





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					Collection[float] | None = None							):


    '''simple docstring'''





    if components is None:
      _snake_case   :				Optional[int]														=	[]
    _snake_case   :				str														=	list(UpperCamelCase							)







  def __len__(      self      :					List[Any]							):


    '''simple docstring'''





    return len(self.__components							)







  def __str__(      self      :					Optional[int]							):


    '''simple docstring'''





    return "(" + ",".join(map(UpperCamelCase      ,				self.__components							)							) + ")"







  def __add__(      self      :					List[Any]      ,				UpperCamelCase      :					Vector							):


    '''simple docstring'''





    _snake_case   :				Dict														=	len(self							)
    if size == len(UpperCamelCase							):
      _snake_case   :				str														=	[self.__components[i] + other.component(UpperCamelCase							) for i in range(UpperCamelCase							)]
      return Vector(UpperCamelCase							)
    else:
      raise Exception('must have the same size'							)







  def __sub__(      self      :					List[str]      ,				UpperCamelCase      :					Vector							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	len(self							)
    if size == len(UpperCamelCase							):
      _snake_case   :				List[str]														=	[self.__components[i] - other.component(UpperCamelCase							) for i in range(UpperCamelCase							)]
      return Vector(UpperCamelCase							)
    else:  # error case
      raise Exception('must have the same size'							)







  @overload
  def __mul__(      self      :					Optional[int]      ,				UpperCamelCase      :					float							):


    '''simple docstring'''





    ...







  @overload
  def __mul__(      self      :					Dict      ,				UpperCamelCase      :					Vector							):


    '''simple docstring'''





    ...







  def __mul__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					float | Vector							):


    '''simple docstring'''





    if isinstance(UpperCamelCase      ,				(float, int)							):
      _snake_case   :				List[Any]														=	[c * other for c in self.__components]
      return Vector(UpperCamelCase							)
    elif isinstance(UpperCamelCase      ,				UpperCamelCase							) and len(self							) == len(UpperCamelCase							):
      _snake_case   :				List[Any]														=	len(self							)
      _snake_case   :				Tuple														=	[self.__components[i] * other.component(UpperCamelCase							) for i in range(UpperCamelCase							)]
      return sum(UpperCamelCase							)
    else:  # error case
      raise Exception('invalid operand!'							)







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    return Vector(self.__components							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    if isinstance(UpperCamelCase      ,				UpperCamelCase							) and -len(self.__components							) <= i < len(self.__components							):
      return self.__components[i]
    else:
      raise Exception('index out of range'							)







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					float							):


    '''simple docstring'''





    assert -len(self.__components							) <= pos < len(self.__components							)
    _snake_case   :				str														=	value







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    if len(self.__components							) == 0:
      raise Exception('Vector is empty'							)
    _snake_case   :				Tuple														=	[c**2 for c in self.__components]
    return math.sqrt(sum(UpperCamelCase							)							)







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Vector      ,				UpperCamelCase      :					bool = False							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	self * other
    _snake_case   :				List[Any]														=	self.euclidean_length() * other.euclidean_length()
    if deg:
      return math.degrees(math.acos(num / den							)							)
    else:
      return math.acos(num / den							)




def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		Vector:
  assert isinstance(lowerCAmelCase    ,							lowerCAmelCase				)
  return Vector([0] * dimension				)


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int				)->		Vector:
  assert isinstance(lowerCAmelCase    ,							lowerCAmelCase				) and (isinstance(lowerCAmelCase    ,							lowerCAmelCase				))
  _snake_case   :				Any														=	[0] * dimension
  _snake_case   :				str														=	1
  return Vector(lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	float    ,							lowerCAmelCase:	Vector    ,							lowerCAmelCase:	Vector				)->		Vector:
  assert (
      isinstance(lowerCAmelCase    ,							lowerCAmelCase				)
      and isinstance(lowerCAmelCase    ,							lowerCAmelCase				)
      and (isinstance(lowerCAmelCase    ,							(int, float)				))
  )
  return x * scalar + y


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int				)->		Vector:
  random.seed(lowerCAmelCase				)
  _snake_case   :				Any														=	[random.randint(lowerCAmelCase    ,							lowerCAmelCase				) for _ in range(lowerCAmelCase				)]
  return Vector(lowerCAmelCase				)



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					str      ,				UpperCamelCase      :					list[list[float]]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    _snake_case   :				Any														=	matrix
    _snake_case   :				Union[str, Any]														=	w
    _snake_case   :				Tuple														=	h







  def __str__(      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	''
    for i in range(self.__height							):
      ans += "|"
      for j in range(self.__width							):
        if j < self.__width - 1:
          ans += str(self.__matrix[i][j]							) + ","
        else:
          ans += str(self.__matrix[i][j]							) + "|\n"
    return ans







  def __add__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					Matrix							):


