0]
						for l in iterable_dataset_lists[1:]:
									self.assertEqual(len(a)		,					len(a))
									self.assertTrue(len(a) % shard_batch_size == 0)

						SCREAMING_SNAKE_CASE						= []
						for idx in range(0		,					len(a)		,					a):
									for l in iterable_dataset_lists:
												observed += l[idx : idx + shard_batch_size]

						if not drop_last:
									while len(a) < len(a):
												reference += reference
						self.assertListEqual(a		,					reference[: len(a)])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= 42
						SCREAMING_SNAKE_CASE						= RandomIterableDataset()

						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)

						# Edge case with a very small dataset
						SCREAMING_SNAKE_CASE						= RandomIterableDataset(max_length=2)

						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)
						self.check_iterable_dataset_shards(a		,					a		,					batch_size=4		,					drop_last=a		,					split_batches=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= BatchSampler(range(16)		,					batch_size=4		,					drop_last=a)
						SCREAMING_SNAKE_CASE						= SkipBatchSampler(a		,					2)
						self.assertListEqual(list(a)		,					[[8, 9, 10, 11], [12, 13, 14, 15]])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= SkipDataLoader(list(range(16))		,					batch_size=4		,					skip_batches=2)
						self.assertListEqual([t.tolist() for t in dataloader]		,					[[8, 9, 10, 11], [12, 13, 14, 15]])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= DataLoader(list(range(16))		,					batch_size=4)
						SCREAMING_SNAKE_CASE						= skip_first_batches(a		,					num_batches=2)
						self.assertListEqual([t.tolist() for t in new_dataloader]		,					[[8, 9, 10, 11], [12, 13, 14, 15]])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= DataLoaderShard(list(range(16))		,					batch_size=4)
						for idx, _ in enumerate(a):
									self.assertEqual(dataloader.end_of_dataloader		,					idx == 3)

						# Test it also works on the second iteration
						for idx, _ in enumerate(a):
									self.assertEqual(dataloader.end_of_dataloader		,					idx == 3)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						Accelerator()
						SCREAMING_SNAKE_CASE						= DataLoaderDispatcher(range(16)		,					batch_size=4)
						for idx, _ in enumerate(a):
									self.assertEqual(dataloader.end_of_dataloader		,					idx == 3)

						# Test it also works on the second iteration
						for idx, _ in enumerate(a):
									self.assertEqual(dataloader.end_of_dataloader		,					idx == 3)












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, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

a_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

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

						requires_backends(self		,					'vision')
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= model_outputs['target_size']
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									SCREAMING_SNAKE_CASE						= [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [dict(zip(a		,					a)) for vals in zip(scores.tolist()		,					a		,					a) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									SCREAMING_SNAKE_CASE						= self.image_processor.post_process_object_detection(a		,					a		,					a)
									SCREAMING_SNAKE_CASE						= raw_annotations[0]
									SCREAMING_SNAKE_CASE						= raw_annotation['scores']
									SCREAMING_SNAKE_CASE						= raw_annotation['labels']
									SCREAMING_SNAKE_CASE						= raw_annotation['boxes']

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




