





'''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()
lowerCAmelCase_   :   List[Any]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   int       =					{
    "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",
}
lowerCAmelCase_   :   Union[str, Any]       =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]     )   ->    List[str]:
			a__     = {}
			with open(_lowerCamelCase					,  "r"     ) as file:
						for line_number, line in enumerate(_lowerCamelCase     ):
									a__     = line.strip()
									if line:
												a__     = line.split()
												a__     = line_number
												a__     = words[0]
												a__     = value
			return result







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[int]     )   ->    List[Any]:


			for attribute in key.split("."     ):
						a__     = getattr(_lowerCamelCase					,  _lowerCamelCase     )

			a__     = None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_lowerCamelCase     ):
									a__     = PARAM_MAPPING[full_name.split("."     )[-1]]
									a__     = "param"

			if weight_type is not None and weight_type != "param":
						a__     = getattr(_lowerCamelCase					,  _lowerCamelCase     ).shape
			elif weight_type is not None and weight_type == "param":
						a__     = hf_pointer
						for attribute in hf_param_name.split("."     ):
									a__     = getattr(_lowerCamelCase					,  _lowerCamelCase     )
						a__     = shape_pointer.shape

						# let's reduce dimension
						a__     = value[0]
			else:
						a__     = 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__     = value
			elif weight_type == "weight_g":
						a__     = value
			elif weight_type == "weight_v":
						a__     = value
			elif weight_type == "bias":
						a__     = value
			elif weight_type == "param":
						for attribute in hf_param_name.split("."     ):
									a__     = getattr(_lowerCamelCase					,  _lowerCamelCase     )
						a__     = value
			else:
						a__     = value

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







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Optional[Any]     )   ->    Optional[int]:
			a__     = None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_lowerCamelCase     ):
									a__     = PARAM_MAPPING[full_name.split("."     )[-1]]
									a__     = "param"

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

			a__     = value if "lm_head" in full_key else value[0]


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







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Union[str, Any]     )   ->    Dict:
			a__     = []
			a__     = fairseq_model.state_dict()

			a__     = hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						a__     = False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  hf_model.config.feat_extract_norm == "group"					,  )
									a__     = True
						else:
									a__     = load_wavaveca_layer(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase     )
						if not is_used:
									unused_weights.append(_lowerCamelCase     )

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







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Any     )   ->    int:
			a__     = full_name.split("conv_layers."     )[-1]
			a__     = name.split("."     )
			a__     = int(items[0]     )
			a__     = 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__     = 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__     = 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__     = 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__     = value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
			else:
						unused_weights.append(_lowerCamelCase     )







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Any=None					,  __lowerCamelCase  :     int=None					,  __lowerCamelCase  :     Dict=True					,  __lowerCamelCase  :     Dict=False     )   ->    Tuple:


			if config_path is not None:
						a__     = WavaVecaConfig.from_pretrained(_lowerCamelCase     )
			else:
						a__     = WavaVecaConfig()

			if is_seq_class:
						a__     = read_txt_into_dict(_lowerCamelCase     )
						a__     = idalabel
						a__     = WavaVecaForSequenceClassification(_lowerCamelCase     )
						a__     = WavaVecaFeatureExtractor(
						    feature_size=1					,  sampling_rate=1_6000					,  padding_value=0					,  do_normalize=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  )
						feature_extractor.save_pretrained(_lowerCamelCase     )

			elif is_finetuned:
						if dict_path:
									a__     = Dictionary.load(_lowerCamelCase     )

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

									# fairseq has the <pad> and <s> switched
									a__     = 0
									a__     = 1
									with open(_lowerCamelCase					,  "w"					,  encoding="utf-8"     ) as vocab_handle:
												json.dump(_lowerCamelCase					,  _lowerCamelCase     )
									a__     = WavaVecaCTCTokenizer(
									    _lowerCamelCase					,  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=_lowerCamelCase					,  )
									a__     = True if config.feat_extract_norm == "layer" else False
									a__     = WavaVecaFeatureExtractor(
									    feature_size=1					,  sampling_rate=1_6000					,  padding_value=0					,  do_normalize=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  )
									a__     = WavaVecaProcessor(feature_extractor=_lowerCamelCase					,  tokenizer=_lowerCamelCase     )
									processor.save_pretrained(_lowerCamelCase     )

						a__     = WavaVecaForCTC(_lowerCamelCase     )
			else:
						a__     = WavaVecaForPreTraining(_lowerCamelCase     )

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

						a__     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,  task=_lowerCamelCase     )

			a__     = model[0].eval()

			recursively_load_weights(_lowerCamelCase					,  _lowerCamelCase					,  not is_finetuned     )

			hf_wavavec.save_pretrained(_lowerCamelCase     )


if __name__ == "__main__":
		lowerCAmelCase_   :   str       =					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",
		)
		lowerCAmelCase_   :   List[str]       =					parser.parse_args()

		lowerCAmelCase_   :   Dict       =					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'''


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCAmelCase_   :   List[Any]       =					[
    "EAGER",
    "AOT_EAGER",
    "INDUCTOR",
    "NVFUSER",
    "AOT_NVFUSER",
    "AOT_CUDAGRAPHS",
    "OFI",
    "FX2TRT",
    "ONNXRT",
    "IPEX",
]







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     List[Any]=None     )   ->    Any:
			a__     = True
			while ask_again:
						a__     = input(__lowerCamelCase     )
						try:
									if default is not None and len(__lowerCamelCase     ) == 0:
												return default
									return convert_value(__lowerCamelCase     ) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]=[]					,  __lowerCamelCase  :     str=None					,  __lowerCamelCase  :     List[Any]=0     )   ->    List[Any]:
			a__     = BulletMenu(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = menu.run(default_choice=__lowerCamelCase     )
			return convert_value(__lowerCamelCase     ) if convert_value is not None else result







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[int]:
			a__     = int(__lowerCamelCase     )
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return DynamoBackend(DYNAMO_BACKENDS[value]     ).value







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    str:
			a__     = int(__lowerCamelCase     )
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:
			return {"yes": True, "no": False}[value.lower()]






class       UpperCamelCase__  (		argparse.RawDescriptionHelpFormatter     ):







	def 						__a   (				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = super()._format_usage(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				a__     = usage.replace("<command> [<args>] " , ""		)
				return usage



