






'''simple docstring'''




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowerCAmelCase__          =						NewType('DataClass', Any)
lowerCAmelCase__          =						NewType('DataClassType', Any)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		if isinstance(lowerCamelCase_     ,      lowerCamelCase_):
				return v
		if v.lower() in ("yes", "true", "t", "y", "1"):
				return True
		elif v.lower() in ("no", "false", "f", "n", "0"):
				return False
		else:
				raise ArgumentTypeError(
				    f'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).')







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Callable[[str], Any]:
		UpperCamelCase__				:					Any					=					{str(lowerCamelCase_): choice for choice in choices}
		return lambda lowerCamelCase_: str_to_choice.get(lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  *,
    lowerCamelCase_ = None     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = dataclasses.MISSING     ,      lowerCamelCase_ = dataclasses.MISSING     ,      lowerCamelCase_ = None     ,      **lowerCamelCase_     ,      )							->     dataclasses.Field:
		if metadata is None:
				# Important, don't use as default param in function signature because dict is mutable and shared across function calls
				UpperCamelCase__				:					int					=					{}
		if aliases is not None:
				UpperCamelCase__				:					Optional[Any]					=					aliases
		if help is not None:
				UpperCamelCase__				:					Optional[int]					=					help

		return dataclasses.field(metadata=lowerCamelCase_     ,      default=lowerCamelCase_     ,      default_factory=lowerCamelCase_     ,      **lowerCamelCase_)







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  42

	def __init__(      self   :						int  ,					UpperCAmelCase_   :						Union[DataClassType, Iterable[DataClassType]]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			# To make the default appear when using --help
			if "formatter_class" not in kwargs:
					UpperCamelCase__				:					List[str]					=					ArgumentDefaultsHelpFormatter
			super().__init__(**UpperCAmelCase_)
			if dataclasses.is_dataclass(UpperCAmelCase_):
					UpperCamelCase__				:					int					=					[dataclass_types]
			UpperCamelCase__				:					List[str]					=					list(UpperCAmelCase_)
			for dtype in self.dataclass_types:
					self._add_dataclass_arguments(UpperCAmelCase_)

	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						ArgumentParser  ,					UpperCAmelCase_   :						dataclasses.Field):
			UpperCamelCase__				:					int					=					F'--{field.name}'
			UpperCamelCase__				:					List[str]					=					field.metadata.copy()
			# field.metadata is not used at all by Data Classes,
			# it is provided as a third-party extension mechanism.
			if isinstance(field.type  ,					UpperCAmelCase_):
					raise RuntimeError(
					    'Unresolved type detected, which should have been done with the help of '
					    '`typing.get_type_hints` method by default')

			UpperCamelCase__				:					str					=					kwargs.pop('aliases'  ,					[])
			if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					UpperCamelCase__				:					Union[str, Any]					=					[aliases]

			UpperCamelCase__				:					Optional[int]					=					getattr(field.type  ,					'__origin__'  ,					field.type)
			if origin_type is Union or (hasattr(UpperCAmelCase_  ,					'UnionType') and isinstance(UpperCAmelCase_  ,					types.UnionType)):
					if str not in field.type.__args__ and (
					    len(field.type.__args__) != 2 or type(UpperCAmelCase_) not in field.type.__args__
					):
							raise ValueError(
							    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
							    ' the argument parser only supports one type per argument.'
							    F' Problem encountered in field \'{field.name}\'.')
					if type(UpperCAmelCase_) not in field.type.__args__:
							# filter `str` in Union
							UpperCamelCase__				:					Any					=					field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
							UpperCamelCase__				:					Dict					=					getattr(field.type  ,					'__origin__'  ,					field.type)
					elif bool not in field.type.__args__:
							# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
							UpperCamelCase__				:					List[Any]					=					(
							    field.type.__args__[0] if isinstance(UpperCAmelCase_  ,					field.type.__args__[1]) else field.type.__args__[1]
							)
							UpperCamelCase__				:					List[Any]					=					getattr(field.type  ,					'__origin__'  ,					field.type)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
			UpperCamelCase__				:					Any					=					{}
			if origin_type is Literal or (isinstance(field.type  ,					UpperCAmelCase_) and issubclass(field.type  ,					UpperCAmelCase_)):
					if origin_type is Literal:
							UpperCamelCase__				:					int					=					field.type.__args__
					else:
							UpperCamelCase__				:					List[Any]					=					[x.value for x in field.type]

					UpperCamelCase__				:					str					=					make_choice_type_function(kwargs['choices'])

					if field.default is not dataclasses.MISSING:
							UpperCamelCase__				:					Any					=					field.default
					else:
							UpperCamelCase__				:					Optional[Any]					=					True
			elif field.type is bool or field.type == Optional[bool]:
					# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
					# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
					UpperCamelCase__				:					Union[str, Any]					=					copy(UpperCAmelCase_)

