



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

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



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			for attribute in key.split("."					):
						lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

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

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

						# let's reduce dimension
						lowercase__   					=			value[0]
			else:
						lowercase__   					=			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":
						lowercase__   					=			value
			elif weight_type == "weight_g":
						lowercase__   					=			value
			elif weight_type == "weight_v":
						lowercase__   					=			value
			elif weight_type == "bias":
						lowercase__   					=			value
			elif weight_type == "param":
						for attribute in hf_param_name.split("."					):
									lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			value
			else:
						lowercase__   					=			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_					):
			lowercase__   					=			None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			PARAM_MAPPING[full_name.split("."					)[-1]]
									lowercase__   					=			"param"

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

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


lowercase_						=       {
    """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					):
			lowercase__   					=			False
			for key, mapped_key in MAPPING.items():
						lowercase__   					=			"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]:
									lowercase__   					=			True
									if "*" in mapped_key:
												lowercase__   					=			name.split(SCREAMING_SNAKE_CASE_					)[0].split("."					)[-2]
												lowercase__   					=			mapped_key.replace("*"  ,					SCREAMING_SNAKE_CASE_					)
									if "weight_g" in name:
												lowercase__   					=			"weight_g"
									elif "weight_v" in name:
												lowercase__   					=			"weight_v"
									elif "bias" in name:
												lowercase__   					=			"bias"
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase__   					=			"weight"
									else:
												lowercase__   					=			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[]
			lowercase__   					=			fairseq_model.state_dict()

			lowercase__   					=			hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						lowercase__   					=			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"  ,					)
									lowercase__   					=			True
						else:
									lowercase__   					=			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			full_name.split("conv_layers."					)[-1]
			lowercase__   					=			name.split("."					)
			lowercase__   					=			int(items[0]					)
			lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=True  ,					SCREAMING_SNAKE_CASE_=False					):
			if config_path is not None:
						lowercase__   					=			WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
			else:
						lowercase__   					=			WavaVecaConfig()

			if is_seq_class:
						lowercase__   					=			read_txt_into_dict(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			idalabel
						lowercase__   					=			WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			WavaVecaFeatureExtractor(
						    feature_size=1  ,					sampling_rate=1_6000  ,					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:
									lowercase__   					=			Dictionary.load(SCREAMING_SNAKE_CASE_					)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowercase__   					=			target_dict.pad_index
									lowercase__   					=			target_dict.bos_index
									lowercase__   					=			target_dict.eos_index
									lowercase__   					=			len(target_dict.symbols					)
									lowercase__   					=			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_					)
									lowercase__   					=			target_dict.indices

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

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

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

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

			lowercase__   					=			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__":
							lowercase_						=       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""",
							)
							lowercase_						=       parser.parse_args()

							lowercase_						=       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 pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




