
'''simple docstring'''
from __future__ import annotations

from collections.abc import MutableSequence






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_):
     if len(lowercase_) != degree + 1:
         raise ValueError(
             "The number of coefficients should be equal to the degree + 1.")

     snake_case_ :    list[float]     			=						list(lowercase_)
     snake_case_ :    Optional[Any]     			=						degree





 def __add__(    self ,       lowercase_):

     if self.degree > polynomial_a.degree:
         snake_case_ :    Any     			=						self.coefficients[:]
         for i in range(polynomial_a.degree + 1):
             coefficients[i] += polynomial_a.coefficients[i]
         return Polynomial(self.degree ,       lowercase_)
     else:
         snake_case_ :    Union[str, Any]     			=						polynomial_a.coefficients[:]
         for i in range(self.degree + 1):
             coefficients[i] += self.coefficients[i]
         return Polynomial(polynomial_a.degree ,       lowercase_)





 def __sub__(    self ,       lowercase_):
     return self + polynomial_a * Polynomial(0 ,       [-1])





 def __neg__(    self):
     return Polynomial(self.degree ,       [-c for c in self.coefficients])





 def __mul__(    self ,       lowercase_):
     snake_case_ :    list[float]     			=						[0] * (self.degree + polynomial_a.degree + 1)
     for i in range(self.degree + 1):
         for j in range(polynomial_a.degree + 1):
             coefficients[i + j] += (
                 self.coefficients[i] * polynomial_a.coefficients[j]
             )

     return Polynomial(self.degree + polynomial_a.degree ,       lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    int | float     			=						0
     for i in range(self.degree + 1):
         result += self.coefficients[i] * (substitution**i)
     return result





 def __str__(    self):
     snake_case_ :    Union[str, Any]     			=						""
     for i in range(self.degree ,       -1 ,       -1):
         if self.coefficients[i] == 0:
             continue
         elif self.coefficients[i] > 0:
             if polynomial:
                 polynomial += " + "
         else:
             polynomial += " - "

         if i == 0:
             polynomial += str(abs(self.coefficients[i]))
         elif i == 1:
             polynomial += str(abs(self.coefficients[i])) + "x"
         else:
             polynomial += str(abs(self.coefficients[i])) + "x^" + str(lowercase_)

     return polynomial





 def __repr__(    self):
     return self.__str__()





 def        snake_case__		(    self):
     snake_case_ :    list[float]     			=						[0] * self.degree
     for i in range(self.degree):
         snake_case_ :    Dict     			=						self.coefficients[i + 1] * (i + 1)
     return Polynomial(self.degree - 1 ,       lowercase_)





 def        snake_case__		(    self ,       lowercase_ = 0):
     snake_case_ :    list[float]     			=						[0] * (self.degree + 2)
     snake_case_ :    List[str]     			=						constant
     for i in range(self.degree + 1):
         snake_case_ :    Tuple     			=						self.coefficients[i] / (i + 1)
     return Polynomial(self.degree + 1 ,       lowercase_)





 def __eq__(    self ,       lowercase_):
     if not isinstance(lowercase_ ,       lowercase_):
         return False

     if self.degree != polynomial_a.degree:
         return False

     for i in range(self.degree + 1):
         if self.coefficients[i] != polynomial_a.coefficients[i]:
             return False

     return True





 def __ne__(    self ,       lowercase_):
     return not self.__eq__(lowercase_)






'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "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_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

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





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