    '''simple docstring'''





    if self.__width == other.width() and self.__height == other.height():
      _snake_case   :				List[Any]														=	[]
      for i in range(self.__height							):
        _snake_case   :				int														=	[
            self.__matrix[i][j] + other.component(UpperCamelCase      ,				UpperCamelCase							)
            for j in range(self.__width							)
        ]
        matrix.append(UpperCamelCase							)
      return Matrix(UpperCamelCase      ,				self.__width      ,				self.__height							)
    else:
      raise Exception('matrix must have the same dimension!'							)







  def __sub__(      self      :					List[str]      ,				UpperCamelCase      :					Matrix							):


    '''simple docstring'''





    if self.__width == other.width() and self.__height == other.height():
      _snake_case   :				Any														=	[]
      for i in range(self.__height							):
        _snake_case   :				List[str]														=	[
            self.__matrix[i][j] - other.component(UpperCamelCase      ,				UpperCamelCase							)
            for j in range(self.__width							)
        ]
        matrix.append(UpperCamelCase							)
      return Matrix(UpperCamelCase      ,				self.__width      ,				self.__height							)
    else:
      raise Exception('matrices must have the same dimension!'							)







  @overload
  def __mul__(      self      :					str      ,				UpperCamelCase      :					float							):


    '''simple docstring'''





    ...







  @overload
  def __mul__(      self      :					str      ,				UpperCamelCase      :					Vector							):


    '''simple docstring'''





    ...







  def __mul__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					float | Vector							):


    '''simple docstring'''





    if isinstance(UpperCamelCase      ,				UpperCamelCase							):  # matrix-vector
      if len(UpperCamelCase							) == self.__width:
        _snake_case   :				Union[str, Any]														=	zero_vector(self.__height							)
        for i in range(self.__height							):
          _snake_case   :				Tuple														=	[
              self.__matrix[i][j] * other.component(UpperCamelCase							)
              for j in range(self.__width							)
          ]
          ans.change_component(UpperCamelCase      ,				sum(UpperCamelCase							)							)
        return ans
      else:
        raise Exception(
            'vector must have the same size as the '
            'number of columns of the matrix!'							)
    elif isinstance(UpperCamelCase      ,				(int, float)							):  # matrix-scalar
      _snake_case   :				List[str]														=	[
          [self.__matrix[i][j] * other for j in range(self.__width							)]
          for i in range(self.__height							)
      ]
      return Matrix(UpperCamelCase      ,				self.__width      ,				self.__height							)
    return None







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    return self.__height







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    return self.__width







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    if 0 <= x < self.__height and 0 <= y < self.__width:
      return self.__matrix[x][y]
    else:
      raise Exception('change_component: indices out of bounds'							)







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					float							):


    '''simple docstring'''





    if 0 <= x < self.__height and 0 <= y < self.__width:
      _snake_case   :				Union[str, Any]														=	value
    else:
      raise Exception('change_component: indices out of bounds'							)







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    if self.__height != self.__width:
      raise Exception('Matrix is not square'							)
    _snake_case   :				Union[str, Any]														=	self.__matrix[:x] + self.__matrix[x + 1 :]
    for i in range(len(UpperCamelCase							)							):
      _snake_case   :				Optional[int]														=	minor[i][:y] + minor[i][y + 1 :]
    return Matrix(UpperCamelCase      ,				self.__width - 1      ,				self.__height - 1							).determinant()







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    if self.__height != self.__width:
      raise Exception('Matrix is not square'							)
    if 0 <= x < self.__height and 0 <= y < self.__width:
      return (-1) ** (x + y) * self.minor(UpperCamelCase      ,				UpperCamelCase							)
    else:
      raise Exception('Indices out of bounds'							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    if self.__height != self.__width:
      raise Exception('Matrix is not square'							)
    if self.__height < 1:
      raise Exception('Matrix has no element'							)
    elif self.__height == 1:
      return self.__matrix[0][0]
    elif self.__height == 2:
      return (
          self.__matrix[0][0] * self.__matrix[1][1]
          - self.__matrix[0][1] * self.__matrix[1][0]
      )
    else:
      _snake_case   :				str														=	[
          self.__matrix[0][y] * self.cofactor(0      ,				UpperCamelCase							) for y in range(self.__width							)
      ]
      return sum(UpperCamelCase							)




def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		Matrix:
  _snake_case   :				list[list[float]]														=	[[0] * n for _ in range(lowerCAmelCase				)]
  return Matrix(lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase				)







def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int				)->		Matrix:
  random.seed(lowerCAmelCase				)
  _snake_case   :				list[list[float]]														=	[
      [random.randint(lowerCAmelCase    ,							lowerCAmelCase				) for _ in range(lowerCAmelCase				)] for _ in range(lowerCAmelCase				)
  ]
  return Matrix(lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase				)











