



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

SCREAMING_SNAKE_CASE_				= {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
SCREAMING_SNAKE_CASE_				= [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      str:
		a_     :      List[str] 				=     {}
		with open(_A, "r"	) as file:
				for line_number, line in enumerate(_A	):
						a_     :      int 				=     line.strip()
						if line:
								a_     :      Any 				=     line.split()
								a_     :      Any 				=     line_number
								a_     :      List[Any] 				=     words[0]
								a_     :      int 				=     value
		return result



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Any:




		for attribute in key.split("."	):
				a_     :      Any 				=     getattr(_A, _A	)

		a_     :      str 				=     None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(_A	):
						a_     :      int 				=     PARAM_MAPPING[full_name.split("."	)[-1]]
						a_     :      Tuple 				=     "param"

		if weight_type is not None and weight_type != "param":
				a_     :      Union[str, Any] 				=     getattr(_A, _A	).shape
		elif weight_type is not None and weight_type == "param":
				a_     :      Tuple 				=     hf_pointer
				for attribute in hf_param_name.split("."	):
						a_     :      Tuple 				=     getattr(_A, _A	)
				a_     :      List[str] 				=     shape_pointer.shape

				# let's reduce dimension
				a_     :      List[str] 				=     value[0]
		else:
				a_     :      int 				=     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":
				a_     :      Dict 				=     value
		elif weight_type == "weight_g":
				a_     :      Union[str, Any] 				=     value
		elif weight_type == "weight_v":
				a_     :      Union[str, Any] 				=     value
		elif weight_type == "bias":
				a_     :      int 				=     value
		elif weight_type == "param":
				for attribute in hf_param_name.split("."	):
						a_     :      Any 				=     getattr(_A, _A	)
				a_     :      int 				=     value
		else:
				a_     :      Dict 				=     value

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Any:
		a_     :      Union[str, Any] 				=     None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(_A	):
						a_     :      List[Any] 				=     PARAM_MAPPING[full_name.split("."	)[-1]]
						a_     :      int 				=     "param"

		if weight_type is not None and weight_type != "param":
				a_     :      Union[str, Any] 				=     ".".join([key, weight_type]	)
		elif weight_type is not None and weight_type == "param":
				a_     :      Union[str, Any] 				=     ".".join([key, hf_param_name]	)
		else:
				a_     :      str 				=     key

		a_     :      List[str] 				=     value if "lm_head" in full_key else value[0]


SCREAMING_SNAKE_CASE_				= {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      str:
		a_     :      int 				=     False
		for key, mapped_key in MAPPING.items():
				a_     :      Dict 				=     "wav2vec2." + 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]:
						a_     :      Tuple 				=     True
						if "*" in mapped_key:
								a_     :      Any 				=     name.split(_A	)[0].split("."	)[-2]
								a_     :      Optional[int] 				=     mapped_key.replace("*", _A	)
						if "weight_g" in name:
								a_     :      Union[str, Any] 				=     "weight_g"
						elif "weight_v" in name:
								a_     :      List[Any] 				=     "weight_v"
						elif "bias" in name:
								a_     :      List[Any] 				=     "bias"
						elif "weight" in name:
								# TODO: don't match quantizer.weight_proj
								a_     :      Optional[Any] 				=     "weight"
						else:
								a_     :      Tuple 				=     None
						if hf_dict is not None:
								rename_dict(_A, _A, _A, _A, _A	)
						else:
								set_recursively(_A, _A, _A, _A, _A	)
						return is_used
		return is_used



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Any:
		a_     :      List[Any] 				=     []
		a_     :      Union[str, Any] 				=     fairseq_model.state_dict()

		a_     :      Optional[int] 				=     hf_model.wavaveca.feature_extractor

		for name, value in fairseq_dict.items():
				a_     :      Union[str, Any] 				=     False
				if "conv_layers" in name:
						load_conv_layer(
						    _A, _A, _A, _A, hf_model.config.feat_extract_norm == "group", )
						a_     :      Optional[int] 				=     True
				else:
						a_     :      Optional[int] 				=     load_wavaveca_layer(_A, _A, _A	)
				if not is_used:
						unused_weights.append(_A	)

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		a_     :      int 				=     full_name.split("conv_layers."	)[-1]
		a_     :      Optional[Any] 				=     name.split("."	)
		a_     :      Any 				=     int(items[0]	)
		a_     :      int 				=     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."""	)
						a_     :      Tuple 				=     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."""	)
						a_     :      Tuple 				=     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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
						a_     :      Union[str, Any] 				=     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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
						a_     :      Any 				=     value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
		else:
				unused_weights.append(_A	)



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=True, SCREAMING_SNAKE_CASE__=False	)			->      Any:




		if config_path is not None:
				a_     :      Optional[Any] 				=     WavaVecaConfig.from_pretrained(_A	)
		else:
				a_     :      str 				=     WavaVecaConfig()

		if is_seq_class:
				a_     :      str 				=     read_txt_into_dict(_A	)
				a_     :      List[Any] 				=     idalabel
				a_     :      List[Any] 				=     WavaVecaForSequenceClassification(_A	)
				a_     :      Optional[int] 				=     WavaVecaFeatureExtractor(
				    feature_size=1, sampling_rate=16_000, padding_value=0, do_normalize=_A, return_attention_mask=_A, )
				feature_extractor.save_pretrained(_A	)

		elif is_finetuned:
				if dict_path:
						a_     :      Dict 				=     Dictionary.load(_A	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						a_     :      List[Any] 				=     target_dict.pad_index
						a_     :      str 				=     target_dict.bos_index
						a_     :      str 				=     target_dict.eos_index
						a_     :      Tuple 				=     len(target_dict.symbols	)
						a_     :      Optional[Any] 				=     os.path.join(_A, "vocab.json"	)
						if not os.path.isdir(_A	):
								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A	)	)
								return
						os.makedirs(_A, exist_ok=_A	)
						a_     :      List[str] 				=     target_dict.indices

						# fairseq has the <pad> and <s> switched
						a_     :      str 				=     0
						a_     :      Dict 				=     1
						with open(_A, "w", encoding="utf-8"	) as vocab_handle:
								json.dump(_A, _A	)
						a_     :      Any 				=     WavaVecaCTCTokenizer(
						    _A, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=_A, )
						a_     :      Dict 				=     True if config.feat_extract_norm == "layer" else False
						a_     :      List[Any] 				=     WavaVecaFeatureExtractor(
						    feature_size=1, sampling_rate=16_000, padding_value=0, do_normalize=_A, return_attention_mask=_A, )
						a_     :      Any 				=     WavaVecaProcessor(feature_extractor=_A, tokenizer=_A	)
						processor.save_pretrained(_A	)

				a_     :      int 				=     WavaVecaForCTC(_A	)
		else:
				a_     :      Optional[Any] 				=     WavaVecaForPreTraining(_A	)

		if is_finetuned or is_seq_class:
				a_     :      Dict 				=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
		else:
				a_     :      Dict 				=     argparse.Namespace(task="audio_pretraining"	)
				a_     :      Any 				=     fairseq.tasks.setup_task(_A	)

				a_     :      str 				=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=_A	)

		a_     :      List[str] 				=     model[0].eval()

		recursively_load_weights(_A, _A, not is_finetuned	)

		hf_wavavec.save_pretrained(_A	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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"""
					)
					parser.add_argument(
					    """--is_seq_class""",
					    action="""store_true""",
					    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()

					SCREAMING_SNAKE_CASE_				= not args.not_finetuned and not args.is_seq_class
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.dict_path,
					    is_finetuned,
					    args.is_seq_class,
					)




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)