


'''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   : int             =				"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase   : Optional[int]             =				direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase   : Any             =				transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase   : str             =				{
    # 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							,A							,A							,A							)  ->				Tuple:
	lowercase						:		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
			):
				lowercase						:		str   					=		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
			):
				lowercase						:		Union[str, 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:
					lowercase						:		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
	lowercase						:		Union[str, Any]   					=		[
	    "bos_index",
	    "eos_index",
	    "pad_index",
	    "unk_index",
	    "mask_index",
	    "image_size",
	    "use_cache",
	    "out_features",
	    "out_indices",
	]
	lowercase						:		Union[str, Any]   					=		["encoder_no_repeat_ngram_size"]

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

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

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

	return attribute_used or case_allowed




def 	_A  (       A							)  ->				int:
	lowercase						:		Any   					=		dict(inspect.signature(config_class.__init__							).parameters							)
	lowercase						:		str   					=		[x for x in list(signature.keys()							) if x not in ["self", "kwargs"]]
	lowercase						:		List[Any]   					=		[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
	lowercase						:		List[Any]   					=		{}
	if len(config_class.attribute_map							) > 0:
		lowercase						:		str   					=		{v: k for k, v in config_class.attribute_map.items()}

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

	# Get the source code strings
	lowercase						:		Union[str, Any]   					=		[]
	for path in modeling_paths:
		if os.path.isfile(A							):
			with open(A							) as fp:
				modeling_sources.append(fp.read()							)

	lowercase						:		Any   					=		[]
	for config_param, default_value in zip(A							,A							):
		# `attributes` here is all the variant names for `config_param`
		lowercase						:		Tuple   					=		[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  (       )  ->				Optional[int]:
	lowercase						:		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.)
		lowercase						:		Tuple   					=		[
		    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:
			lowercase						:		Tuple   					=		check_config_attributes_being_used(A							)
			if len(A							) > 0:
				lowercase						:		Optional[int]   					=		unused_attributes

	if len(A							) > 0:
		lowercase						:		Any   					=		"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'''
from manim import *




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		Rectangle(height=0.5							,      width=0.5							)
					lowercase						:		str   					=		Rectangle(height=0.25							,      width=0.25							)
					lowercase						:		Union[str, Any]   					=		Rectangle(height=0.46							,      width=0.46							).set_stroke(width=0							)

					lowercase						:		List[str]   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		List[str]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Union[str, Any]   					=		Text("CPU"							,      font_size=2_4							)
					lowercase						:		List[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					cpu.move_to([-2.5, -0.5, 0]							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		[mem.copy() for i in range(4							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		Text("GPU"							,      font_size=2_4							)
					lowercase						:		Tuple   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					gpu.move_to([-1, -1, 0]							)
					self.add(a_							)

					lowercase						:		Tuple   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Optional[int]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		Any   					=		Text("Model"							,      font_size=2_4							)
					lowercase						:		str   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					model.move_to([3, -1.0, 0]							)
					self.add(a_							)

					lowercase						:		Dict   					=		[]
					lowercase						:		Tuple   					=		[]
					lowercase						:		List[Any]   					=		[]

					for i, rect in enumerate(a_							):
						rect.set_stroke(a_							)

						lowercase						:		Tuple   					=		Rectangle(height=0.46 / 4							,      width=0.46 / 3							).set_stroke(width=0.0							).set_fill(a_							,      opacity=0.7							)

						if i == 0:
							cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)							,      buff=0.02							,      direction=a_							)
							cpu_target.set_x(cpu_target.get_x() + 0.1							)
						elif i == 3:
							cpu_target.next_to(model_cpu_arr[0]							,      direction=a_							,      buff=0.0							)
						else:
							cpu_target.next_to(model_cpu_arr[i - 1]							,      direction=a_							,      buff=0.0							)
						self.add(a_							)
						model_cpu_arr.append(a_							)

					self.add(*a_							,      *a_							,      *a_							)

					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		List[str]   					=		Text("Loaded Checkpoint"							,      font_size=2_4							)
					lowercase						:		Optional[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					checkpoint.move_to([3, 0.5, 0]							)

					self.add(a_							)

					lowercase						:		Any   					=		[]
					lowercase						:		int   					=		[]

					for i, rect in enumerate(a_							):
						lowercase						:		str   					=		fill.copy().set_fill(a_							,      opacity=0.7							)
						target.move_to(a_							)
						ckpt_arr.append(a_							)

						lowercase						:		Optional[Any]   					=		target.copy()
						if i < 5:
							cpu_target.move_to(cpu_left_col_base[i + 1]							)
						else:
							cpu_target.move_to(cpu_right_col_base[i - 5]							)
						ckpt_cpu_arr.append(a_							)
					self.add(*a_							,      *a_							)

					lowercase						:		Optional[Any]   					=		Square(side_length=2.2							)
					key.move_to([-5, 2, 0]							)

					lowercase						:		str   					=		MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''							,      font_size=1_8							,      )

					key_text.move_to([-5, 2.4, 0]							)

					self.add(a_							,      a_							)

					lowercase						:		Any   					=		MarkupText(
					    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''							,      font_size=1_8							,      )

					blue_text.next_to(a_							,      DOWN * 2.4							,      aligned_edge=key_text.get_left()							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		MarkupText(
					    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''							,      font_size=2_4							,      )
					step_a.move_to([2, 2, 0]							)

					lowercase						:		Any   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Any   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Optional[Any]   					=		Text("Disk"							,      font_size=2_4							)
					lowercase						:		List[str]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					disk.move_to([-4.0, -1.25, 0]							)
					self.play(Write(a_							,      run_time=3							)							,      Write(a_							,      run_time=1							)							,      Create(a_							,      run_time=1							)							)

					lowercase						:		Optional[Any]   					=		[]
					for i, rect in enumerate(a_							):
						lowercase						:		int   					=		rect.copy()
						target.generate_target()
						target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
						animations.append(MoveToTarget(a_							,      run_time=1.5							)							)
					self.play(*a_							)

					self.play(FadeOut(a_							)							)

					lowercase						:		List[Any]   					=		MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''							,      font_size=2_4							)
					step_a.move_to([2, 2, 0]							)

					self.play(Write(a_							,      run_time=3							)							)

					self.play(
					    FadeOut(a_							,      a_							,      *a_							,      *a_							)							,      )

					self.wait()


