'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Optional[torch.FloatTensor]      =       None
			UpperCAmelCase_			:     torch.FloatTensor      =       None
			UpperCAmelCase_			:     Optional[Tuple[torch.FloatTensor]]      =       None
			UpperCAmelCase_			:     Optional[Tuple[torch.FloatTensor]]      =       None







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=512       ,    __lowerCAmelCase="cls"       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=True       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase       ,    bos_token_id=__lowerCAmelCase       ,    eos_token_id=__lowerCAmelCase       ,    **__lowerCAmelCase)
					lowerCAmelCase   					=  project_dim
					lowerCAmelCase   					=  pooler_fn
					lowerCAmelCase   					=  learn_encoder
					lowerCAmelCase   					=  use_attention_mask







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       [r'''pooler''', r'''logit_scale''']
			UpperCAmelCase_			:     Optional[int]      =       [r'''position_ids''', r'''predictions.decoder.bias''']
			UpperCAmelCase_			:     Optional[Any]      =       '''roberta'''
			UpperCAmelCase_			:     Tuple      =       RobertaSeriesConfig



			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase)
					lowerCAmelCase   					=  XLMRobertaModel(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Linear(config.hidden_size       ,    config.project_dim)
					lowerCAmelCase   					=  getattr(__lowerCAmelCase       ,    """has_pre_transformation"""       ,    __lowerCAmelCase)
					if self.has_pre_transformation:
							lowerCAmelCase   					=  nn.Linear(config.hidden_size       ,    config.project_dim)
							lowerCAmelCase   					=  nn.LayerNorm(config.hidden_size       ,    eps=config.layer_norm_eps)
					self.post_init()







			def 							a_       (   self       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  return_dict if return_dict is not None else self.config.use_return_dict

					lowerCAmelCase   					=  self.base_model(
					    input_ids=__lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase       ,    position_ids=__lowerCAmelCase       ,    head_mask=__lowerCAmelCase       ,    inputs_embeds=__lowerCAmelCase       ,    encoder_hidden_states=__lowerCAmelCase       ,    encoder_attention_mask=__lowerCAmelCase       ,    output_attentions=__lowerCAmelCase       ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,    return_dict=__lowerCAmelCase       ,    )

					if self.has_pre_transformation:
							lowerCAmelCase   					=  outputs["""hidden_states"""][-2]
							lowerCAmelCase   					=  self.pre_LN(__lowerCAmelCase)
							lowerCAmelCase   					=  self.transformation_pre(__lowerCAmelCase)

							return TransformationModelOutput(
							    projection_state=__lowerCAmelCase       ,    last_hidden_state=outputs.last_hidden_state       ,    hidden_states=outputs.hidden_states       ,    attentions=outputs.attentions       ,    )
					else:
							lowerCAmelCase   					=  self.transformation(outputs.last_hidden_state)
							return TransformationModelOutput(
							    projection_state=__lowerCAmelCase       ,    last_hidden_state=outputs.last_hidden_state       ,    hidden_states=outputs.hidden_states       ,    attentions=outputs.attentions       ,    )





'''simple docstring'''




def 				snake_case__							(			_A:					str )    ->	list[int]:

		'''simple docstring'''


		lowerCAmelCase   					=  [0 for i in range(len(_A ) )]

		# initialize interval's left pointer and right pointer
		lowerCAmelCase					,     lowerCAmelCase   					=  0, 0

		for i in range(1      ,   len(_A ) ):
				# case when current index is inside the interval
				if i <= right_pointer:
						lowerCAmelCase   					=  min(right_pointer - i + 1      ,   z_result[i - left_pointer] )
						lowerCAmelCase   					=  min_edge

				while go_next(_A      ,   _A      ,   _A ):
						z_result[i] += 1

				# if new index's result gives us more right interval,
				# we've to update left_pointer and right_pointer
				if i + z_result[i] - 1 > right_pointer:
						lowerCAmelCase					,     lowerCAmelCase   					=  i, i + z_result[i] - 1

		return z_result






def 				snake_case__							(			_A:					int      ,   _A:					list[int]      ,   _A:					str )    ->	bool:

		'''simple docstring'''


		return i + z_result[i] < len(_A ) and s[z_result[i]] == s[i + z_result[i]]






def 				snake_case__							(			_A:					str      ,   _A:					str )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  0
		# concatenate 'pattern' and 'input_str' and call z_function
		# with concatenated string
		lowerCAmelCase   					=  z_function(pattern + input_str )

		for val in z_result:
				# if value is greater then length of the pattern string
				# that means this index is starting position of substring
				# which is equal to pattern string
				if val >= len(_A ):
						answer += 1

		return answer


if __name__ == "__main__":
						import doctest

						doctest.testmod()




