'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 			UpperCAmelCase  :





       '''simple docstring'''



       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        return None



class 			UpperCAmelCase  :





       '''simple docstring'''



       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:
        return None



class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    [
           # (model_name, model_kwargs)
           ("bert-base-cased", {}),
           ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
       ]

       @require_tf
       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCAmelCase	,   '''tf'''	,   12	,   **__lowerCAmelCase )

       @require_torch
       @slow
       def _lowerCAmelCase(						self )  ->  List[str]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCAmelCase	,   '''pt'''	,   12	,   **__lowerCAmelCase )

       @require_torch
       @slow
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        from transformers import BertModel

        lowercase__				:    int  =					['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
        with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
         vocab_file.write('''\n'''.join(__lowerCAmelCase ) )
         vocab_file.flush()
         lowercase__				:    Union[str, Any]  =					BertTokenizerFast(vocab_file.name )

        with TemporaryDirectory() as bert_save_dir:
         lowercase__				:    List[str]  =					BertModel(BertConfig(vocab_size=len(__lowerCAmelCase ) ) )
         model.save_pretrained(__lowerCAmelCase )
         self._test_export(__lowerCAmelCase	,   '''pt'''	,   12	,   __lowerCAmelCase )

       @require_tf
       @slow
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowercase__				:    List[Any]  =					self._test_export(__lowerCAmelCase	,   '''tf'''	,   12	,   **__lowerCAmelCase )
         lowercase__				:    Optional[Any]  =					quantize(Path(__lowerCAmelCase ) )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCAmelCase ).stat().st_size:
          self.fail('''Quantized model is bigger than initial ONNX model''' )

       @require_torch
       @slow
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowercase__				:    Union[str, Any]  =					self._test_export(__lowerCAmelCase	,   '''pt'''	,   12	,   **__lowerCAmelCase )
         lowercase__				:    List[Any]  =					quantize(__lowerCAmelCase )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCAmelCase ).stat().st_size:
          self.fail('''Quantized model is bigger than initial ONNX model''' )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=None	,   **__lowerCAmelCase )  ->  Union[str, Any]:
        try:
         # Compute path
         with TemporaryDirectory() as tempdir:
          lowercase__				:    Optional[Any]  =					Path(__lowerCAmelCase ).joinpath('''model.onnx''' )

         # Remove folder if exists
         if path.parent.exists():
          path.parent.rmdir()

         # Export
         convert(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )

         return path
        except Exception as e:
         self.fail(__lowerCAmelCase )

       @require_torch
       @require_tokenizers
       @slow
       def _lowerCAmelCase(						self )  ->  List[Any]:
        from transformers import BertModel

        lowercase__				:    str  =					BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
        lowercase__				:    Tuple  =					BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
        self._test_infer_dynamic_axis(__lowerCAmelCase	,   __lowerCAmelCase	,   '''pt''' )

       @require_tf
       @require_tokenizers
       @slow
       def _lowerCAmelCase(						self )  ->  int:
        from transformers import TFBertModel

        lowercase__				:    Tuple  =					TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
        lowercase__				:    int  =					BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
        self._test_infer_dynamic_axis(__lowerCAmelCase	,   __lowerCAmelCase	,   '''tf''' )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:
        lowercase__				:    Any  =					FeatureExtractionPipeline(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    List[str]  =					['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
        lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    Dict  =					infer_shapes(__lowerCAmelCase	,   __lowerCAmelCase )

        # Assert all variables are present
        self.assertEqual(len(__lowerCAmelCase )	,   len(__lowerCAmelCase ) )
        self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
        self.assertSequenceEqual(variable_names[:3]	,   __lowerCAmelCase )
        self.assertSequenceEqual(variable_names[3:]	,   __lowerCAmelCase )

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
         self.assertDictEqual(shapes[var_name]	,   {0: '''batch''', 1: '''sequence'''} )

