
def      lowerCamelCase__       (       __lowerCAmelCase						:						list       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			len(UpperCAmelCase__       )
			for i in range(1					,							UpperCAmelCase__       ):
						lowerCAmelCase_      			=			collection[i]
						lowerCAmelCase_      			=			0
						lowerCAmelCase_      			=			i - 1

						while low <= high:
									lowerCAmelCase_      			=			(low + high) // 2
									if val < collection[mid]:
												lowerCAmelCase_      			=			mid - 1
									else:
												lowerCAmelCase_      			=			mid + 1
						for j in range(UpperCAmelCase__					,							UpperCAmelCase__					,							-1       ):
									lowerCAmelCase_      			=			collection[j - 1]
						lowerCAmelCase_      			=			val
			return collection


if __name__ == "__main__":
			_A     =  input("Enter numbers separated by a comma:\n").strip()
			_A     =  [int(item) for item in user_input.split(",")]
			print(binary_insertion_sort(unsorted))





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

import black


_A     =  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.
_A     =  "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,       "models/bert/"  )  )
							lowerCAmelCase_      			=			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  )       ->   List[Any]:
							lowerCAmelCase_      			=			"src/transformers"
							shutil.rmtree(self.transformer_dir  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Optional[int]:
							lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_      			=			black.Mode(target_versions={black.TargetVersion.PYaa}       ,       line_length=119  )
							lowerCAmelCase_      			=			black.format_str(_UpperCamelCase       ,       mode=_UpperCamelCase  )
							lowerCAmelCase_      			=			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  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							# 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_      			=			"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  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

							lowerCAmelCase_      			=			(
							    "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_      			=			(
							    "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_      			=			(
							    "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_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

							self.assertFalse(_UpperCamelCase  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_			,						lowerCAmelCase_      			=			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_      			=			(
							    "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_      			=			(
							    "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_      			=			(
							    "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_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

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