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
      import tensorflow as tf

if is_keras_nlp_available():
      from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase_						   =    ["""gpt2"""]
lowerCAmelCase_						   =    """gpt2"""

if is_tf_available():



      class        _lowerCAmelCase    (			tf.Module							):


        '''simple docstring'''







        def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Dict							):


          '''simple docstring'''





          super().__init__()
          _snake_case   :				Optional[int]														=	tokenizer
          _snake_case   :				Union[str, Any]														=	AutoConfig.from_pretrained(UpperCamelCase							)
          _snake_case   :				int														=	TFGPTaLMHeadModel.from_config(UpperCamelCase							)







        @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='text'							),)							)
        def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[int]							):


          '''simple docstring'''





          _snake_case   :				Dict														=	self.tokenizer(UpperCamelCase							)
          _snake_case   :				Union[str, Any]														=	tokenized['input_ids'].to_tensor()

          _snake_case   :				Any														=	tf.cast(input_ids_dense > 0      ,				tf.intaa							)
          # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

          _snake_case   :				Tuple														=	self.model(input_ids=UpperCamelCase      ,				attention_mask=UpperCamelCase							)['logits']

          return outputs



@require_tf
@require_keras_nlp
class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    super().setUp()

    _snake_case   :				Optional[int]														=	[GPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
    _snake_case   :				Tuple														=	[TFGPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
    assert len(self.tokenizers							) == len(self.tf_tokenizers							)

    _snake_case   :				Any														=	[
        'This is a straightforward English test sentence.',
        'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
        'Now we\'re going to add some Chinese: 一 二 三 一二三',
        'And some much more rare Chinese: 齉 堃 齉堃',
        'Je vais aussi écrire en français pour tester les accents',
        'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
    ]
    _snake_case   :				Tuple														=	list(zip(self.test_sentences      ,				self.test_sentences[::-1]							)							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers							):
      for test_inputs in self.test_sentences:
        _snake_case   :				Optional[int]														=	tokenizer([test_inputs]      ,				return_tensors='tf'							)
        _snake_case   :				Tuple														=	tf_tokenizer([test_inputs]							)

        for key in python_outputs.keys():
          # convert them to numpy to avoid messing with ragged tensors
          _snake_case   :				Dict														=	python_outputs[key].numpy()
          _snake_case   :				Optional[Any]														=	tf_outputs[key].numpy()

          self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
          self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase      ,				tf.intaa							) == tf_outputs_values							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				str														=	tf.function(UpperCamelCase							)
      for test_inputs in self.test_sentences:
        _snake_case   :				int														=	tf.constant(UpperCamelCase							)
        _snake_case   :				Tuple														=	compiled_tokenizer(UpperCamelCase							)
        _snake_case   :				int														=	tf_tokenizer(UpperCamelCase							)

        for key in eager_outputs.keys():
          self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Union[str, Any]														=	ModelToSave(tokenizer=UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Tuple														=	model.serving(UpperCamelCase							)  # Build model with some sample inputs
      with TemporaryDirectory() as tempdir:
        _snake_case   :				str														=	Path(UpperCamelCase							) / 'saved.model'
        tf.saved_model.save(UpperCamelCase      ,				UpperCamelCase      ,				signatures={'serving_default': model.serving}							)
        _snake_case   :				Optional[int]														=	tf.saved_model.load(UpperCamelCase							)
      _snake_case   :				List[str]														=	loaded_model.signatures['serving_default'](UpperCamelCase							)['output_0']
      # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
      self.assertTrue(tf.reduce_all(out == loaded_output							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Any														=	tf_tokenizer(UpperCamelCase							)  # Build model with some sample inputs

      _snake_case   :				Optional[Any]														=	tf_tokenizer.get_config()
      _snake_case   :				Tuple														=	TFGPTaTokenizer.from_config(UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	model_from_config(UpperCamelCase							)

      for key in from_config_output.keys():
        self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      # for the test to run
      _snake_case   :				Union[str, Any]														=	12_31_23

      for max_length in [3, 5, 10_24]:
        _snake_case   :				Union[str, Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
        _snake_case   :				List[str]														=	tf_tokenizer(UpperCamelCase      ,				max_length=UpperCamelCase							)

        _snake_case   :				int														=	out['input_ids'].numpy().shape[1]

        assert out_length == max_length




