






'''simple docstring'''




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class      _UpperCAmelCase    (  snake_case_							,					unittest.TestCase		):




   """simple docstring"""

   snake_case					       =		BartTokenizer
   snake_case					       =		BartTokenizerFast
   snake_case					       =		True
   snake_case					       =		filter_roberta_detectors

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         super().setUp()
         _A      =			[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         _A      =			dict(zip(__UpperCAmelCase   ,   range(len(__UpperCAmelCase	)	)	)	)
         _A      =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         _A      =			{"unk_token": "<unk>"}

         _A      =			os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["vocab_file"]	)
         _A      =			os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["merges_file"]	)
         with open(self.vocab_file   ,   "w"   ,   encoding="utf-8"	) as fp:
               fp.write(json.dumps(__UpperCAmelCase	) + "\n"	)
         with open(self.merges_file   ,   "w"   ,   encoding="utf-8"	) as fp:
               fp.write("\n".join(__UpperCAmelCase	)	)

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   **__UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         kwargs.update(self.special_tokens_map	)
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,   **__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   **__UpperCAmelCase				:			str	):





         '''simple docstring'''





         kwargs.update(self.special_tokens_map	)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,   **__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         return "lower newer", "lower newer"

   @cached_property
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         return BartTokenizer.from_pretrained("facebook/bart-large"	)

   @cached_property
   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return BartTokenizerFast.from_pretrained("facebook/bart-large"	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			["A long paragraph for summarization.", "Another paragraph for summarization."]
         _A      =			[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _A      =			tokenizer(__UpperCAmelCase   ,   max_length=len(__UpperCAmelCase	)   ,   padding=__UpperCAmelCase   ,   return_tensors="pt"	)
               self.assertIsInstance(__UpperCAmelCase   ,   __UpperCAmelCase	)

               self.assertEqual((2, 9)   ,   batch.input_ids.shape	)
               self.assertEqual((2, 9)   ,   batch.attention_mask.shape	)
               _A      =			batch.input_ids.tolist()[0]
               self.assertListEqual(__UpperCAmelCase   ,   __UpperCAmelCase	)
               # Test that special tokens are reset

   @require_torch
   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			["A long paragraph for summarization.", "Another paragraph for summarization."]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _A      =			tokenizer(__UpperCAmelCase   ,   padding=__UpperCAmelCase   ,   return_tensors="pt"	)
               # check if input_ids are returned and no labels
               self.assertIn("input_ids"   ,   __UpperCAmelCase	)
               self.assertIn("attention_mask"   ,   __UpperCAmelCase	)
               self.assertNotIn("labels"   ,   __UpperCAmelCase	)
               self.assertNotIn("decoder_attention_mask"   ,   __UpperCAmelCase	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			[
             "Summary of the text.",
             "Another summary.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _A      =			tokenizer(text_target=__UpperCAmelCase   ,   max_length=32   ,   padding="max_length"   ,   return_tensors="pt"	)
               self.assertEqual(32   ,   targets["input_ids"].shape[1]	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _A      =			tokenizer(
                   ["I am a small frog" * 1024, "I am a small frog"]   ,   padding=__UpperCAmelCase   ,   truncation=__UpperCAmelCase   ,   return_tensors="pt"	)
               self.assertIsInstance(__UpperCAmelCase   ,   __UpperCAmelCase	)
               self.assertEqual(batch.input_ids.shape   ,   (2, 1024)	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			["A long paragraph for summarization."]
         _A      =			[
             "Summary of the text.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _A      =			tokenizer(__UpperCAmelCase   ,   return_tensors="pt"	)
               _A      =			tokenizer(text_target=__UpperCAmelCase   ,   return_tensors="pt"	)
               _A      =			inputs["input_ids"]
               _A      =			targets["input_ids"]
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         pass

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                     _A      =			self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase   ,   **__UpperCAmelCase	)
                     _A      =			self.tokenizer_class.from_pretrained(__UpperCAmelCase   ,   **__UpperCAmelCase	)
                     _A      =			"A, <mask> AllenNLP sentence."
                     _A      =			tokenizer_r.encode_plus(__UpperCAmelCase   ,   add_special_tokens=__UpperCAmelCase   ,   return_token_type_ids=__UpperCAmelCase	)
                     _A      =			tokenizer_p.encode_plus(__UpperCAmelCase   ,   add_special_tokens=__UpperCAmelCase   ,   return_token_type_ids=__UpperCAmelCase	)

                     # token_type_ids should put 0 everywhere
                     self.assertEqual(sum(tokens_r["token_type_ids"]	)   ,   sum(tokens_p["token_type_ids"]	)	)

                     # attention_mask should put 1 everywhere, so sum over length should be 1
                     self.assertEqual(
                         sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)   ,   sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)   ,   )

                     _A      =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
                     _A      =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

                     # Rust correctly handles the space before the mask while python doesnt
                     self.assertSequenceEqual(tokens_p["input_ids"]   ,   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                     self.assertSequenceEqual(tokens_r["input_ids"]   ,   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

                     self.assertSequenceEqual(
                         __UpperCAmelCase   ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
                     self.assertSequenceEqual(
                         __UpperCAmelCase   ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)













'''simple docstring'''




from typing import Any

import numpy as np

def       __lowercase		(    __lowercase ) ->		bool:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(__lowercase     ,						matrix.T ) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      _A  ,			_A      =			np.linalg.eigh(__lowercase )

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0 ) )

def       __lowercase		(    __lowercase ) ->		Any:

      '''simple docstring'''


      _A      =			np.random.randn(__lowercase     ,						__lowercase )
      _A      =			np.dot(__lowercase     ,						random_matrix.T )
      assert _is_matrix_spd(__lowercase )
      return spd_matrix

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase = 1000     ,						__lowercase = 1e-8     ,						) ->		Any:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[0]
      assert _is_matrix_spd(__lowercase )

      # Initialize solution guess, residual, search direction.
      _A      =			np.zeros((np.shape(__lowercase )[0], 1) )
      _A      =			np.copy(__lowercase )
      _A      =			np.copy(__lowercase )

      # Set initial errors in solution guess and residual.
      _A      =			1e9
      _A      =			1e9
      _A      =			1e9

      # Set iteration counter to threshold number of iterations.
      _A      =			0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            _A      =			np.dot(__lowercase     ,						__lowercase )

            # The main algorithm.

            # Update search direction magnitude.
            _A      =			np.dot(ra.T     ,						__lowercase ) / np.dot(pa.T     ,						__lowercase )
            # Update solution guess.
            _A      =			xa + alpha * pa
            # Calculate new residual.
            _A      =			ra - alpha * w
            # Calculate new Krylov subspace scale.
            _A      =			np.dot(r.T     ,						__lowercase ) / np.dot(ra.T     ,						__lowercase )
            # Calculate new A conjuage search direction.
            _A      =			r + beta * pa

            # Calculate errors.
            _A      =			np.linalg.norm(r - ra )
            _A      =			np.linalg.norm(x - xa )
            _A      =			np.maximum(__lowercase     ,						__lowercase )

            # Update variables.
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x

def       __lowercase		(    ) ->		None:

      '''simple docstring'''


      _A      =			3
      _A      =			_create_spd_matrix(__lowercase )
      _A      =			np.random.randn(__lowercase     ,						1 )
      _A      =			np.dot(__lowercase     ,						__lowercase )

      # Numpy solution.
      _A      =			np.linalg.solve(__lowercase     ,						__lowercase )

      # Our implementation.
      _A      =			conjugate_gradient(__lowercase     ,						__lowercase )

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
      assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()





