





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
_SCREAMING_SNAKE_CASE      	=		'src/transformers'


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

_SCREAMING_SNAKE_CASE      	=		transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE      	=		{
    # 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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Dict											=	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
			):
				snake_case_		:			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}\""""   , lowerCAmelCase_   , )
			    is not None
			):
				snake_case_		:			str											=	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:
					snake_case_		:			List[str]											=	True
			if attribute_used:
				break
		if attribute_used:
			break

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

	# Special cases to be allowed
	snake_case_		:			List[Any]											=	True
	if not attribute_used:
		snake_case_		:			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:
				snake_case_		:			Dict											=	True
			elif attribute in ["tie_word_embeddings"] and default_value is False:
				snake_case_		:			Union[str, Any]											=	True

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

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

	return attribute_used or case_allowed


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Any											=	dict(inspect.signature(config_class.__init__   ).parameters   )
	snake_case_		:			Dict											=	[x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
	snake_case_		:			Optional[int]											=	[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
	snake_case_		:			List[str]											=	{}
	if len(config_class.attribute_map   ) > 0:
		snake_case_		:			Optional[Any]											=	{v: k for k, v in config_class.attribute_map.items()}

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

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

	snake_case_		:			Dict											=	[]
	for config_param, default_value in zip(lowerCAmelCase_   , lowerCAmelCase_   ):
		# `attributes` here is all the variant names for `config_param`
		snake_case_		:			Any											=	[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(lowerCAmelCase_   , lowerCAmelCase_   , lowerCAmelCase_   , lowerCAmelCase_   ):
			unused_attributes.append(attributes[0]   )

	return sorted(lowerCAmelCase_   )


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[int]											=	{}
	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.)
		snake_case_		:			Tuple											=	[
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class   )   , lambda __a   : inspect.isclass(lowerCAmelCase_   )
		        and issubclass(lowerCAmelCase_   , lowerCAmelCase_   )
		        and inspect.getmodule(lowerCAmelCase_   ) == inspect.getmodule(_config_class   )   , )
		]
		for config_class in config_classes_in_module:
			snake_case_		:			Dict											=	check_config_attributes_being_used(lowerCAmelCase_   )
			if len(lowerCAmelCase_   ) > 0:
				snake_case_		:			str											=	unused_attributes

	if len(lowerCAmelCase_   ) > 0:
		snake_case_		:			Optional[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(lowerCAmelCase_   )


if __name__ == "__main__":
						check_config_attributes()








import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Load checkpoint
	snake_case_		:			Union[str, Any]											=	torch.load(__a   , map_location='cpu'   )

	snake_case_		:			Union[str, Any]											=	chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	snake_case_		:			str											=	{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			snake_case_		:			Tuple											=	v
		else:
			snake_case_		:			Dict											=	v

	snake_case_		:			Tuple											=	chkpt['params']
	snake_case_		:			List[Any]											=	{n: v for n, v in config.items() if not isinstance(__a   , (torch.FloatTensor, numpy.ndarray)   )}

	snake_case_		:			Optional[int]											=	chkpt['dico_word2id']
	snake_case_		:			List[str]											=	{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   , ''   ): i for s, i in vocab.items()}

	# Save pytorch-model
	snake_case_		:			List[str]											=	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	snake_case_		:			Dict											=	pytorch_dump_folder_path + '/' + CONFIG_NAME
	snake_case_		:			Any											=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(__a   , __a   )

	print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )

	print(f"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

