
"""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()
__magic_name__       =			logging.get_logger(__name__)

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



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):





		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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				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            = """.""".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]


__magic_name__       =			{
    "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				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=None  ,				UpperCamelCase_=None				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=None  ,				UpperCamelCase_=None  ,				UpperCamelCase_=True  ,				UpperCamelCase_=False				):





		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=1_6000  ,				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=1_6000  ,				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__":
							__magic_name__       =			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",
							)
							__magic_name__       =			parser.parse_args()

							__magic_name__       =			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"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





