
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''M-CLIP'''







				def __init__(						self       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=7_6_8       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      transformerDimSize
								__magic_name__		:Any        =      imageDimSize
								super().__init__(**__lowerCAmelCase     )







class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  MCLIPConfig







				def __init__(						self       ,					__lowerCAmelCase       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(__lowerCAmelCase       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     )
								__magic_name__		:Dict        =      XLMRobertaModel(__lowerCAmelCase     )
								__magic_name__		:Dict        =      torch.nn.Linear(
								    in_features=config.transformerDimensions       ,					out_features=config.numDims     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.transformer(input_ids=__lowerCAmelCase       ,					attention_mask=__lowerCAmelCase     )[0]
								__magic_name__		:Optional[int]        =      (embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
								return self.LinearTransformation(__lowerCAmelCase     ), embs














import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4]
								__magic_name__		:Union[str, Any]        =      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								__magic_name__		:Optional[Any]        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
								__magic_name__		:str        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      '''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.'''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      ''''''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		: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'''
								)
								__magic_name__     ,				__magic_name__		:str        =      process_story(__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      [
								    '''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(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      ['''It was the best of times.''']
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([1, 2, 3, 4]     )
								__magic_name__		:List[str]        =      torch.tensor([1, 1, 1, 1]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					0     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]     )
								__magic_name__		:Union[str, Any]        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					2_3     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([8, 2, 3, 4, 1, 1, 1]     )
								__magic_name__		:Dict        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					1     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0_1
								__magic_name__		:List[Any]        =      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]     )
								__magic_name__		:str        =      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]     )

								__magic_name__		:Tuple        =      compute_token_type_ids(__lowerCAmelCase       ,					__lowerCAmelCase     )
								np.testing.assert_array_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )






