import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
_lowerCamelCase   :				Any     			=						[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Optional[Any]					):
					for attribute in key.split("."					):
										if is_finetuned:
															if attribute in ["quantizer", "project_q", "project_hid"]:
																				# those layers are only relevant for pretraining and should be dropped
																				return

															if attribute == "ctc_proj":
																				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																				SCREAMING_SNAKE_CASE           =    "lm_head"

										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					)

					if weight_type is not None:
										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					).shape
					else:
										SCREAMING_SNAKE_CASE           =    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           =    value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE           =    value
					else:
										SCREAMING_SNAKE_CASE           =    value

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE           =    hf_model.unispeech.feature_extractor

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE           =    False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	hf_model.config.feat_extract_norm == "group" ,	)
															SCREAMING_SNAKE_CASE           =    True
										else:
															for key, mapped_key in MAPPING.items():
																				SCREAMING_SNAKE_CASE           =    "unispeech." + 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]:
																									SCREAMING_SNAKE_CASE           =    True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE           =    name.split(UpperCAmelCase__					)[0].split("."					)[-2]
																														SCREAMING_SNAKE_CASE           =    mapped_key.replace("*" ,	UpperCAmelCase__					)
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_g"
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_v"
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE           =    "bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														SCREAMING_SNAKE_CASE           =    "weight"
																									else:
																														SCREAMING_SNAKE_CASE           =    None
																									set_recursively(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
																				continue
										if not is_used:
															unused_weights.append(UpperCAmelCase__					)

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    full_name.split("conv_layers."					)[-1]
					SCREAMING_SNAKE_CASE           =    name.split("."					)
					SCREAMING_SNAKE_CASE           =    int(items[0]					)
					SCREAMING_SNAKE_CASE           =    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           =    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           =    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           =    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           =    value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
					else:
										unused_weights.append(UpperCAmelCase__					)





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							int=None ,	UpperCAmelCase__       :							List[str]=None ,	UpperCAmelCase__       :							str=True					):
					if config_path is not None:
										SCREAMING_SNAKE_CASE           =    UniSpeechConfig.from_pretrained(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															SCREAMING_SNAKE_CASE           =    Dictionary.load_from_json(UpperCAmelCase__					)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															SCREAMING_SNAKE_CASE           =    target_dict.pad_index
															SCREAMING_SNAKE_CASE           =    target_dict.bos_index
															SCREAMING_SNAKE_CASE           =    target_dict.eos_index
															SCREAMING_SNAKE_CASE           =    len(target_dict.symbols					)
															SCREAMING_SNAKE_CASE           =    os.path.join(UpperCAmelCase__ ,	"vocab.json"					)
															if not os.path.isdir(UpperCAmelCase__					):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase__					)					)
																				return
															os.makedirs(UpperCAmelCase__ ,	exist_ok=UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    target_dict.indices

															# fairseq has the <pad> and <s> switched
															SCREAMING_SNAKE_CASE           =    4_2
															SCREAMING_SNAKE_CASE           =    4_3
															with open(UpperCAmelCase__ ,	"w" ,	encoding="utf-8"					) as vocab_handle:
																				json.dump(UpperCAmelCase__ ,	UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    WavaVecaPhonemeCTCTokenizer(
															    UpperCAmelCase__ ,	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=UpperCAmelCase__ ,	)
															SCREAMING_SNAKE_CASE           =    True if config.feat_extract_norm == "layer" else False
															SCREAMING_SNAKE_CASE           =    WavaVecaFeatureExtractor(
															    feature_size=1 ,	sampling_rate=1_6_0_0_0 ,	padding_value=0 ,	do_normalize=UpperCAmelCase__ ,	return_attention_mask=UpperCAmelCase__ ,	)
															SCREAMING_SNAKE_CASE           =    WavaVecaProcessor(feature_extractor=UpperCAmelCase__ ,	tokenizer=UpperCAmelCase__					)
															processor.save_pretrained(UpperCAmelCase__					)

										SCREAMING_SNAKE_CASE           =    UniSpeechForCTC(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechForPreTraining(UpperCAmelCase__					)

					if is_finetuned:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path] ,	arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					), "w2v_path": checkpoint_path}					)
					else:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

					SCREAMING_SNAKE_CASE           =    model[0].eval()

					recursively_load_weights(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					hf_unispeech.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
							_lowerCamelCase   :				Any     			=						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'''
							)
							_lowerCamelCase   :				Optional[int]     			=						parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