					# Hack because type=bool in argparse does not behave as we want.
					UpperCamelCase__				:					Tuple					=					string_to_bool
					if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
							# Default value is False if we have no default when of type bool.
							UpperCamelCase__				:					List[Any]					=					False if field.default is dataclasses.MISSING else field.default
							# This is the value that will get picked if we don't include --field_name in any way
							UpperCamelCase__				:					List[str]					=					default
							# This tells argparse we accept 0 or 1 value after --field_name
							UpperCamelCase__				:					str					=					'?'
							# This is the value that will get picked if we do --field_name (without value)
							UpperCamelCase__				:					Union[str, Any]					=					True
			elif isclass(UpperCAmelCase_) and issubclass(UpperCAmelCase_  ,					UpperCAmelCase_):
					UpperCamelCase__				:					Dict					=					field.type.__args__[0]
					UpperCamelCase__				:					Optional[int]					=					'+'
					if field.default_factory is not dataclasses.MISSING:
							UpperCamelCase__				:					Any					=					field.default_factory()
					elif field.default is dataclasses.MISSING:
							UpperCamelCase__				:					List[Any]					=					True
			else:
					UpperCamelCase__				:					List[str]					=					field.type
					if field.default is not dataclasses.MISSING:
							UpperCamelCase__				:					Optional[Any]					=					field.default
					elif field.default_factory is not dataclasses.MISSING:
							UpperCamelCase__				:					Optional[Any]					=					field.default_factory()
					else:
							UpperCamelCase__				:					Union[str, Any]					=					True
			parser.add_argument(UpperCAmelCase_  ,					*UpperCAmelCase_  ,					**UpperCAmelCase_)

			# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
			# Order is important for arguments with the same destination!
			# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
			# here and we do not need those changes/additional keys.
			if field.default is True and (field.type is bool or field.type == Optional[bool]):
					UpperCamelCase__				:					Union[str, Any]					=					False
					parser.add_argument(F'--no_{field.name}'  ,					action='store_false'  ,					dest=field.name  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						DataClassType):
			if hasattr(UpperCAmelCase_  ,					'_argument_group_name'):
					UpperCamelCase__				:					Any					=					self.add_argument_group(dtype._argument_group_name)
			else:
					UpperCamelCase__				:					Union[str, Any]					=					self

			try:
					UpperCamelCase__				:					Dict[str, type]					=					get_type_hints(UpperCAmelCase_)
			except NameError:
					raise RuntimeError(
					    F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
					    'removing line of `from __future__ import annotations` which opts in Postponed '
					    'Evaluation of Annotations (PEP 563)')
			except TypeError as ex:
					# Remove this block when we drop Python 3.9 support
					if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCAmelCase_):
							UpperCamelCase__				:					str					=					'.'.join(map(UpperCAmelCase_  ,					sys.version_info[:3]))
							raise RuntimeError(
							    F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
							    'line of `from __future__ import annotations` which opts in union types as '
							    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
							    'support Python versions that lower than 3.10, you need to use '
							    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
							    '`X | None`.') from ex
					raise

			for field in dataclasses.fields(UpperCAmelCase_):
					if not field.init:
							continue
					UpperCamelCase__				:					int					=					type_hints[field.name]
					self._parse_dataclass_field(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Tuple=None  ,					UpperCAmelCase_   :						int=False  ,					UpperCAmelCase_   :						Dict=True  ,					UpperCAmelCase_   :						List[str]=None  ,					UpperCAmelCase_   :						List[str]=None  ,					):

			if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
					UpperCamelCase__				:					Optional[int]					=					[]

					if args_filename:
							args_files.append(Path(UpperCAmelCase_))
					elif look_for_args_file and len(sys.argv):
							args_files.append(Path(sys.argv[0]).with_suffix('.args'))

					# args files specified via command line flag should overwrite default args files so we add them last
					if args_file_flag:
							# Create special parser just to extract the args_file_flag values
							UpperCamelCase__				:					List[str]					=					ArgumentParser()
							args_file_parser.add_argument(UpperCAmelCase_  ,					type=UpperCAmelCase_  ,					action='append')

							# Use only remaining args for further parsing (remove the args_file_flag)
							UpperCamelCase__,	UpperCamelCase__				:					Optional[int]					=					args_file_parser.parse_known_args(args=UpperCAmelCase_)
							UpperCamelCase__				:					str					=					vars(UpperCAmelCase_).get(args_file_flag.lstrip('-')  ,					UpperCAmelCase_)

							if cmd_args_file_paths:
									args_files.extend([Path(UpperCAmelCase_) for p in cmd_args_file_paths])