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes['''output_0''']	,   {0: '''batch''', 1: '''sequence'''} )
        self.assertDictEqual(shapes['''output_1''']	,   {0: '''batch'''} )

       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Dict  =					['''input_ids''', '''attention_mask''', '''token_type_ids''']
        lowercase__				:    Optional[int]  =					{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
        lowercase__ ,							lowercase__				:    Optional[int]  =					ensure_valid_input(FuncContiguousArgs()	,   __lowerCAmelCase	,   __lowerCAmelCase )

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(__lowerCAmelCase )	,   3 )

        # Should have exactly the same input names
        self.assertEqual(set(__lowerCAmelCase )	,   set(__lowerCAmelCase ) )

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(__lowerCAmelCase	,   (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        lowercase__ ,							lowercase__				:    Union[str, Any]  =					ensure_valid_input(FuncNonContiguousArgs()	,   __lowerCAmelCase	,   __lowerCAmelCase )

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(__lowerCAmelCase )	,   1 )
        self.assertEqual(len(__lowerCAmelCase )	,   1 )

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0]	,   tokens['''input_ids'''] )
        self.assertEqual(ordered_input_names[0]	,   '''input_ids''' )


       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' )	,   '''-test''' )
        self.assertEqual('''/home/something/my_fake_model-test.onnx'''	,   generated.as_posix() )


'''simple docstring'''

import logging

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

logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return input_array.reshape((input_array.size, 1)     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    Optional[Any]  =					features[:, labels == i]
  lowercase__				:    Optional[Any]  =					data.mean(1     )
  # Centralize the data of class i
  lowercase__				:    Dict  =					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)
   lowercase__				:    List[str]  =					np.dot(UpperCAmelCase		,     centered_data.T     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Tuple  =					features.mean(1     )
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    List[str]  =					features[:, labels == i]
  lowercase__				:    int  =					data.shape[1]
  lowercase__				:    Optional[int]  =					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)
   lowercase__				:    Optional[int]  =					device_data * np.dot(
       column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )		,     (column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )).T		,     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):

 # Check if the features have been loaded
 if features.any():
  lowercase__				:    Optional[Any]  =					features.mean(1     )
  # Center the dataset
  lowercase__				:    List[str]  =					features - np.reshape(UpperCAmelCase		,     (data_mean.size, 1)     )
  lowercase__				:    Optional[Any]  =					np.dot(UpperCAmelCase		,     centered_data.T     ) / features.shape[1]
  lowercase__ ,							lowercase__				:    Tuple  =					np.linalg.eigh(UpperCAmelCase     )
  # Take all the columns in the reverse order (-1), and then takes only the first
  lowercase__				:    str  =					eigenvectors[:, ::-1][:, 0:dimensions]
  # Project the database on the new space
  lowercase__				:    Tuple  =					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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 assert classes > dimensions

 # Check if features have been already loaded
 if features.any:
  lowercase__ ,							lowercase__				:    Any  =					eigh(
      covariance_between_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     covariance_within_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     )
  lowercase__				:    Optional[int]  =					eigenvectors[:, ::-1][:, :dimensions]
  lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					np.linalg.svd(UpperCAmelCase     )
  lowercase__				:    List[str]  =					svd_matrix[:, 0:dimensions]
  lowercase__				:    str  =					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 			__UpperCamelCase       (   ):
 # Create dummy dataset with 2 classes and 3 features
 lowercase__				:    List[str]  =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
 lowercase__				:    Optional[Any]  =					np.array([0, 0, 0, 1, 1]     )
 lowercase__				:    str  =					2
 lowercase__				:    Dict  =					2

 # Assert that the function raises an AssertionError if dimensions > classes
 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    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 			__UpperCamelCase       (   ):
 lowercase__				:    Optional[int]  =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
 lowercase__				:    int  =					2
 lowercase__				:    Any  =					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(UpperCAmelCase     ) as error_info:
  lowercase__				:    Dict  =					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()

