




"""simple docstring"""






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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase=0.01  ,      lowercase=1000  ):
							_lowerCamelCase     :			List[str]    =    p_stop
							_lowerCamelCase     :			str    =    max_length






			def __iter__(  self  ):
							_lowerCamelCase     :			int    =    0
							_lowerCamelCase     :			List[Any]    =    False
							while not stop and count < self.max_length:
											yield count
											count += 1
											_lowerCamelCase     :			Dict    =    random.random() < self.p_stop






class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase=False  ,      lowercase=True  ):
							_lowerCamelCase     :			List[Any]    =    [
							    BatchSamplerShard(lowercase  ,      2  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )
							    for i in range(2  )
							]
							_lowerCamelCase     :			List[Any]    =    [list(lowercase  ) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
											self.assertListEqual([len(lowercase  ) for shard in batch_sampler_shards]  ,      [len(lowercase  ) for e in expected]  )
							self.assertListEqual(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [
							    [[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(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Optional[int]    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [
							    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
							    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
							]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							_lowerCamelCase     :			int    =    BatchSampler(range(22  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Any    =    [
							    [[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(lowercase  ,      lowercase  )

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

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(20  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [
							    [[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(lowercase  ,      lowercase  )

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

							# Check the shards when the dataset is very small.
							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [[[0, 1, 0]], [[1, 0, 1]]]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Optional[int]    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of batch size.
							_lowerCamelCase     :			int    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			int    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size.
							_lowerCamelCase     :			Tuple    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Tuple    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			int    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[str]    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							_lowerCamelCase     :			Optional[Any]    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Any    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			Tuple    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Any    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							# Check the shards when the dataset is very small.
							_lowerCamelCase     :			str    =    BatchSampler(range(2  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Any    =    [[[0, 1]], [[0, 1]]]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			str    =    BatchSampler(range(2  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[str]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase     :			Dict    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			str    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							_lowerCamelCase     :			str    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(22  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

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

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							_lowerCamelCase     :			Any    =    BatchSampler(range(20  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

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

							# Check the shards when the dataset is very small.
							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [[[0, 1]], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			int    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of batch size.
							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size.
							_lowerCamelCase     :			int    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Any    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							_lowerCamelCase     :			Any    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Tuple    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

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

							_lowerCamelCase     :			List[str]    =    BatchSampler(range(2  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
							_lowerCamelCase     :			List[str]    =    [BatchSamplerShard(lowercase  ,      2  ,      lowercase  ,      even_batches=lowercase  ) 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 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase=False  ,      lowercase=2  ,      lowercase=False  ):
							random.seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    list(lowercase  )

							_lowerCamelCase     :			Dict    =    [
							    IterableDatasetShard(
							        lowercase  ,      batch_size=lowercase  ,      drop_last=lowercase  ,      num_processes=lowercase  ,      process_index=lowercase  ,      split_batches=lowercase  ,      )
							    for i in range(lowercase  )
							]
							_lowerCamelCase     :			Optional[int]    =    []
							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(lowercase  )
											iterable_dataset_lists.append(list(lowercase  )  )

							_lowerCamelCase     :			int    =    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
							_lowerCamelCase     :			Any    =    iterable_dataset_lists[0]
							for l in iterable_dataset_lists[1:]:
											self.assertEqual(len(lowercase  )  ,      len(lowercase  )  )
											self.assertTrue(len(lowercase  ) % shard_batch_size == 0  )

							_lowerCamelCase     :			List[Any]    =    []
							for idx in range(0  ,      len(lowercase  )  ,      lowercase  ):
											for l in iterable_dataset_lists:
															observed += l[idx : idx + shard_batch_size]

							if not drop_last:
											while len(lowercase  ) < len(lowercase  ):
															reference += reference
							self.assertListEqual(lowercase  ,      reference[: len(lowercase  )]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    42
							_lowerCamelCase     :			Tuple    =    RandomIterableDataset()

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

							# Edge case with a very small dataset
							_lowerCamelCase     :			Optional[Any]    =    RandomIterableDataset(max_length=2  )

							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(16  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    SkipBatchSampler(lowercase  ,      2  )
							self.assertListEqual(list(lowercase  )  ,      [[8, 9, 10, 11], [12, 13, 14, 15]]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    DataLoader(list(range(16  )  )  ,      batch_size=4  )
							_lowerCamelCase     :			Union[str, Any]    =    skip_first_batches(lowercase  ,      num_batches=2  )
							self.assertListEqual([t.tolist() for t in new_dataloader]  ,      [[8, 9, 10, 11], [12, 13, 14, 15]]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    DataLoaderShard(list(range(16  )  )  ,      batch_size=4  )
							for idx, _ in enumerate(lowercase  ):
											self.assertEqual(dataloader.end_of_dataloader  ,      idx == 3  )

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






			def 					A_    (  self  ):
							Accelerator()
							_lowerCamelCase     :			List[str]    =    DataLoaderDispatcher(range(16  )  ,      batch_size=4  )
							for idx, _ in enumerate(lowercase  ):
											self.assertEqual(dataloader.end_of_dataloader  ,      idx == 3  )

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






"""simple docstring"""




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
   import tensorflow as tf

   from ..tf_utils import stable_softmax


if is_torch_available():
   import torch


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

@add_end_docstrings(
    UpperCAmelCase__			,   r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """			,   )
class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if self.framework == "tf":
          A_  :       str										=  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
        elif self.framework == "pt":
          A_  :       List[str]										=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,      as_tuple=snake_case_  )
        else:
          raise ValueError('Unsupported framework'  )
        return masked_index

