
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						:						Dict					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Dict       ):




			"""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						:						Tuple					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]       ):




			"""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						:						Optional[int]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Optional[Any]       ):




			"""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						:						Union[str, Any]					,							__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						:						Union[str, Any]					,							__lowerCAmelCase						:						Optional[Any]=None					,							__lowerCAmelCase						:						Optional[int]=None					,							__lowerCAmelCase						:						Dict=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
			)





import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_A     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_A     =  10
_A     =  2_56






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			if len(__lowerCAmelCase       ) < MIN_NUM_TOKENS:
						return None
			lowerCAmelCase_      			=			MinHash(num_perm=__lowerCAmelCase       )
			for token in set(__lowerCAmelCase       ):
						min_hash.update(token.encode()       )
			return min_hash






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			return {t for t in NON_ALPHA.split(__lowerCAmelCase       ) if len(t.strip()       ) > 0}





class 			_lowerCAmelCase      :




				def __init__(      self       ,       *,
				    _UpperCamelCase = 0.85       ,       )       ->   Dict:
							lowerCAmelCase_      			=			duplication_jaccard_threshold
							lowerCAmelCase_      			=			NUM_PERM
							lowerCAmelCase_      			=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,       num_perm=self._num_perm  )

							lowerCAmelCase_      			=			defaultdict(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self._index.query(_UpperCamelCase  )
							if code_key in self._index.keys:
										print(f"""Duplicate key {code_key}"""  )
										return

							self._index.insert(_UpperCamelCase       ,       _UpperCamelCase  )
							if len(_UpperCamelCase  ) > 0:
										for base_duplicate in close_duplicates:
													if base_duplicate in self._duplicate_clusters:
																self._duplicate_clusters[base_duplicate].add(_UpperCamelCase  )
																break
										else:
													self._duplicate_clusters[close_duplicates[0]].add(_UpperCamelCase  )




				def   __a		(      self  )       ->   List[List[Dict]]:
							lowerCAmelCase_      			=			[]
							for base, duplicates in self._duplicate_clusters.items():
										lowerCAmelCase_      			=			[base] + list(_UpperCamelCase  )
										# reformat the cluster to be a list of dict
										lowerCAmelCase_      			=			[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
										duplicate_clusters.append(_UpperCamelCase  )
							return duplicate_clusters




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.get_duplicate_clusters()
							with open(_UpperCamelCase       ,       "w"  ) as f:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			get_min_hash([t for t in NON_ALPHA.split(data["content"]       ) if len(t.strip()       ) > 0]       )
			if min_hash is not None:
						return (index, data["repo_name"], data["path"]), min_hash






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]       ):




			"""simple docstring"""


			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash					,							ThreadedIterator(__lowerCAmelCase					,							max_queue_size=10000       )					,							chunksize=100					,							):
									if data is not None:
												yield data






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase       )

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase       )       )					,							max_queue_size=100       )       ):
						di.add(__lowerCAmelCase					,							__lowerCAmelCase       )

			# Returns a List[Cluster] where Cluster is List[str] with the filenames.
			return di.get_duplicate_clusters()






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A     =  None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for elementa in cluster:
						lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(__lowerCAmelCase					,							__lowerCAmelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									lowerCAmelCase_      			=			1
									extremes.append(__lowerCAmelCase       )
			return extremes






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




			"""simple docstring"""


			global _shared_dataset
			lowerCAmelCase_      			=			dataset
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			partial(_find_cluster_extremes_shared					,							jaccard_threshold=__lowerCAmelCase       )
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        __lowerCAmelCase					,							__lowerCAmelCase					,							)					,							total=len(__lowerCAmelCase       )					,							):
									extremes_list.append(__lowerCAmelCase       )
			return extremes_list






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float = 0.85       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			make_duplicate_clusters(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_      			=			{x["base_index"] for cluster in duplicate_clusters for x in cluster}
			lowerCAmelCase_      			=			{}
			lowerCAmelCase_      			=			find_extremes(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			for extremes in extremes_clusters:
						for element in extremes:
									lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			duplicate_indices - set(extreme_dict.keys()       )
			lowerCAmelCase_      			=			dataset.filter(lambda __lowerCAmelCase					,							__lowerCAmelCase       : idx not in remove_indices					,							with_indices=__lowerCAmelCase       )

			# update duplicate_clusters
			for cluster in duplicate_clusters:
						for element in cluster:
									lowerCAmelCase_      			=			element["base_index"] in extreme_dict
									if element["is_extreme"]:
												lowerCAmelCase_      			=			extreme_dict[element["base_index"]]["copies"]

			print(F"""Original dataset size: {len(__lowerCAmelCase       )}"""       )
			print(F"""Number of duplicate clusters: {len(__lowerCAmelCase       )}"""       )
			print(F"""Files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Unique files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Filtered dataset size: {len(__lowerCAmelCase       )}"""       )

			return ds_filter, duplicate_clusters



