
from math import sqrt





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        number >= 0
    ), "'number' must been an int and positive"

    snake_case__												=				True

    # 0 and 1 are none primes.
    if number <= 1:
        snake_case__												=				False

    for divisor in range(2    ,		int(round(sqrt(__lowerCAmelCase							)							)							) + 1							):
        # if 'number' divisible by 'divisor' then sets 'status'
        # of false and break up the loop.
        if number % divisor == 0:
            snake_case__												=				False
            break

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'status' must been from type bool"

    return status





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n > 2), "'N' must been an int and > 2"

    # beginList: contains all natural numbers from 2 up to N
    snake_case__												=				list(range(2    ,		n + 1							)							)

    snake_case__												=				[]  # this list will be returns.

    # actual sieve of erathostenes
    for i in range(len(__lowerCAmelCase							)							):
        for j in range(i + 1    ,		len(__lowerCAmelCase							)							):
            if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                snake_case__												=				0

    # filters actual prime numbers.
    snake_case__												=				[x for x in begin_list if x != 0]

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'ans' must been from type list"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n > 2), "'N' must been an int and > 2"

    snake_case__												=				[]

    # iterates over all numbers between 2 up to N+1
    # if a number is prime then appends to list 'ans'
    for number in range(2    ,		n + 1							):
        if is_prime(__lowerCAmelCase							):
            ans.append(__lowerCAmelCase							)

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'ans' must been from type list"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and number >= 0, "'number' must been an int and >= 0"

    snake_case__												=				[]  # this list will be returns of the function.

    # potential prime number factors.

    snake_case__												=				2

    snake_case__												=				number

    if number == 0 or number == 1:
        ans.append(__lowerCAmelCase							)

    # if 'number' not prime then builds the prime factorization of 'number'
    elif not is_prime(__lowerCAmelCase							):
        while quotient != 1:
            if is_prime(__lowerCAmelCase							) and (quotient % factor == 0):
                ans.append(__lowerCAmelCase							)
                quotient /= factor
            else:
                factor += 1

    else:
        ans.append(__lowerCAmelCase							)

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'ans' must been from type list"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    snake_case__												=				0

    # prime factorization of 'number'
    snake_case__												=				prime_factorization(__lowerCAmelCase							)

    snake_case__												=				max(__lowerCAmelCase							)

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'ans' must been from type int"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    snake_case__												=				0

    # prime factorization of 'number'
    snake_case__												=				prime_factorization(__lowerCAmelCase							)

    snake_case__												=				min(__lowerCAmelCase							)

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'ans' must been from type int"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'number' must been an int"
    assert isinstance(number % 2 == 0    ,		__lowerCAmelCase							), "compare bust been from type bool"

    return number % 2 == 0





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							), "'number' must been an int"
    assert isinstance(number % 2 != 0    ,		__lowerCAmelCase							), "compare bust been from type bool"

    return number % 2 != 0





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (number > 2) and is_even(__lowerCAmelCase							)
    ), "'number' must been an int, even and > 2"

    snake_case__												=				[]  # this list will returned

    # creates a list of prime numbers between 2 up to 'number'
    snake_case__												=				get_prime_numbers(__lowerCAmelCase							)
    snake_case__												=				len(__lowerCAmelCase							)

    # run variable for while-loops.
    snake_case__												=				0
    snake_case__												=				None

    # exit variable. for break up the loops
    snake_case__												=				True

    while i < len_pn and loop:
        snake_case__												=				i + 1

        while j < len_pn and loop:
            if prime_numbers[i] + prime_numbers[j] == number:
                snake_case__												=				False
                ans.append(prime_numbers[i]							)
                ans.append(prime_numbers[j]							)

            j += 1

        i += 1

    # precondition
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (len(__lowerCAmelCase							) == 2)
        and (ans[0] + ans[1] == number)
        and is_prime(ans[0]							)
        and is_prime(ans[1]							)
    ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (numbera >= 0)
        and (numbera >= 0)
    ), "'number1' and 'number2' must been positive integer."

    snake_case__												=				0

    while numbera != 0:
        snake_case__												=				numbera % numbera
        snake_case__												=				numbera
        snake_case__												=				rest

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        numbera >= 0
    ), "'number' must been from type int and positive"

    return numbera





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (numbera >= 1)
        and (numbera >= 1)
    ), "'number1' and 'number2' must been positive integer."

    snake_case__												=				1  # actual answer that will be return.

    # for kgV (x,1)
    if numbera > 1 and numbera > 1:
        # builds the prime factorization of 'number1' and 'number2'
        snake_case__												=				prime_factorization(__lowerCAmelCase							)
        snake_case__												=				prime_factorization(__lowerCAmelCase							)

    elif numbera == 1 or numbera == 1:
        snake_case__												=				[]
        snake_case__												=				[]
        snake_case__												=				max(__lowerCAmelCase    ,		__lowerCAmelCase							)

    snake_case__												=				0
    snake_case__												=				0

    snake_case__												=				[]  # captured numbers int both 'primeFac1' and 'primeFac2'

    # iterates through primeFac1
    for n in prime_fac_a:
        if n not in done:
            if n in prime_fac_a:
                snake_case__												=				prime_fac_a.count(__lowerCAmelCase							)
                snake_case__												=				prime_fac_a.count(__lowerCAmelCase							)

                for _ in range(max(__lowerCAmelCase    ,		__lowerCAmelCase							)							):
                    ans *= n

            else:
                snake_case__												=				prime_fac_a.count(__lowerCAmelCase							)

                for _ in range(__lowerCAmelCase							):
                    ans *= n

            done.append(__lowerCAmelCase							)