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  self.get_masked_index(snake_case_  )
        A_  :       str										=  np.prod(masked_index.shape  )
        if numel < 1:
          raise PipelineException(
              'fill-mask'					,      self.model.base_model_prefix					,      F"""No mask_token ({self.tokenizer.mask_token}) found on the input"""					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          for model_input in model_inputs:
            self._ensure_exactly_one_mask_token(model_input['input_ids'][0]  )
        else:
          for input_ids in model_inputs["input_ids"]:
            self._ensure_exactly_one_mask_token(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        if return_tensors is None:
          A_  :       Any										=  self.framework
        A_  :       Dict										=  self.tokenizer(snake_case_					,      return_tensors=snake_case_  )
        self.ensure_exactly_one_mask_token(snake_case_  )
        return model_inputs

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  self.model(**snake_case_  )
        A_  :       Optional[int]										=  model_inputs['input_ids']
        return model_outputs

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=5					,      snake_case_=None  ):

        """simple docstring"""

        if target_ids is not None and target_ids.shape[0] < top_k:
          A_  :       str										=  target_ids.shape[0]
        A_  :       Optional[Any]										=  model_outputs['input_ids'][0]
        A_  :       List[Any]										=  model_outputs['logits']

        if self.framework == "tf":
          A_  :       List[str]										=  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

          A_  :       Union[str, Any]										=  outputs.numpy()

          A_  :       Optional[int]										=  outputs[0, masked_index, :]
          A_  :       Optional[Any]										=  stable_softmax(snake_case_					,      axis=-1  )
          if target_ids is not None:
            A_  :       Union[str, Any]										=  tf.gather_nd(tf.squeeze(snake_case_					,      0  )					,      target_ids.reshape(-1					,      1  )  )
            A_  :       Optional[int]										=  tf.expand_dims(snake_case_					,      0  )

          A_  :       Any										=  tf.math.top_k(snake_case_					,      k=snake_case_  )
          A_						,       A_  :       str										=  topk.values.numpy(), topk.indices.numpy()
        else:
          A_  :       int										=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,      as_tuple=snake_case_  ).squeeze(-1  )
          # Fill mask pipeline supports only one ${mask_token} per sample

          A_  :       Tuple										=  outputs[0, masked_index, :]
          A_  :       List[str]										=  logits.softmax(dim=-1  )
          if target_ids is not None:
            A_  :       str										=  probs[..., target_ids]

          A_						,       A_  :       List[str]										=  probs.topk(snake_case_  )

        A_  :       List[Any]										=  []
        A_  :       int										=  values.shape[0] == 1
        for i, (_values, _predictions) in enumerate(zip(values.tolist()					,      predictions.tolist()  )  ):
          A_  :       str										=  []
          for v, p in zip(_values					,      _predictions  ):
            # Copy is important since we're going to modify this array in place
            A_  :       Union[str, Any]										=  input_ids.numpy().copy()
            if target_ids is not None:
              A_  :       str										=  target_ids[p].tolist()

            A_  :       Union[str, Any]										=  p
            # Filter padding out:
            A_  :       Any										=  tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
            # Originally we skip special tokens to give readable output.
            # For multi masks though, the other [MASK] would be removed otherwise
            # making the output look odd, so we add them back
            A_  :       Any										=  self.tokenizer.decode(snake_case_					,      skip_special_tokens=snake_case_  )
            A_  :       Any										=  {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]  ), 'sequence': sequence}
            row.append(snake_case_  )
          result.append(snake_case_  )
        if single_mask:
          return result[0]
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       List[str]										=  [targets]
        try:
          A_  :       Optional[int]										=  self.tokenizer.get_vocab()
        except Exception:
          A_  :       int										=  {}
        A_  :       Tuple										=  []
        for target in targets:
          A_  :       int										=  vocab.get(snake_case_					,      snake_case_  )
          if id_ is None:
            A_  :       Tuple										=  self.tokenizer(
                snake_case_					,      add_special_tokens=snake_case_					,      return_attention_mask=snake_case_					,      return_token_type_ids=snake_case_					,      max_length=1					,      truncation=snake_case_					,      )['input_ids']
            if len(snake_case_  ) == 0:
              logger.warning(
                  F"""The specified target token `{target}` does not exist in the model vocabulary. """
                  'We cannot replace it with anything meaningful, ignoring it'  )
              continue
            A_  :       str										=  input_ids[0]
            # XXX: If users encounter this pass
            # it becomes pretty slow, so let's make sure
            # The warning enables them to fix the input to
            # get faster performance.
            logger.warning(
                F"""The specified target token `{target}` does not exist in the model vocabulary. """
                F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`."""  )
          target_ids.append(id_  )
        A_  :       Tuple										=  list(set(snake_case_  )  )
        if len(snake_case_  ) == 0:
          raise ValueError('At least one target must be provided when passed.'  )
        A_  :       Optional[Any]										=  np.array(snake_case_  )
        return target_ids

      def 					lowerCamelCase_			(     self					,      snake_case_=None					,      snake_case_=None  ):

        """simple docstring"""

        A_  :       List[str]										=  {}

        if targets is not None:
          A_  :       Any										=  self.get_target_ids(snake_case_					,      snake_case_  )
          A_  :       Optional[Any]										=  target_ids

        if top_k is not None:
          A_  :       int										=  top_k

        if self.tokenizer.mask_token_id is None:
          raise PipelineException(
              'fill-mask'					,      self.model.base_model_prefix					,      'The tokenizer does not define a `mask_token`.'  )
        return {}, {}, postprocess_params

      def __call__(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  super().__call__(snake_case_					,      **snake_case_  )
        if isinstance(snake_case_					,      snake_case_  ) and len(snake_case_  ) == 1:
          return outputs[0]
        return outputs