





import logging

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

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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    np.ndarray   )   ->			np.ndarray:
				return input_array.reshape((input_array.size, 1)   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    int   )   ->			np.ndarray:
				UpperCamelCase__							:       List[Any]      =   np.nan
				for i in range(__UpperCAmelCase   ):
								UpperCamelCase__							:       Union[str, Any]      =   features[:, labels == i]
								UpperCamelCase__							:       Any      =   data.mean(1   )
								# Centralize the data of class i
								UpperCamelCase__							:       Any      =   data - column_reshape(__UpperCAmelCase   )
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(__UpperCAmelCase	,     centered_data.T   )
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												UpperCamelCase__							:       Union[str, Any]      =   np.dot(__UpperCAmelCase	,     centered_data.T   )

				return covariance_sum / features.shape[1]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    int   )   ->			np.ndarray:
				UpperCamelCase__							:       List[str]      =   features.mean(1   )
				UpperCamelCase__							:       Union[str, Any]      =   np.nan
				for i in range(__UpperCAmelCase   ):
								UpperCamelCase__							:       Dict      =   features[:, labels == i]
								UpperCamelCase__							:       int      =   data.shape[1]
								UpperCamelCase__							:       Optional[Any]      =   data.mean(1   )
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += device_data * np.dot(
												    column_reshape(__UpperCAmelCase   ) - column_reshape(__UpperCAmelCase   )	,     (column_reshape(__UpperCAmelCase   ) - column_reshape(__UpperCAmelCase   )).T	,     )
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												UpperCamelCase__							:       Dict      =   device_data * np.dot(
												    column_reshape(__UpperCAmelCase   ) - column_reshape(__UpperCAmelCase   )	,     (column_reshape(__UpperCAmelCase   ) - column_reshape(__UpperCAmelCase   )).T	,     )

				return covariance_sum / features.shape[1]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    int   )   ->			np.ndarray:

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

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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    np.ndarray	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    int   )   ->			np.ndarray:
				assert classes > dimensions

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

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




def 	lowerCAmelCase_					(						)   ->			None:
				# Create dummy dataset with 2 classes and 3 features
				UpperCamelCase__							:       Optional[Any]      =   np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]   )
				UpperCamelCase__							:       Tuple      =   np.array([0, 0, 0, 1, 1]   )
				UpperCamelCase__							:       Optional[Any]      =   2
				UpperCamelCase__							:       Union[str, Any]      =   2

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




def 	lowerCAmelCase_					(						)   ->			None:
				UpperCamelCase__							:       List[Any]      =   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
				UpperCamelCase__							:       Dict      =   2
				UpperCamelCase__							:       Union[str, Any]      =   np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]   )

				with pytest.raises(__UpperCAmelCase   ) as error_info:
								UpperCamelCase__							:       List[Any]      =   principal_component_analysis(__UpperCAmelCase	,     __UpperCAmelCase   )
								if not np.allclose(__UpperCAmelCase	,     __UpperCAmelCase   ):
												raise AssertionError
								assert error_info.type is AssertionError


if __name__ == "__main__":
		import doctest

		doctest.testmod()








import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_  							=				{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_  							=				{
    'Salesforce/codegen-350M-mono': 2048,
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	VOCAB_FILES_NAMES
   a		:							List[str]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Any     	=	CodeGenTokenizer




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__=False,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       super().__init__(
           __magic_name__,	__magic_name__,	tokenizer_file=__magic_name__,	unk_token=__magic_name__,	bos_token=__magic_name__,	eos_token=__magic_name__,	add_prefix_space=__magic_name__,	**__magic_name__,	)

       if kwargs.pop('''add_bos_token''',	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   kwargs.pop('''name_or_path''',	''''''							)
           raise ValueError(
               '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
               '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
               f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
               f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
               '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
               ''' so that the fast tokenizer works correctly.'''							)

       UpperCamelCase__							:       Dict      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[str]      =   pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   add_prefix_space




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   kwargs.get('''is_split_into_words''',	__magic_name__							)
       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	__magic_name__							)

       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self._tokenizer.model.save(__magic_name__,	name=__magic_name__							)
       return tuple(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   super().decode(
           token_ids=__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__,	**__magic_name__,	)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:       str      =   self.truncate(__magic_name__,	__magic_name__							)

       return decoded_text




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       def find_re(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Dict      =   pattern.search(__magic_name__,	__magic_name__							)
           return m.start() if m else -1

       UpperCamelCase__							:       Union[str, Any]      =   [re.compile(__magic_name__,	re.MULTILINE							) for pattern in truncate_before_pattern]

       UpperCamelCase__							:       List[Any]      =   list(re.finditer('''^print''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Tuple      =   completion[: prints[1].start()]

       UpperCamelCase__							:       Optional[int]      =   list(re.finditer('''^def''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Optional[Any]      =   completion[: defs[1].start()]

       UpperCamelCase__							:       str      =   0

       UpperCamelCase__							:       Tuple      =   [
           pos for pos in [find_re(__magic_name__,	__magic_name__,	__magic_name__							) for terminal in terminals] if pos != -1
       ]

       if len(__magic_name__							) > 0:
           return completion[: min(__magic_name__							)]
       else:
           return completion

