
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad




class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			10



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			[1, 2, 3, 4]
					lowerCamelCase_						=			[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
					self.assertEqual(truncate_or_pad(lowercase						,  self.block_size						,  0 )						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
					lowerCamelCase_						=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
					self.assertEqual(truncate_or_pad(lowercase						,  self.block_size						,  0 )						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
					lowerCamelCase_						=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
					self.assertEqual(truncate_or_pad(lowercase						,  self.block_size						,  0 )						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			"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."
					lowerCamelCase_	,    lowerCamelCase_						=			process_story(lowercase )
					self.assertEqual(lowercase						,  [] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			""
					lowerCamelCase_	,    lowerCamelCase_						=			process_story(lowercase )
					self.assertEqual(lowercase						,  [] )
					self.assertEqual(lowercase						,  [] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			(
					    "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"
					)
					lowerCamelCase_	,    lowerCamelCase_						=			process_story(lowercase )

					lowerCamelCase_						=			[
					    "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(lowercase						,  lowercase )

					lowerCamelCase_						=			["It was the best of times."]
					self.assertEqual(lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			torch.tensor([1, 2, 3, 4] )
					lowerCamelCase_						=			torch.tensor([1, 1, 1, 1] )
					np.testing.assert_array_equal(build_mask(lowercase						,  0 ).numpy()						,  expected.numpy() )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			torch.tensor([1, 2, 3, 4, 23, 23, 23] )
					lowerCamelCase_						=			torch.tensor([1, 1, 1, 1, 0, 0, 0] )
					np.testing.assert_array_equal(build_mask(lowercase						,  23 ).numpy()						,  expected.numpy() )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			torch.tensor([8, 2, 3, 4, 1, 1, 1] )
					lowerCamelCase_						=			torch.tensor([1, 1, 1, 1, 0, 0, 0] )
					np.testing.assert_array_equal(build_mask(lowercase						,  1 ).numpy()						,  expected.numpy() )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			101
					lowerCamelCase_						=			torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
					lowerCamelCase_						=			torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )

					lowerCamelCase_						=			compute_token_type_ids(lowercase						,  lowercase )
					np.testing.assert_array_equal(lowercase						,  lowercase )






'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





