

import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							10
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							[1, 2, 3, 4]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(a   ,   self.block_size   ,   0						)   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(a   ,   self.block_size   ,   0						)   ,   a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(a   ,   self.block_size   ,   0						)   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							"It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							process_story(a						)
				self.assertEqual(a   ,   []						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							""
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							process_story(a						)
				self.assertEqual(a   ,   []						)
				self.assertEqual(a   ,   []						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							(
				    "It was the year of Our Lord one thousand seven hundred and "
				    "seventy-five\n\nSpiritual revelations were conceded to England "
				    "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
				)
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							process_story(a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							[
				    "It was the year of Our Lord one thousand seven hundred and seventy-five.",
				    "Spiritual revelations were conceded to England at that favoured period, as at this.",
				]
				self.assertEqual(a   ,   a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							["It was the best of times."]
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							torch.tensor([1, 2, 3, 4]						)
				SCREAMING_SNAKE_CASE :   Any					  =							torch.tensor([1, 1, 1, 1]						)
				np.testing.assert_array_equal(build_mask(a   ,   0						).numpy()   ,   expected.numpy()						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.tensor([1, 2, 3, 4, 23, 23, 23]						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
				np.testing.assert_array_equal(build_mask(a   ,   23						).numpy()   ,   expected.numpy()						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							torch.tensor([8, 2, 3, 4, 1, 1, 1]						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
				np.testing.assert_array_equal(build_mask(a   ,   1						).numpy()   ,   expected.numpy()						)






			def 				__UpperCamelCase			(      self				:			Any						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							101
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							compute_token_type_ids(a   ,   a						)
				np.testing.assert_array_equal(a   ,   a						)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['PLBartTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)