

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    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.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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):



	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       getattr(_a							,			_a)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       getattr(_a							,			_a).shape
	else:
		SCREAMING_SNAKE_CASE   :      Any				     =       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":
		SCREAMING_SNAKE_CASE   :      List[str]				     =       value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE   :      Tuple				     =       value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE   :      int				     =       value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE   :      List[str]				     =       value
	else:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value

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

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE   :      int				     =       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE   :      str				     =       False
		if "conv_layers" in name:
			load_conv_layer(
			    _a							,			_a							,			_a							,			_a							,			hf_model.config.feat_extract_norm == "group"							,			)
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
		else:
			for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE   :      Dict				     =       "hubert." + 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] and not is_finetuned):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       name.split(_a)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mapped_key.replace("*"							,			_a)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE   :      Dict				     =       "weight_g"
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "weight_v"
					elif "weight" in name:
						SCREAMING_SNAKE_CASE   :      Dict				     =       "weight"
					elif "bias" in name:
						SCREAMING_SNAKE_CASE   :      str				     =       "bias"
					else:
						SCREAMING_SNAKE_CASE   :      str				     =       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  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       full_name.split("conv_layers.")[-1]
	SCREAMING_SNAKE_CASE   :      int				     =       name.split(".")
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       int(items[0])
	SCREAMING_SNAKE_CASE   :      Any				     =       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."
			)
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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."
			)
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       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."
			)
			SCREAMING_SNAKE_CASE   :      List[str]				     =       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."
			)
			SCREAMING_SNAKE_CASE   :      str				     =       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  lowerCamelCase__ (					_a							,			_a							,			_a=None							,			_a=None							,			_a=True):



	if config_path is not None:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       HubertConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       HubertConfig()

	if is_finetuned:
		if dict_path:
			SCREAMING_SNAKE_CASE   :      Tuple				     =       Dictionary.load(_a)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			SCREAMING_SNAKE_CASE   :      Tuple				     =       target_dict.pad_index
			SCREAMING_SNAKE_CASE   :      List[str]				     =       target_dict.bos_index
			SCREAMING_SNAKE_CASE   :      Dict				     =       target_dict.eos_index
			SCREAMING_SNAKE_CASE   :      Tuple				     =       len(target_dict.symbols)
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       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)
			with open(_a							,			"w"							,			encoding="utf-8") as vocab_handle:
				json.dump(target_dict.indices							,			_a)
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       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							,			)
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True if config.feat_extract_norm == "layer" else False
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       WavaVecaFeatureExtractor(
			    feature_size=1							,			sampling_rate=16000							,			padding_value=0							,			do_normalize=_a							,			return_attention_mask=_a							,			)
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       WavaVecaProcessor(feature_extractor=_a							,			tokenizer=_a)
			processor.save_pretrained(_a)

		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       HubertForCTC(_a)
	else:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       HubertModel(_a)

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

	SCREAMING_SNAKE_CASE   :      List[str]				     =       model[0].eval()

	recursively_load_weights(_a							,			_a							,			_a)

	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_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_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_						     =    256

def  lowerCamelCase__ (					_a):
	if len(_a) < MIN_NUM_TOKENS:
		return None
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MinHash(num_perm=_a)
	for token in set(_a):
		min_hash.update(token.encode())
	return min_hash

def  lowerCamelCase__ (					_a):
	return {t for t in NON_ALPHA.split(_a) if len(t.strip()) > 0}






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      *,
			    a							: float = 0.85      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       duplication_jaccard_threshold
				SCREAMING_SNAKE_CASE   :      Any				     =       NUM_PERM
				SCREAMING_SNAKE_CASE   :      int				     =       MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       defaultdict(a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: MinHash	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._index.query(a	)
				if code_key in self._index.keys:
					print(F"Duplicate key {code_key}"	)
					return

				self._index.insert(a      ,      a	)
				if len(a	) > 0:
					for base_duplicate in close_duplicates:
						if base_duplicate in self._duplicate_clusters:
							self._duplicate_clusters[base_duplicate].add(a	)
							break
					else:
						self._duplicate_clusters[close_duplicates[0]].add(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[List[Dict]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for base, duplicates in self._duplicate_clusters.items():
					SCREAMING_SNAKE_CASE   :      int				     =       [base] + list(a	)
					# reformat the cluster to be a list of dict
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
					duplicate_clusters.append(a	)
				return duplicate_clusters






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: List[str]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_duplicate_clusters()
				with open(a      ,      "w"	) as f:
					json.dump(a      ,      a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       element
	SCREAMING_SNAKE_CASE   :      Any				     =       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__ (					_a):
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,			ThreadedIterator(_a							,			max_queue_size=10000)							,			chunksize=100							,			):
			if data is not None:
				yield data

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DuplicationIndex(duplication_jaccard_threshold=_a)

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

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       get_tokens(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       get_tokens(_a)
	return len(tokensa & tokensa) / len(tokensa | tokensa)


a_						     =    None

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	for elementa in cluster:
		SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_a							,			_a) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1
			extremes.append(_a)
	return extremes

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	global _shared_dataset
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dataset
	SCREAMING_SNAKE_CASE   :      str				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       partial(_find_cluster_extremes_shared							,			jaccard_threshold=_a)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        _a							,			_a							,			)							,			total=len(_a)							,			):
			extremes_list.append(_a)
	return extremes_list


def  lowerCamelCase__ (					_a							,			_a = 0.85):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       make_duplicate_clusters(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       find_extremes(_a							,			_a							,			_a)
	for extremes in extremes_clusters:
		for element in extremes:
			SCREAMING_SNAKE_CASE   :      str				     =       element
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       duplicate_indices - set(extreme_dict.keys())
	SCREAMING_SNAKE_CASE   :      str				     =       dataset.filter(lambda _a							,			_a: idx not in remove_indices							,			with_indices=_a)

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			SCREAMING_SNAKE_CASE   :      Dict				     =       element["base_index"] in extreme_dict
			if element["is_extreme"]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       extreme_dict[element["base_index"]]["copies"]

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

	return ds_filter, duplicate_clusters