





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

import black


_UpperCAmelCase       : Optional[Any]			     =       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.
_UpperCAmelCase       : Any			     =       """    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      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Any			)    ->			Dict:
     lowerCamelCase__					:   Optional[int]      				=	tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir    ,  'models/bert/'			)			)
     lowerCamelCase__					:   Dict      				=	self.transformer_dir
     shutil.copy(
         os.path.join(UpperCAmelCase    ,  'src/transformers/models/bert/modeling_bert.py'			)    ,  os.path.join(self.transformer_dir    ,  'models/bert/modeling_bert.py'			)    ,  )
 def   A_			(   self						:   Dict			)    ->			List[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	'src/transformers'
     shutil.rmtree(self.transformer_dir			)
 def   A_			(   self						:   List[Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   str=None			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Tuple      				=	comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
     if overwrite_result is not None:
         lowerCamelCase__					:   int      				=	comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
     lowerCamelCase__					:   Optional[int]      				=	black.Mode(target_versions={black.TargetVersion.PYaa}    ,  line_length=119			)
     lowerCamelCase__					:   str      				=	black.format_str(UpperCAmelCase    ,  mode=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	os.path.join(self.transformer_dir    ,  'new_code.py'			)
     with open(UpperCAmelCase    ,  'w'    ,  newline='\n'			) as f:
         f.write(UpperCAmelCase			)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase			)			) == 0			)
     else:
         check_copies.is_copy_consistent(f.name    ,  overwrite=UpperCAmelCase			)
         with open(UpperCAmelCase    ,  'r'			) as f:
             self.assertTrue(f.read()    ,  UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   Union[str, Any]      				=	check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
     self.assertEqual(UpperCAmelCase    ,  UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]			)    ->			str:
     # 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'    ,  UpperCAmelCase    ,  )

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

     # Copy consistency with a really long name
     lowerCamelCase__					:   List[str]      				=	'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'    ,  UpperCAmelCase    ,  UpperCAmelCase			)    ,  )

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







 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Any      				=	check_copies.LOCALIZED_READMES['README_zh-hans.md']

     lowerCamelCase__					:   List[Any]      				=	(
         '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.'
     )
     lowerCamelCase__					:   int      				=	(
         '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'
     )
     lowerCamelCase__					:   Optional[Any]      				=	(
         '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'
     )

     lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	check_copies.convert_to_localized_md(
         UpperCAmelCase    ,  UpperCAmelCase    ,  localized_readme['format_model_list']			)

     self.assertFalse(UpperCAmelCase			)
     self.assertEqual(UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	check_copies.convert_to_localized_md(
         UpperCAmelCase    ,  UpperCAmelCase    ,  localized_readme['format_model_list']			)

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

     lowerCamelCase__					:   Union[str, Any]      				=	(
         '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.'
     )
     lowerCamelCase__					:   Any      				=	(
         '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'
     )
     lowerCamelCase__					:   Any      				=	(
         '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'
     )

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[int]      				=	check_copies.convert_to_localized_md(
         UpperCAmelCase    ,  UpperCAmelCase    ,  localized_readme['format_model_list']			)

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












from collections import defaultdict
from math import ceil, sqrt


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 100_0000		,			_UpperCAmelCase = 10				)    ->  int:
    lowerCamelCase__					:   defaultdict      				=	defaultdict(_UpperCAmelCase				)

    for outer_width in range(3		,			(t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            lowerCamelCase__					:   List[str]      				=	max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)		,			1				)
        else:
            lowerCamelCase__					:   int      				=	1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_UpperCAmelCase		,			outer_width - 1		,			2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
    print(F"""{solution() = }""")





