





from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return [
										    {"col_1": 3, "col_2": "a"},
										    {"col_1": 2, "col_2": "b"},
										    {"col_1": 1, "col_2": "c"},
										    {"col_1": 0, "col_2": "d"},
										]





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
										return Dataset.from_dict(_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self._create_example_records()
										lowerCamelCase__       =       Dataset.from_list(_lowerCAmelCase	)
										self.assertListEqual(dset.column_names	,  ["col_1", "col_2"]	)
										for i, r in enumerate(_lowerCAmelCase	):
															self.assertDictEqual(_lowerCAmelCase	,  example_records[i]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self._create_example_records()
										lowerCamelCase__       =       Dataset.from_list(_lowerCAmelCase	)
										lowerCamelCase__       =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}	)
										self.assertEqual(dset.info	,  dset_from_dict.info	)





					def 					__magic_name__ ( self	):  # checks what happens with missing columns
										lowerCamelCase__       =       [{"col_1": 1}, {"col_2": "x"}]
										lowerCamelCase__       =       Dataset.from_list(_lowerCAmelCase	)
										self.assertDictEqual(dset[0]	,  {"col_1": 1}	)
										self.assertDictEqual(dset[1]	,  {"col_1": None}	)  # NB: first record is used for columns





					def 					__magic_name__ ( self	):  # checks if the type can be inferred from the second record
										lowerCamelCase__       =       [{"col_1": []}, {"col_1": [1, 2]}]
										lowerCamelCase__       =       Dataset.from_list(_lowerCAmelCase	)
										self.assertEqual(dset.info.features["col_1"]	,  Sequence(Value("int64"	)	)	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       Dataset.from_list([]	)
										self.assertEqual(len(_lowerCAmelCase	)	,  0	)
										self.assertListEqual(dset.column_names	,  []	)










import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "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",
}
A_												=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->Dict:
					for attribute in key.split("."):
										lowerCamelCase__       =       getattr(a,     a)

					if weight_type is not None:
										lowerCamelCase__       =       getattr(a,     a).shape
					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 == "running_mean":
										lowerCamelCase__       =       value
					elif weight_type == "running_var":
										lowerCamelCase__       =       value
					elif weight_type == "num_batches_tracked":
										lowerCamelCase__       =       value
					elif weight_type == "inv_freq":
										lowerCamelCase__       =       value
					else:
										lowerCamelCase__       =       value

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





def    __UpperCamelCase      (    a,     a,     a) ->Optional[int]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       fairseq_model.state_dict()

					lowerCamelCase__       =       hf_model.wavaveca_conformer.feature_extractor

					for name, value in fairseq_dict.items():
										lowerCamelCase__       =       False
										if "conv_layers" in name:
															load_conv_layer(
															    a,     a,     a,     a,     hf_model.config.feat_extract_norm == "group",     )
															lowerCamelCase__       =       True
										else:
															for key, mapped_key in MAPPING.items():
																				lowerCamelCase__       =       "wav2vec2_conformer." + 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(a)[0].split(".")[-2]
																														lowerCamelCase__       =       mapped_key.replace("*",     a)
																									if "pos_bias_u" in name:
																														lowerCamelCase__       =       None
																									elif "pos_bias_v" in name:
																														lowerCamelCase__       =       None
																									elif "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"
																									elif "running_mean" in name:
																														lowerCamelCase__       =       "running_mean"
																									elif "inv_freq" in name:
																														lowerCamelCase__       =       "inv_freq"
																									elif "running_var" in name:
																														lowerCamelCase__       =       "running_var"
																									elif "num_batches_tracked" in name:
																														lowerCamelCase__       =       "num_batches_tracked"
																									else:
																														lowerCamelCase__       =       None
																									set_recursively(a,     a,     a,     a,     a)
																				continue
										if not is_used:
															unused_weights.append(a)

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





def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->str:
					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(a)





@torch.no_grad()
def    __UpperCamelCase      (    a,     a,     a=None,     a=None,     a=True) ->Optional[Any]:
					if config_path is not None:
										lowerCamelCase__       =       WavaVecaConformerConfig.from_pretrained(a,     hidden_act="swish")
					else:
										lowerCamelCase__       =       WavaVecaConformerConfig()

					if "rope" in checkpoint_path:
										lowerCamelCase__       =       "rotary"

					if is_finetuned:
										if dict_path:
															lowerCamelCase__       =       Dictionary.load(a)

															# 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(a,     "vocab.json")
															if not os.path.isdir(a):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a))
																				return
															os.makedirs(a,     exist_ok=a)
															lowerCamelCase__       =       target_dict.indices

															# fairseq has the <pad> and <s> switched
															lowerCamelCase__       =       0
															lowerCamelCase__       =       1
															with open(a,     "w",     encoding="utf-8") as vocab_handle:
																				json.dump(a,     a)
															lowerCamelCase__       =       WavaVecaCTCTokenizer(
															    a,     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=a,     )
															lowerCamelCase__       =       True if config.feat_extract_norm == "layer" else False
															lowerCamelCase__       =       WavaVecaFeatureExtractor(
															    feature_size=1,     sampling_rate=16000,     padding_value=0,     do_normalize=a,     return_attention_mask=a,     )
															lowerCamelCase__       =       WavaVecaProcessor(feature_extractor=a,     tokenizer=a)
															processor.save_pretrained(a)

										lowerCamelCase__       =       WavaVecaConformerForCTC(a)
					else:
										lowerCamelCase__       =       WavaVecaConformerForPreTraining(a)

					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__       =       argparse.Namespace(task="audio_pretraining")
										lowerCamelCase__       =       fairseq.tasks.setup_task(a)

										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],     task=a)

					lowerCamelCase__       =       model[0].eval()

					recursively_load_weights(a,     a,     not is_finetuned)

					hf_wavavec.save_pretrained(a)


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"
						)
						A_												=     parser.parse_args()
						convert_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)

