




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
a													=			"src/transformers"


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

a													=			transformers.models.auto.configuration_auto.CONFIG_MAPPING

a													=			{
    # 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      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Dict:
						_UpperCAmelCase      =			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
																		):
																								_UpperCAmelCase      =			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}\""			,     snake_case			,     )
																		    is not None
																		):
																								_UpperCAmelCase      =			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:
																														_UpperCAmelCase      =			True
																		if attribute_used:
																								break
												if attribute_used:
																		break

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

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

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

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

						return attribute_used or case_allowed




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

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

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

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

						return sorted(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		)						->				Any:
						_UpperCAmelCase      =			{}
						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.)
												_UpperCAmelCase      =			[
												    cls
												    for name, cls in inspect.getmembers(
												        inspect.getmodule(_config_class       )			,     lambda snake_case       : inspect.isclass(snake_case       )
												        and issubclass(snake_case			,     snake_case       )
												        and inspect.getmodule(snake_case       ) == inspect.getmodule(_config_class       )			,     )
												]
												for config_class in config_classes_in_module:
																		_UpperCAmelCase      =			check_config_attributes_being_used(snake_case       )
																		if len(snake_case       ) > 0:
																								_UpperCAmelCase      =			unused_attributes

						if len(snake_case       ) > 0:
												_UpperCAmelCase      =			"""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(snake_case       )


if __name__ == "__main__":
				check_config_attributes()





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a													=			logging.get_logger(__name__)

a													=			{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
a													=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[str]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
												    f" {value.shape} for {full_name}"       )

						if weight_type == "weight":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_g":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_v":
												_UpperCAmelCase      =			value
						elif weight_type == "bias":
												_UpperCAmelCase      =			value
						else:
												_UpperCAmelCase      =			value

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			[]
						_UpperCAmelCase      =			fairseq_model.state_dict()

						_UpperCAmelCase      =			hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase      =			"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
																														if "layer_norm_for_extract" in name and (".".join(name.split("""."""       )[:-1]       ) != key):
																																				# special case since naming is very similar
																																				continue
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCAmelCase      =			"""weight"""
																														else:
																																				_UpperCAmelCase      =			None
																														set_recursively(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

						logger.warning(f"Unused weights: {unused_weights}"       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
						_UpperCAmelCase      =			name.split("""."""       )
						_UpperCAmelCase      =			int(items[0]       )
						_UpperCAmelCase      =			int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						else:
												unused_weights.append(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=True       )						->				List[Any]:
						if config_path is not None:
												_UpperCAmelCase      =			UniSpeechSatConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			UniSpeechSatConfig()

						_UpperCAmelCase      =			""""""

						if is_finetuned:
												_UpperCAmelCase      =			UniSpeechSatForCTC(snake_case       )
						else:
												_UpperCAmelCase      =			UniSpeechSatForPreTraining(snake_case       )

						_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
						_UpperCAmelCase      =			model[0].eval()

						recursively_load_weights(snake_case			,     snake_case       )

						hf_wavavec.save_pretrained(snake_case       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				a													=			parser.parse_args()
				convert_unispeech_sat_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)