





'''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      __A						(							a_						:  Tuple      ,a_						:  Optional[Any]      ,a_						:  Dict      ,a_						:  int	):
		lowerCAmelCase     :    Optional[Any]						=   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
						):
								lowerCAmelCase     :    Optional[int]						=   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}"'''      ,a_      ,)
						    is not None
						):
								lowerCAmelCase     :    List[Any]						=   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:
										lowerCAmelCase     :    Optional[Any]						=   True
						if attribute_used:
								break
				if attribute_used:
						break

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

		# Special cases to be allowed
		lowerCAmelCase     :    Optional[Any]						=   True
		if not attribute_used:
				lowerCAmelCase     :    Any						=   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:
								lowerCAmelCase     :    str						=   True
						elif attribute in ["tie_word_embeddings"] and default_value is False:
								lowerCAmelCase     :    Optional[Any]						=   True

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

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

		return attribute_used or case_allowed






def      __A						(							a_						:  Union[str, Any]	):
		lowerCAmelCase     :    Dict						=   dict(inspect.signature(config_class.__init__	).parameters	)
		lowerCAmelCase     :    Dict						=   [x for x in list(signature.keys()	) if x not in ["self", "kwargs"]]
		lowerCAmelCase     :    str						=   [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
		lowerCAmelCase     :    int						=   {}
		if len(config_class.attribute_map	) > 0:
				lowerCAmelCase     :    Optional[int]						=   {v: k for k, v in config_class.attribute_map.items()}

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

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

		lowerCAmelCase     :    List[str]						=   []
		for config_param, default_value in zip(a_      ,a_	):
				# `attributes` here is all the variant names for `config_param`
				lowerCAmelCase     :    int						=   [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(a_      ,a_      ,a_      ,a_	):
						unused_attributes.append(attributes[0]	)

		return sorted(a_	)






def      __A						(							):
		lowerCAmelCase     :    Any						=   {}
		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.)
				lowerCAmelCase     :    int						=   [
				    cls
				    for name, cls in inspect.getmembers(
				        inspect.getmodule(_config_class	)      ,lambda a_	: inspect.isclass(a_	)
				        and issubclass(a_      ,a_	)
				        and inspect.getmodule(a_	) == inspect.getmodule(_config_class	)      ,)
				]
				for config_class in config_classes_in_module:
						lowerCAmelCase     :    str						=   check_config_attributes_being_used(a_	)
						if len(a_	) > 0:
								lowerCAmelCase     :    Tuple						=   unused_attributes

		if len(a_	) > 0:
				lowerCAmelCase     :    Optional[int]						=   "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(a_	)


if __name__ == "__main__":
					check_config_attributes()










'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def      __A						(							a_						:  int	):
		return EnvironmentCommand()






def      __A						(							a_						:  Tuple	):
		return EnvironmentCommand(args.accelerate_config_file	)

class 		lowerCamelCase  (      _A       ):



	@staticmethod
	def   _lowerCamelCase    (   a_	):
			lowerCAmelCase     :    int						=   parser.add_parser("env"	)
			download_parser.set_defaults(func=a_	)
			download_parser.add_argument(
			    "--accelerate-config_file"		,      default=a_		,      help="The accelerate config file to use for the default values in the launching script."		,      )
			download_parser.set_defaults(func=a_	)



	def __init__(   self		,      a_		,      *a_	):
			lowerCAmelCase     :    List[str]						=   accelerate_config_file



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "not installed"
			if is_safetensors_available():
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   safetensors.__version__
			elif importlib.util.find_spec("safetensors"	) is not None:
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    List[str]						=   "not found"
			if is_accelerate_available():
					import accelerate
					from accelerate.commands.config import default_config_file, load_config_from_file

					lowerCAmelCase     :    int						=   accelerate.__version__
					# Get the default from the config file.
					if self._accelerate_config_file is not None or os.path.isfile(a_	):
							lowerCAmelCase     :    int						=   load_config_from_file(self._accelerate_config_file	).to_dict()

					lowerCAmelCase     :    Optional[int]						=   (
					    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
					    if isinstance(a_		,      a_	)
					    else F'''\t{accelerate_config}'''
					)

			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Union[str, Any]						=   "NA"
			if is_torch_available():
					import torch

					lowerCAmelCase     :    Optional[Any]						=   torch.__version__
					lowerCAmelCase     :    List[Any]						=   torch.cuda.is_available()

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_tf_available():
					import tensorflow as tf

					lowerCAmelCase     :    Optional[int]						=   tf.__version__
					try:
							# deprecated in v2.1
							lowerCAmelCase     :    List[str]						=   tf.test.is_gpu_available()
					except AttributeError:
							# returns list of devices, convert to bool
							lowerCAmelCase     :    List[Any]						=   bool(tf.config.list_physical_devices("GPU"	)	)

			lowerCAmelCase     :    Union[str, Any]						=   "not installed"
			lowerCAmelCase     :    Any						=   "not installed"
			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_flax_available():
					import flax
					import jax
					import jaxlib

					lowerCAmelCase     :    Union[str, Any]						=   flax.__version__
					lowerCAmelCase     :    int						=   jax.__version__
					lowerCAmelCase     :    Optional[Any]						=   jaxlib.__version__
					lowerCAmelCase     :    int						=   jax.lib.xla_bridge.get_backend().platform

			lowerCAmelCase     :    Optional[Any]						=   {
			    "`transformers` version": version,
			    "Platform": platform.platform(),
			    "Python version": platform.python_version(),
			    "Huggingface_hub version": huggingface_hub.__version__,
			    "Safetensors version": F'''{safetensors_version}''',
			    "Accelerate version": F'''{accelerate_version}''',
			    "Accelerate config": F'''{accelerate_config_str}''',
			    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
			    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
			    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
			    "Jax version": F'''{jax_version}''',
			    "JaxLib version": F'''{jaxlib_version}''',
			    "Using GPU in script?": "<fill in>",
			    "Using distributed or parallel set-up in script?": "<fill in>",
			}

			print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"	)
			print(self.format_dict(a_	)	)

			return info





	@staticmethod
	def   _lowerCamelCase    (   a_	):
			return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"



