

'''simple docstring'''






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

import black


lowerCAmelCase__											=	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__											=	'''    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 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = tempfile.mkdtemp()
   os.makedirs(os.path.join(self.transformer_dir					,'''models/bert/'''			)			)
   __lowercase							  = self.transformer_dir
   shutil.copy(
       os.path.join(lowercase__					,'''src/transformers/models/bert/modeling_bert.py'''			)					,os.path.join(self.transformer_dir					,'''models/bert/modeling_bert.py'''			)					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = '''src/transformers'''
   shutil.rmtree(self.transformer_dir			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     List[str]					,lowercase__   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     Dict=None			):
   __lowercase							  = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
   if overwrite_result is not None:
    __lowercase							  = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
   __lowercase							  = black.Mode(target_versions={black.TargetVersion.PYaa}					,line_length=1_1_9			)
   __lowercase							  = black.format_str(lowercase__					,mode=lowercase__			)
   __lowercase							  = os.path.join(self.transformer_dir					,'''new_code.py'''			)
   with open(lowercase__					,'''w'''					,newline='''\n'''			) as f:
    f.write(lowercase__			)
   if overwrite_result is None:
    self.assertTrue(len(check_copies.is_copy_consistent(lowercase__			)			) == 0			)
   else:
    check_copies.is_copy_consistent(f.name					,overwrite=lowercase__			)
    with open(lowercase__					,'''r'''			) as f:
     self.assertTrue(f.read()					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # 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'''					,lowercase__					,)

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

   # Copy consistency with a really long name
   __lowercase							  = '''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'''					,lowercase__					,lowercase__			)					,)

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

   __lowercase							  = (
       '''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.'''
   )
   __lowercase							  = (
       '''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'''
   )
   __lowercase							  = (
       '''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'''
   )

   __lowercase  ,	__lowercase							  = check_copies.convert_to_localized_md(
       lowercase__					,lowercase__					,localized_readme['''format_model_list''']			)

   self.assertFalse(lowercase__			)
   self.assertEqual(lowercase__					,lowercase__			)

   __lowercase  ,	__lowercase							  = check_copies.convert_to_localized_md(
       lowercase__					,lowercase__					,localized_readme['''format_model_list''']			)

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

   __lowercase							  = (
       '''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.'''
   )
   __lowercase							  = (
       '''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'''
   )
   __lowercase							  = (
       '''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'''
   )

   __lowercase  ,	__lowercase							  = check_copies.convert_to_localized_md(
       lowercase__					,lowercase__					,localized_readme['''format_model_list''']			)

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




'''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__											=	'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''


def 		_A     (    A__=None      ):






 """simple docstring"""



 if subparsers is not None:
  __lowercase							  = subparsers.add_parser('''tpu-config'''     ,				description=_description      )
 else:
  __lowercase							  = argparse.ArgumentParser('''Accelerate tpu-config command'''     ,				description=_description      )
 # Core arguments
 __lowercase							  = parser.add_argument_group(
     '''Config Arguments'''     ,				'''Arguments that can be configured through `accelerate config`.'''      )
 config_args.add_argument(
     '''--config_file'''     ,				type=A__     ,				default=A__     ,				help='''Path to the config file to use for accelerate.'''     ,				)
 config_args.add_argument(
     '''--tpu_name'''     ,				default=A__     ,				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=A__     ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''     ,				)
 __lowercase							  = 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=A__     ,				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=A__      )
 return parser


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = None

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

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

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 __lowercase							  = ['''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(A__      )}"      )
  return
 subprocess.run(A__      )
 print('''Successfully setup pod.'''      )






def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = tpu_command_parser()
 __lowercase							  = parser.parse_args()

 tpu_command_launcher(A__      )