    # iterates through primeFac2
    for n in prime_fac_a:
        if n not in done:
            snake_case__												=				prime_fac_a.count(__lowerCAmelCase							)

            for _ in range(__lowerCAmelCase							):
                ans *= n

            done.append(__lowerCAmelCase							)

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        ans >= 0
    ), "'ans' must been from type int and positive"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n >= 0), "'number' must been a positive int"

    snake_case__												=				0
    snake_case__												=				2  # this variable holds the answer

    while index < n:
        index += 1

        ans += 1  # counts to the next number

        # if ans not prime then
        # runs to the next prime number.
        while not is_prime(__lowerCAmelCase							):
            ans += 1

    # precondition
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and is_prime(
        __lowerCAmelCase							), "'ans' must been a prime number and from type int"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    assert (
        is_prime(__lowerCAmelCase							) and is_prime(__lowerCAmelCase							) and (p_number_a < p_number_a)
    ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

    snake_case__												=				p_number_a + 1  # jump to the next number

    snake_case__												=				[]  # this list will be returns.

    # if number is not prime then
    # fetch the next prime number.
    while not is_prime(__lowerCAmelCase							):
        number += 1

    while number < p_number_a:
        ans.append(__lowerCAmelCase							)

        number += 1

        # fetch the next prime number.
        while not is_prime(__lowerCAmelCase							):
            number += 1

    # precondition
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and ans[0] != p_number_a
        and ans[len(__lowerCAmelCase							) - 1] != p_number_a
    ), "'ans' must been a list without the arguments"

    # 'ans' contains not 'pNumber1' and 'pNumber2' !
    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n >= 1), "'n' must been int and >= 1"

    snake_case__												=				[]  # will be returned.

    for divisor in range(1    ,		n + 1							):
        if n % divisor == 0:
            ans.append(__lowerCAmelCase							)

    # precondition
    assert ans[0] == 1 and ans[len(__lowerCAmelCase							) - 1] == n, "Error in function getDivisiors(...)"

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (
        number > 1
    ), "'number' must been an int and >= 1"

    snake_case__												=				get_divisors(__lowerCAmelCase							)

    # precondition
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (divisors[0] == 1)
        and (divisors[len(__lowerCAmelCase							) - 1] == number)
    ), "Error in help-function getDivisiors(...)"

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]							) == number





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (denominator != 0)
    ), "The arguments must been from type int and 'denominator' != 0"

    # build the greatest common divisor of numerator and denominator.
    snake_case__												=				gcd(abs(__lowerCAmelCase							)    ,		abs(__lowerCAmelCase							)							)

    # precondition
    assert (
        isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							)
        and (numerator % gcd_of_fraction == 0)
        and (denominator % gcd_of_fraction == 0)
    ), "Error in function gcd(...,...)"

    return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n >= 0), "'n' must been a int and >= 0"

    snake_case__												=				1  # this will be return.

    for factor in range(1    ,		n + 1							):
        ans *= factor

    return ans





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and (n >= 0), "'n' must been an int and >= 0"

    snake_case__												=				0
    snake_case__												=				1
    snake_case__												=				1  # this will be return

    for _ in range(n - 1							):
        snake_case__												=				ans
        ans += fiba
        snake_case__												=				tmp

    return ans





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__magic_name__            =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__magic_name__            =  '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,	"models/bert/" ) )
          snake_case__						=				self.transformer_dir
          shutil.copy(
              os.path.join(lowerCamelCase		,	"src/transformers/models/bert/modeling_bert.py" )		,	os.path.join(self.transformer_dir		,	"models/bert/modeling_bert.py" )		,	)
      def A_			(			self ):
          snake_case__						=				"src/transformers"
          shutil.rmtree(self.transformer_dir )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None ):
          snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
              snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          snake_case__						=				black.Mode(target_versions={black.TargetVersion.PYaa}		,	line_length=1_19 )
          snake_case__						=				black.format_str(lowerCamelCase		,	mode=lowerCamelCase )
          snake_case__						=				os.path.join(self.transformer_dir		,	"new_code.py" )
          with open(lowerCamelCase		,	"w"		,	newline="\n" ) as f:
              f.write(lowerCamelCase )
          if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase ) ) == 0 )
          else:
              check_copies.is_copy_consistent(f.name		,	overwrite=lowerCamelCase )
              with open(lowerCamelCase		,	"r" ) as f:
                  self.assertTrue(f.read()		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          # Base copy consistency
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	REFERENCE_CODE + "\n"		,	)

          # With no empty line at the end
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	lowerCamelCase		,	)

          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)

          # Copy consistency with a really long name
          snake_case__						=				"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
          self.check_copy_consistency(
              F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,	F"""{long_class_name}LMPredictionHead"""		,	re.sub("Bert"		,	lowerCamelCase		,	lowerCamelCase )		,	)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	lowerCamelCase		,	overwrite_result=re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)



      def A_			(			self ):
          snake_case__						=				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
              " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
              " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
              " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
              " Luong, Quoc V. Le, Christopher D. Manning."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
              " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
              " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
              " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
              " Christopher D. Manning 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          self.assertFalse(lowerCamelCase )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(lowerCamelCase )

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
              " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check if the model link is synchronized.
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )



