



'''simple docstring'''





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowerCamelCase     :							Dict  							=				"Run commands across TPU VMs for initial setup before running `accelerate launch`."

def    lowerCamelCase_(lowerCamelCase_=None    ) ->       Optional[int]:
						if subparsers is not None:
												UpperCAmelCase         =     subparsers.add_parser("tpu-config"    ,   description=_description    )
						else:
												UpperCAmelCase         =     argparse.ArgumentParser("Accelerate tpu-config command"    ,   description=_description    )
						# Core arguments
						UpperCAmelCase         =     parser.add_argument_group(
						    "Config Arguments"    ,   "Arguments that can be configured through `accelerate config`."    )
						config_args.add_argument(
						    "--config_file"    ,   type=lowerCamelCase_    ,   default=lowerCamelCase_    ,   help="Path to the config file to use for accelerate."    ,   )
						config_args.add_argument(
						    "--tpu_name"    ,   default=lowerCamelCase_    ,   help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."    ,   )
						config_args.add_argument(
						    "--tpu_zone"    ,   default=lowerCamelCase_    ,   help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."    ,   )
						UpperCAmelCase         =     parser.add_argument_group("TPU Arguments"    ,   "Arguments for options ran inside the TPU."    )
						pod_args.add_argument(
						    "--use_alpha"    ,   action="store_true"    ,   help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."    ,   )
						pod_args.add_argument(
						    "--command_file"    ,   default=lowerCamelCase_    ,   help="The path to the file containing the commands to run on the pod on startup."    ,   )
						pod_args.add_argument(
						    "--command"    ,   action="append"    ,   nargs="+"    ,   help="A command to run on the pod. Can be passed multiple times."    ,   )
						pod_args.add_argument(
						    "--install_accelerate"    ,   action="store_true"    ,   help="Whether to install accelerate on the pod. Defaults to False."    ,   )
						pod_args.add_argument(
						    "--accelerate_version"    ,   default="latest"    ,   help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."    ,   )
						pod_args.add_argument(
						    "--debug"    ,   action="store_true"    ,   help="If set, will print the command that would be run instead of running it."    )

						if subparsers is not None:
												parser.set_defaults(func=lowerCamelCase_    )
						return parser

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(lowerCamelCase_    ):
												UpperCAmelCase         =     load_config_from_file(args.config_file    )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		UpperCAmelCase         =     defaults.command_file
												if not args.command and defaults.commands is not None:
																		UpperCAmelCase         =     defaults.commands
												if not args.tpu_name:
																		UpperCAmelCase         =     defaults.tpu_name
												if not args.tpu_zone:
																		UpperCAmelCase         =     defaults.tpu_zone
						if args.accelerate_version == "dev":
												UpperCAmelCase         =     "git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												UpperCAmelCase         =     "accelerate -U"
						elif isinstance(parse(args.accelerate_version    )    ,   lowerCamelCase_    ):
												UpperCAmelCase         =     F'accelerate=={args.accelerate_version}'

						if not args.command_file and not args.command:
												raise ValueError("You must specify either a command file or a command to run on the pod."    )

						if args.command_file:
												with open(args.command_file    ,   "r"    ) as f:
																		UpperCAmelCase         =     [f.read().splitlines()]

    # To turn list of lists into list of strings
						if isinstance(args.command[0]    ,   lowerCamelCase_    ):
												UpperCAmelCase         =     [line for cmd in args.command for line in cmd]
						# Default to the shared folder and install accelerate
						UpperCAmelCase         =     ["cd /usr/share"]
						if args.install_accelerate:
												new_cmd += [F'pip install {args.accelerate_version}']
						new_cmd += args.command
						UpperCAmelCase         =     "; ".join(lowerCamelCase_    )

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						UpperCAmelCase         =     ["gcloud"]
						if args.use_alpha:
												cmd += ["alpha"]
						cmd += [
						    "compute",
						    "tpus",
						    "tpu-vm",
						    "ssh",
						    args.tpu_name,
						    "--zone",
						    args.tpu_zone,
						    "--command",
						    args.command,
						    "--worker",
						    "all",
						]
						if args.debug:
												print(F'Running {" ".join(lowerCamelCase_    )}'    )
												return
						subprocess.run(lowerCamelCase_    )
						print("Successfully setup pod."    )





def    lowerCamelCase_() ->       Union[str, Any]:
						UpperCAmelCase         =     tpu_command_parser()
						UpperCAmelCase         =     parser.parse_args()

						tpu_command_launcher(lowerCamelCase_    )








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

import black


__lowerCamelCase     :							str  							=				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.
__lowerCamelCase     :							Optional[Any]  							=				"    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  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								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
								UpperCAmelCase         =     "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 SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     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__      )

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

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



