

'''simple docstring'''







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase									=     '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase									=     direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCamelCase									=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCamelCase									=     {
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F'''config.{attribute}''' in modeling_source
                         or F'''getattr(config, "{attribute}"''' in modeling_source
                         or F'''getattr(self.config, "{attribute}"''' in modeling_source
                     ):
                            A_						       =   True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             rF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''',							UpperCAmelCase__,							)
                         is not None
                     ):
                            A_						       =   True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   A_						       =   True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       A_						       =   [
           """bos_index""",
           """eos_index""",
           """pad_index""",
           """unk_index""",
           """mask_index""",
           """image_size""",
           """use_cache""",
           """out_features""",
           """out_indices""",
       ]
       A_						       =   ["""encoder_no_repeat_ngram_size"""]

       # Special cases to be allowed
       A_						       =   True
       if not attribute_used:
              A_						       =   False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            A_						       =   True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            A_						       =   True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            A_						       =   True
                     elif attribute.endswith("""_token_id"""					):
                            A_						       =   True

                     # configuration class specific cases
                     if not case_allowed:
                            A_						       =   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__,							[]					)
                            A_						       =   allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      int:
       A_						       =   dict(inspect.signature(config_class.__init__					).parameters					)
       A_						       =   [x for x in list(signature.keys()					) if x not in ["""self""", """kwargs"""]]
       A_						       =   [signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       A_						       =   {}
       if len(config_class.attribute_map					) > 0:
              A_						       =   {v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       A_						       =   inspect.getsourcefile(UpperCAmelCase__					)
       A_						       =   os.path.dirname(UpperCAmelCase__					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       A_						       =   [os.path.join(UpperCAmelCase__,							UpperCAmelCase__					) for fn in os.listdir(UpperCAmelCase__					) if fn.startswith("""modeling_"""					)]

       # Get the source code strings
       A_						       =   []
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase__					):
                     with open(UpperCAmelCase__					) as fp:
                            modeling_sources.append(fp.read()					)

       A_						       =   []
       for config_param, default_value in zip(UpperCAmelCase__,							UpperCAmelCase__					):
              # `attributes` here is all the variant names for `config_param`
              A_						       =   [config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase__					)
def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   {}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              A_						       =   [
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					),							lambda UpperCAmelCase__					: inspect.isclass(UpperCAmelCase__					)
                      and issubclass(UpperCAmelCase__,							UpperCAmelCase__					)
                      and inspect.getmodule(UpperCAmelCase__					) == inspect.getmodule(_config_class					),							)
              ]
              for config_class in config_classes_in_module:
                     A_						       =   check_config_attributes_being_used(UpperCAmelCase__					)
                     if len(UpperCAmelCase__					) > 0:
                            A_						       =   unused_attributes

       if len(UpperCAmelCase__					) > 0:
              A_						       =   """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
              for name, attributes in configs_with_unused_attributes.items():
                     error += F'''{name}: {attributes}\n'''

              raise ValueError(UpperCAmelCase__					)


if __name__ == "__main__":
      check_config_attributes()



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

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

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

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







def    UpperCAmelCase__     (   )      ->      int:
       A_						       =   tpu_command_parser()
       A_						       =   parser.parse_args()

       tpu_command_launcher(UpperCAmelCase__					)
