



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Optional[Any]       = {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class      _lowerCAmelCase							(      lowercase__			):
	__SCREAMING_SNAKE_CASE       :		Any		      = """sew-d"""


	def __init__(self	,				lowercase=32	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase=2	,				lowercase=512	,				lowercase=256	,				lowercase=True	,				lowercase=True	,				lowercase=("p2c", "c2p")	,				lowercase="layer_norm"	,				lowercase="gelu_python"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=0.0	,				lowercase=0.1	,				lowercase=0.02	,				lowercase=1E-7	,				lowercase=1E-5	,				lowercase="group"	,				lowercase="gelu"	,				lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,				lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,				lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,				lowercase=False	,				lowercase=128	,				lowercase=16	,				lowercase=True	,				lowercase=0.05	,				lowercase=10	,				lowercase=2	,				lowercase=0.0	,				lowercase=10	,				lowercase=0	,				lowercase="mean"	,				lowercase=False	,				lowercase=False	,				lowercase=256	,				lowercase=0	,				lowercase=1	,				lowercase=2	,				**lowercase	,				):
				super().__init__(**lowercase_	,				pad_token_id=lowercase_	,				bos_token_id=lowercase_	,				eos_token_id=lowercase_							)
				A_     :						Tuple     =					hidden_size
				A_     :						Dict     =					feat_extract_norm
				A_     :						Tuple     =					feat_extract_activation
				A_     :						str     =					list(lowercase_							)
				A_     :						Tuple     =					list(lowercase_							)
				A_     :						List[str]     =					list(lowercase_							)
				A_     :						Dict     =					conv_bias
				A_     :						int     =					num_conv_pos_embeddings
				A_     :						str     =					num_conv_pos_embedding_groups
				A_     :						int     =					len(self.conv_dim							)
				A_     :						List[str]     =					num_hidden_layers
				A_     :						Optional[int]     =					intermediate_size
				A_     :						Tuple     =					squeeze_factor
				A_     :						Tuple     =					max_position_embeddings
				A_     :						Tuple     =					position_buckets
				A_     :						Optional[int]     =					share_att_key
				A_     :						Dict     =					relative_attention
				A_     :						List[Any]     =					norm_rel_ebd
				A_     :						Optional[int]     =					list(lowercase_							)
				A_     :						List[str]     =					hidden_act
				A_     :						Union[str, Any]     =					num_attention_heads
				A_     :						List[str]     =					hidden_dropout
				A_     :						Optional[int]     =					attention_dropout
				A_     :						Optional[Any]     =					activation_dropout
				A_     :						Tuple     =					feat_proj_dropout
				A_     :						Optional[int]     =					final_dropout
				A_     :						Optional[int]     =					layer_norm_eps
				A_     :						Union[str, Any]     =					feature_layer_norm_eps
				A_     :						Optional[Any]     =					initializer_range
				A_     :						int     =					vocab_size

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
							raise ValueError(
							    """Configuration for convolutional layers is incorrect."""
							    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
							    F'but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'
							    F'= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				A_     :						Optional[int]     =					apply_spec_augment
				A_     :						Dict     =					mask_time_prob
				A_     :						Optional[Any]     =					mask_time_length
				A_     :						List[str]     =					mask_time_min_masks
				A_     :						Dict     =					mask_feature_prob
				A_     :						int     =					mask_feature_length
				A_     :						Any     =					mask_feature_min_masks

				# ctc loss
				A_     :						Tuple     =					ctc_loss_reduction
				A_     :						Tuple     =					ctc_zero_infinity

				# sequence classification
				A_     :						Any     =					use_weighted_layer_sum
				A_     :						Union[str, Any]     =					classifier_proj_size


	@property
	def    _a     (self							):
				return functools.reduce(operator.mul	,				self.conv_stride	,				1							)




'''simple docstring'''




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							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


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


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


	def    _a     (self							):
				A_     :						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."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						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"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """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							)

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


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)