






'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class      A     :






 """simple docstring"""





 __a      :    Dict			  =    field(
     metadata={'''help''': '''The output directory where the model will be written.'''}, )
 __a      :    List[Any]			  =    field(
     metadata={
         '''help''': (
             '''The encoder model checkpoint for weights initialization.'''
             '''Don\'t set if you want to train an encoder model from scratch.'''
         )
     }, )
 __a      :    Union[str, Any]			  =    field(
     metadata={
         '''help''': (
             '''The decoder model checkpoint for weights initialization.'''
             '''Don\'t set if you want to train a decoder model from scratch.'''
         )
     }, )
 __a      :    Optional[int]			  =    field(
     default=__lowercase, metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}    )
 __a      :    int			  =    field(
     default=__lowercase, metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}    )






def 				snake_case       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Any            =				HfArgumentParser((ModelArguments,)						)
      ((UpperCamelCase_)     ,      )					: List[Any]            =				parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

      # Use explicit specified encoder config
      if model_args.encoder_config_name:
            UpperCamelCase_					: Union[str, Any]            =				AutoConfig.from_pretrained(model_args.encoder_config_name						)
      # Use pretrained encoder model's config
      else:
            UpperCamelCase_					: str            =				AutoConfig.from_pretrained(model_args.encoder_model_name_or_path						)

      # Use explicit specified decoder config
      if model_args.decoder_config_name:
            UpperCamelCase_					: List[Any]            =				AutoConfig.from_pretrained(model_args.decoder_config_name						)
      # Use pretrained decoder model's config
      else:
            UpperCamelCase_					: str            =				AutoConfig.from_pretrained(model_args.decoder_model_name_or_path						)

      # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
      UpperCamelCase_					: Optional[Any]            =				True
      UpperCamelCase_					: Any            =				True

      UpperCamelCase_					: List[Any]            =				FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
          encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path     , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path     , encoder_config=__a     , decoder_config=__a     , )

      # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
      UpperCamelCase_					: Optional[int]            =				decoder_config.decoder_start_token_id
      UpperCamelCase_					: Tuple            =				decoder_config.pad_token_id
      if decoder_start_token_id is None:
            UpperCamelCase_					: Optional[int]            =				decoder_config.bos_token_id
      if pad_token_id is None:
            UpperCamelCase_					: int            =				decoder_config.eos_token_id

      # This is necessary to make Flax's generate() work
      UpperCamelCase_					: int            =				decoder_config.eos_token_id
      UpperCamelCase_					: List[Any]            =				decoder_start_token_id
      UpperCamelCase_					: str            =				pad_token_id

      UpperCamelCase_					: Union[str, Any]            =				AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

      UpperCamelCase_					: List[Any]            =				AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
      UpperCamelCase_					: Optional[Any]            =				tokenizer.convert_ids_to_tokens(model.config.pad_token_id						)

      model.save_pretrained(model_args.output_dir						)
      image_processor.save_pretrained(model_args.output_dir						)
      tokenizer.save_pretrained(model_args.output_dir						)


if __name__ == "__main__":
      main()








"""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()