
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_A  =	logging.get_logger(__name__)

_A  =	{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			for attribute in key.split("."							):
						lowerCAmelCase_         =      getattr(__lowerCAmelCase       ,				__lowerCAmelCase							)

			if weight_type is not None:
						lowerCAmelCase_         =      getattr(__lowerCAmelCase       ,				__lowerCAmelCase							).shape
			else:
						lowerCAmelCase_         =      hf_pointer.shape

			assert hf_shape == value.shape, (
			    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
			else:
						lowerCAmelCase_         =      value

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      fairseq_model.state_dict()

			lowerCAmelCase_         =      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCAmelCase_         =      False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				hf_model.config.feat_extract_norm == "group"       ,				)
									lowerCAmelCase_         =      True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCAmelCase_         =      "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") 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(__lowerCAmelCase							)[0].split("."							)[-2]
																		lowerCAmelCase_         =      mapped_key.replace("*"       ,				__lowerCAmelCase							)
															if "weight_g" in name:
																		lowerCAmelCase_         =      "weight_g"
															elif "weight_v" in name:
																		lowerCAmelCase_         =      "weight_v"
															elif "weight" in name:
																		lowerCAmelCase_         =      "weight"
															elif "bias" in name:
																		lowerCAmelCase_         =      "bias"
															else:
																		lowerCAmelCase_         =      None
															set_recursively(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
												continue
						if not is_used:
									unused_weights.append(__lowerCAmelCase							)

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			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:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    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:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    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:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F"""{full_name} has size {value.shape}, but {feature_extractor[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:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F"""{full_name} has size {value.shape}, but"""
									    F""" {feature_extractor[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(__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      SEWConfig()
			if is_finetuned:
						lowerCAmelCase_         =      model.wav_encoder.wav_model.cfg
			else:
						lowerCAmelCase_         =      model.cfg

			lowerCAmelCase_         =      fs_config.conv_bias
			lowerCAmelCase_         =      eval(fs_config.conv_feature_layers							)
			lowerCAmelCase_         =      [x[0] for x in conv_layers]
			lowerCAmelCase_         =      [x[1] for x in conv_layers]
			lowerCAmelCase_         =      [x[2] for x in conv_layers]
			lowerCAmelCase_         =      "gelu"
			lowerCAmelCase_         =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			lowerCAmelCase_         =      0.0
			lowerCAmelCase_         =      fs_config.activation_fn.name
			lowerCAmelCase_         =      fs_config.encoder_embed_dim
			lowerCAmelCase_         =      0.02
			lowerCAmelCase_         =      fs_config.encoder_ffn_embed_dim
			lowerCAmelCase_         =      1e-5
			lowerCAmelCase_         =      fs_config.encoder_layerdrop
			lowerCAmelCase_         =      fs_config.encoder_attention_heads
			lowerCAmelCase_         =      fs_config.conv_pos_groups
			lowerCAmelCase_         =      fs_config.conv_pos
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      fs_config.encoder_layers
			lowerCAmelCase_         =      fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						lowerCAmelCase_         =      model.cfg
						lowerCAmelCase_         =      fs_config.final_dropout
						lowerCAmelCase_         =      fs_config.layerdrop
			lowerCAmelCase_         =      fs_config.activation_dropout
			lowerCAmelCase_         =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			lowerCAmelCase_         =      fs_config.attention_dropout
			lowerCAmelCase_         =      fs_config.dropout_input
			lowerCAmelCase_         =      fs_config.dropout
			lowerCAmelCase_         =      fs_config.mask_channel_length
			lowerCAmelCase_         =      fs_config.mask_channel_prob
			lowerCAmelCase_         =      fs_config.mask_length
			lowerCAmelCase_         =      fs_config.mask_prob

			lowerCAmelCase_         =      "Wav2Vec2FeatureExtractor"
			lowerCAmelCase_         =      "Wav2Vec2CTCTokenizer"

			return config






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Dict=None       ,				__lowerCAmelCase    :    Union[str, Any]=None       ,				__lowerCAmelCase    :    int=True							):




			"""simple docstring"""


			if is_finetuned:
						lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,				arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
			else:
						lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

			if config_path is not None:
						lowerCAmelCase_         =      SEWConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      convert_config(model[0]       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      model[0].eval()

			lowerCAmelCase_         =      True if config.feat_extract_norm == "layer" else False
			lowerCAmelCase_         =      WavaVecaFeatureExtractor(
			    feature_size=1       ,				sampling_rate=16000       ,				padding_value=0       ,				do_normalize=__lowerCAmelCase       ,				return_attention_mask=__lowerCAmelCase       ,				)

			if is_finetuned:
						if dict_path:
									lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)

									# 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.pad_index
									lowerCAmelCase_         =      target_dict.bos_index
									lowerCAmelCase_         =      target_dict.eos_index
									lowerCAmelCase_         =      len(target_dict.symbols							)
									lowerCAmelCase_         =      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							)
									with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as vocab_handle:
												json.dump(target_dict.indices       ,				__lowerCAmelCase							)
									lowerCAmelCase_         =      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       ,				)
									lowerCAmelCase_         =      WavaVecaProcessor(feature_extractor=__lowerCAmelCase       ,				tokenizer=__lowerCAmelCase							)
									processor.save_pretrained(__lowerCAmelCase							)

						lowerCAmelCase_         =      SEWForCTC(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      SEWModel(__lowerCAmelCase							)
						feature_extractor.save_pretrained(__lowerCAmelCase							)

			recursively_load_weights(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)

			hf_model.save_pretrained(__lowerCAmelCase							)


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(
			    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
			)
			_A  =	parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''ibert'''




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=12							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=2							,							_UpperCamelCase="absolute"							,							_UpperCamelCase=False							,							_UpperCamelCase="none"							,							**_UpperCamelCase							,							)							-> List[str]:
							super().__init__(pad_token_id=_UpperCamelCase							,							bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      position_embedding_type
							lowerCAmelCase_         =      quant_mode
							lowerCAmelCase_         =      force_dequant





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_         =      {0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]	)



