





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	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							10




     def        lowerCAmelCase__		(    self							):
         _A     =							[1, 2, 3, 4]
         _A     =							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(_a				,     self.block_size				,     0							)				,     _a							)




     def        lowerCAmelCase__		(    self							):
         _A     =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         _A     =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(_a				,     self.block_size				,     0							)				,     _a							)




     def        lowerCAmelCase__		(    self							):
         _A     =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         _A     =							[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(_a				,     self.block_size				,     0							)				,     _a							)




     def        lowerCAmelCase__		(    self							):
         _A     =							'''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.'''
         _A     =							process_story(_a							)
         self.assertEqual(_a				,     []							)




     def        lowerCAmelCase__		(    self							):
         _A     =							''''''
         _A     =							process_story(_a							)
         self.assertEqual(_a				,     []							)
         self.assertEqual(_a				,     []							)




     def        lowerCAmelCase__		(    self							):
         _A     =							(
             '''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'''
         )
         _A     =							process_story(_a							)

         _A     =							[
             '''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							)

         _A     =							['''It was the best of times.''']
         self.assertEqual(_a				,     _a							)




     def        lowerCAmelCase__		(    self							):
         _A     =							torch.tensor([1, 2, 3, 4]							)
         _A     =							torch.tensor([1, 1, 1, 1]							)
         np.testing.assert_array_equal(build_mask(_a				,     0							).numpy()				,     expected.numpy()							)




     def        lowerCAmelCase__		(    self							):
         _A     =							torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
         _A     =							torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
         np.testing.assert_array_equal(build_mask(_a				,     23							).numpy()				,     expected.numpy()							)




     def        lowerCAmelCase__		(    self							):
         _A     =							torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
         _A     =							torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
         np.testing.assert_array_equal(build_mask(_a				,     1							).numpy()				,     expected.numpy()							)




     def        lowerCAmelCase__		(    self							):
         _A     =							101
         _A     =							torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
         _A     =							torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

         _A     =							compute_token_type_ids(_a				,     _a							)
         np.testing.assert_array_equal(_a				,     _a							)








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



