"""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()
_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',
    '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',
}
_A   					=    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				for attribute in key.split("."       ):
								SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

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

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

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

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				PARAM_MAPPING[full_name.split("."       )[-1]]
												SCREAMING_SNAKE_CASE__		  =				"param"

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

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


_A   					=    {
    '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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				False
				for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE__		  =				"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]:
												SCREAMING_SNAKE_CASE__		  =				True
												if "*" in mapped_key:
																SCREAMING_SNAKE_CASE__		  =				name.split(__UpperCAmelCase       )[0].split("."       )[-2]
																SCREAMING_SNAKE_CASE__		  =				mapped_key.replace("*"  ,					__UpperCAmelCase       )
												if "weight_g" in name:
																SCREAMING_SNAKE_CASE__		  =				"weight_g"
												elif "weight_v" in name:
																SCREAMING_SNAKE_CASE__		  =				"weight_v"
												elif "bias" in name:
																SCREAMING_SNAKE_CASE__		  =				"bias"
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																SCREAMING_SNAKE_CASE__		  =				"weight"
												else:
																SCREAMING_SNAKE_CASE__		  =				None
												if hf_dict is not None:
																rename_dict(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
												else:
																set_recursively(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
												return is_used
				return is_used




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				[]
				SCREAMING_SNAKE_CASE__		  =				fairseq_model.state_dict()

				SCREAMING_SNAKE_CASE__		  =				hf_model.wavaveca.feature_extractor

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

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				full_name.split("conv_layers."       )[-1]
				SCREAMING_SNAKE_CASE__		  =				name.split("."       )
				SCREAMING_SNAKE_CASE__		  =				int(items[0]       )
				SCREAMING_SNAKE_CASE__		  =				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."""       )
												SCREAMING_SNAKE_CASE__		  =				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."""       )
												SCREAMING_SNAKE_CASE__		  =				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."""       )
												SCREAMING_SNAKE_CASE__		  =				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."""       )
												SCREAMING_SNAKE_CASE__		  =				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
				else:
								unused_weights.append(__UpperCAmelCase       )




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=False       )							->   Dict:
				if config_path is not None:
								SCREAMING_SNAKE_CASE__		  =				WavaVecaConfig.from_pretrained(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				WavaVecaConfig()

				if is_seq_class:
								SCREAMING_SNAKE_CASE__		  =				read_txt_into_dict(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				idalabel
								SCREAMING_SNAKE_CASE__		  =				WavaVecaForSequenceClassification(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				WavaVecaFeatureExtractor(
								    feature_size=1  ,					sampling_rate=16_000  ,					padding_value=0  ,					do_normalize=__UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					)
								feature_extractor.save_pretrained(__UpperCAmelCase       )

				elif is_finetuned:
								if dict_path:
												SCREAMING_SNAKE_CASE__		  =				Dictionary.load(__UpperCAmelCase       )

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

												# fairseq has the <pad> and <s> switched
												SCREAMING_SNAKE_CASE__		  =				0
												SCREAMING_SNAKE_CASE__		  =				1
												with open(__UpperCAmelCase  ,					"w"  ,					encoding="utf-8"       ) as vocab_handle:
																json.dump(__UpperCAmelCase  ,					__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				WavaVecaCTCTokenizer(
												    __UpperCAmelCase  ,					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=__UpperCAmelCase  ,					)
												SCREAMING_SNAKE_CASE__		  =				True if config.feat_extract_norm == "layer" else False
												SCREAMING_SNAKE_CASE__		  =				WavaVecaFeatureExtractor(
												    feature_size=1  ,					sampling_rate=16_000  ,					padding_value=0  ,					do_normalize=__UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					)
												SCREAMING_SNAKE_CASE__		  =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase  ,					tokenizer=__UpperCAmelCase       )
												processor.save_pretrained(__UpperCAmelCase       )

								SCREAMING_SNAKE_CASE__		  =				WavaVecaForCTC(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				WavaVecaForPreTraining(__UpperCAmelCase       )

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

								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,					task=__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				model[0].eval()

				recursively_load_weights(__UpperCAmelCase  ,					__UpperCAmelCase  ,					not is_finetuned       )

				hf_wavavec.save_pretrained(__UpperCAmelCase       )


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'
						)
						parser.add_argument(
						    '--is_seq_class',
						    action='store_true',
						    help='Whether the model to convert is a fine-tuned sequence classification model or not',
						)
						_A   					=    parser.parse_args()

						_A   					=    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 Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				Callable   , _snake_case			:				Optional[Features] = None   , _snake_case			:				str = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				Optional[dict] = None   , _snake_case			:				Optional[int] = None   , **_snake_case			:				Dict   , )							->       Any:
									super().__init__(
									    features=_snake_case   , cache_dir=_snake_case   , keep_in_memory=_snake_case   , streaming=_snake_case   , num_proc=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				Generator(
									    cache_dir=_snake_case   , features=_snake_case   , generator=_snake_case   , gen_kwargs=_snake_case   , **_snake_case   , )



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[Any]:
									# Build iterable dataset
									if self.streaming:
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_streaming_dataset(split="train"		)
									# Build regular (map-style) dataset
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

													self.builder.download_and_prepare(
													    download_config=_snake_case   , download_mode=_snake_case   , verification_mode=_snake_case   , base_path=_snake_case   , num_proc=self.num_proc   , )
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_dataset(
													    split="train"   , verification_mode=_snake_case   , in_memory=self.keep_in_memory		)
									return dataset




