
"""simple docstring"""




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase__				=					logging.get_logger(__name__)

lowerCAmelCase__				=					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__				=					{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase__				=					{
    'facebook/blenderbot_small-90M': 512,
}





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   VOCAB_FILES_NAMES
							UpperCAmelCase__      =   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__      =   BlenderbotSmallTokenizer

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_="<|endoftext|>"							,							SCREAMING_SNAKE_CASE_="<|endoftext|>"							,							SCREAMING_SNAKE_CASE_="<|endoftext|>"							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(
									    ByteLevelBPETokenizer(
									        vocab=SCREAMING_SNAKE_CASE_							,							merges=SCREAMING_SNAKE_CASE_							,							add_prefix_space=SCREAMING_SNAKE_CASE_							,							trim_offsets=SCREAMING_SNAKE_CASE_							,							)							,							bos_token=SCREAMING_SNAKE_CASE_							,							eos_token=SCREAMING_SNAKE_CASE_							,							unk_token=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_							,							)
									SCREAMING_SNAKE_CASE_    			=			add_prefix_space

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[self.sep_token_id]
									SCREAMING_SNAKE_CASE_    			=			[self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep     ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]

"""simple docstring"""




import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')



def 							_lowerCamelCase			(			__a     ):
		return input_array.reshape((input_array.size, 1)     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				# Centralize the data of class i
				SCREAMING_SNAKE_CASE_    			=			data - column_reshape(__a     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += np.dot(__a,				centered_data.T     )
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     )

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.shape[1]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a     ):

		# Check if the features have been loaded
		if features.any():
				SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
				# Center the dataset
				SCREAMING_SNAKE_CASE_    			=			features - np.reshape(__a,				(data_mean.size, 1)     )
				SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     ) / features.shape[1]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.eigh(__a     )
				# Take all the columns in the reverse order (-1), and then takes only the first
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, 0:dimensions]
				# Project the database on the new space
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_eigenvectors.T,				__a     )
				logging.info('''Principal Component Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		assert classes > dimensions

		# Check if features have been already loaded
		if features.any:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			eigh(
				    covariance_between_classes(__a,				__a,				__a     ),				covariance_within_classes(__a,				__a,				__a     ),				)
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, :dimensions]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.svd(__a     )
				SCREAMING_SNAKE_CASE_    			=			svd_matrix[:, 0:dimensions]
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_svd_matrix.T,				__a     )
				logging.info('''Linear Discriminant Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			):
		# Create dummy dataset with 2 classes and 3 features
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
		SCREAMING_SNAKE_CASE_    			=			np.array([0, 0, 0, 1, 1]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			2

		# Assert that the function raises an AssertionError if dimensions > classes
		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			linear_discriminant_analysis(
				    __a,				__a,				__a,				__a     )
				if isinstance(__a,				np.ndarray     ):
						raise AssertionError(
						    '''Did not raise AssertionError for dimensions > classes'''     )
				assert error_info.type is AssertionError



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			principal_component_analysis(__a,				__a     )
				if not np.allclose(__a,				__a     ):
						raise AssertionError
				assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()