




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()
UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}
					with open(SCREAMING_SNAKE_CASE_				,				"r"					) as file:
										for line_number, line in enumerate(SCREAMING_SNAKE_CASE_					):
															__lowerCAmelCase           =	line.strip()
															if line:
																				__lowerCAmelCase           =	line.split()
																				__lowerCAmelCase           =	line_number
																				__lowerCAmelCase           =	words[0]
																				__lowerCAmelCase           =	value
					return result







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]					):
					for attribute in key.split("."					):
										__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

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

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

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

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =	None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(SCREAMING_SNAKE_CASE_					):
															__lowerCAmelCase           =	PARAM_MAPPING[full_name.split("."					)[-1]]
															__lowerCAmelCase           =	"param"

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

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


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







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	fairseq_model.state_dict()

					__lowerCAmelCase           =	hf_model.wavaveca.feature_extractor

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

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	full_name.split("conv_layers."					)[-1]
					__lowerCAmelCase           =	name.split("."					)
					__lowerCAmelCase           =	int(items[0]					)
					__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
					else:
										unused_weights.append(SCREAMING_SNAKE_CASE_					)







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[str]=None				,				SCREAMING_SNAKE_CASE_							:    str=None				,				SCREAMING_SNAKE_CASE_							:    Tuple=True				,				SCREAMING_SNAKE_CASE_							:    Any=False					):
					if config_path is not None:
										__lowerCAmelCase           =	WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	WavaVecaConfig()

					if is_seq_class:
										__lowerCAmelCase           =	read_txt_into_dict(SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	idalabel
										__lowerCAmelCase           =	WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	WavaVecaFeatureExtractor(
										    feature_size=1				,				sampling_rate=1_60_00				,				padding_value=0				,				do_normalize=SCREAMING_SNAKE_CASE_				,				return_attention_mask=SCREAMING_SNAKE_CASE_				,				)
										feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_					)

					elif is_finetuned:
										if dict_path:
															__lowerCAmelCase           =	Dictionary.load(SCREAMING_SNAKE_CASE_					)

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

															# fairseq has the <pad> and <s> switched
															__lowerCAmelCase           =	0
															__lowerCAmelCase           =	1
															with open(SCREAMING_SNAKE_CASE_				,				"w"				,				encoding="utf-8"					) as vocab_handle:
																				json.dump(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
															__lowerCAmelCase           =	WavaVecaCTCTokenizer(
															    SCREAMING_SNAKE_CASE_				,				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=SCREAMING_SNAKE_CASE_				,				)
															__lowerCAmelCase           =	True if config.feat_extract_norm == "layer" else False
															__lowerCAmelCase           =	WavaVecaFeatureExtractor(
															    feature_size=1				,				sampling_rate=1_60_00				,				padding_value=0				,				do_normalize=SCREAMING_SNAKE_CASE_				,				return_attention_mask=SCREAMING_SNAKE_CASE_				,				)
															__lowerCAmelCase           =	WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_				,				tokenizer=SCREAMING_SNAKE_CASE_					)
															processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

										__lowerCAmelCase           =	WavaVecaForCTC(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	WavaVecaForPreTraining(SCREAMING_SNAKE_CASE_					)

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

										__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,				task=SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	model[0].eval()

					recursively_load_weights(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				not is_finetuned					)

					hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      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""",
					)
					UpperCamelCase__         =      parser.parse_args()

					UpperCamelCase__         =      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,
					)










from __future__ import annotations







def      _a				(						SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				):
					if (electron_conc, hole_conc, intrinsic_conc).count(0					) != 1:
										raise ValueError("You cannot supply more or less than 2 values"					)
					elif electron_conc < 0:
										raise ValueError("Electron concentration cannot be negative in a semiconductor"					)
					elif hole_conc < 0:
										raise ValueError("Hole concentration cannot be negative in a semiconductor"					)
					elif intrinsic_conc < 0:
										raise ValueError(
										    "Intrinsic concentration cannot be negative in a semiconductor"					)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    intrinsic_conc**2 / hole_conc,
										)
					elif hole_conc == 0:
										return (
										    "hole_conc",
										    intrinsic_conc**2 / electron_conc,
										)
					elif intrinsic_conc == 0:
										return (
										    "intrinsic_conc",
										    (electron_conc * hole_conc) ** 0.5,
										)
					else:
										return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