					UpperCamelCase__				:					str					=					[]
					for args_file in args_files:
							if args_file.exists():
									file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
					UpperCamelCase__				:					List[Any]					=					file_args + args if args is not None else file_args + sys.argv[1:]
			UpperCamelCase__,	UpperCamelCase__				:					List[Any]					=					self.parse_known_args(args=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					[]
			for dtype in self.dataclass_types:
					UpperCamelCase__				:					Dict					=					{f.name for f in dataclasses.fields(UpperCAmelCase_) if f.init}
					UpperCamelCase__				:					List[Any]					=					{k: v for k, v in vars(UpperCAmelCase_).items() if k in keys}
					for k in keys:
							delattr(UpperCAmelCase_  ,					UpperCAmelCase_)
					UpperCamelCase__				:					List[Any]					=					dtype(**UpperCAmelCase_)
					outputs.append(UpperCAmelCase_)
			if len(namespace.__dict__) > 0:
					# additional namespace.
					outputs.append(UpperCAmelCase_)
			if return_remaining_strings:
					return (*outputs, remaining_args)
			else:
					if remaining_args:
							raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}')

					return (*outputs,)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Dict[str, Any]  ,					UpperCAmelCase_   :						bool = False):
			UpperCamelCase__				:					List[str]					=					set(args.keys())
			UpperCamelCase__				:					List[Any]					=					[]
			for dtype in self.dataclass_types:
					UpperCamelCase__				:					Union[str, Any]					=					{f.name for f in dataclasses.fields(UpperCAmelCase_) if f.init}
					UpperCamelCase__				:					List[str]					=					{k: v for k, v in args.items() if k in keys}
					unused_keys.difference_update(inputs.keys())
					UpperCamelCase__				:					List[str]					=					dtype(**UpperCAmelCase_)
					outputs.append(UpperCAmelCase_)
			if not allow_extra_keys and unused_keys:
					raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(UpperCAmelCase_)}')
			return tuple(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						bool = False):
			with open(Path(UpperCAmelCase_)  ,					encoding='utf-8') as open_json_file:
					UpperCamelCase__				:					Union[str, Any]					=					json.loads(open_json_file.read())
			UpperCamelCase__				:					List[str]					=					self.parse_dict(UpperCAmelCase_  ,					allow_extra_keys=UpperCAmelCase_)
			return tuple(UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						bool = False):
			UpperCamelCase__				:					Dict					=					self.parse_dict(yaml.safe_load(Path(UpperCAmelCase_).read_text())  ,					allow_extra_keys=UpperCAmelCase_)
			return tuple(UpperCAmelCase_)












'''simple docstring'''




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__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{
    '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__          =						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     str:
		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
								UpperCamelCase__				:					str					=					'lm_head'

				UpperCamelCase__				:					Optional[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_)

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

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					List[Any]					=					[]
		UpperCamelCase__				:					int					=					fairseq_model.state_dict()

		UpperCamelCase__				:					int					=					hf_model.unispeech.feature_extractor

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

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Dict					=					full_name.split('conv_layers.')[-1]
		UpperCamelCase__				:					List[Any]					=					name.split('.')
		UpperCamelCase__				:					Any					=					int(items[0])
		UpperCamelCase__				:					int					=					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.'
						)
						UpperCamelCase__				:					Tuple					=					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.'
						)
						UpperCamelCase__				:					int					=					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."
						)
						UpperCamelCase__				:					Optional[Any]					=					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.'
						)
						UpperCamelCase__				:					List[Any]					=					value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
		else:
				unused_weights.append(lowerCamelCase_)







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=True)							->     Tuple:
		if config_path is not None:
				UpperCamelCase__				:					Optional[Any]					=					UniSpeechConfig.from_pretrained(lowerCamelCase_)
		else:
				UpperCamelCase__				:					int					=					UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						UpperCamelCase__				:					Union[str, Any]					=					Dictionary.load_from_json(lowerCamelCase_)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCamelCase__				:					List[Any]					=					target_dict.pad_index
						UpperCamelCase__				:					Dict					=					target_dict.bos_index
						UpperCamelCase__				:					Union[str, Any]					=					target_dict.eos_index
						UpperCamelCase__				:					Tuple					=					len(target_dict.symbols)
						UpperCamelCase__				:					Dict					=					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_)
						UpperCamelCase__				:					Optional[int]					=					target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase__				:					Any					=					42
						UpperCamelCase__				:					List[str]					=					43
						with open(lowerCamelCase_     ,      'w'     ,      encoding='utf-8') as vocab_handle:
								json.dump(lowerCamelCase_     ,      lowerCamelCase_)
						UpperCamelCase__				:					Optional[int]					=					WavaVecaPhonemeCTCTokenizer(
						    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_     ,      )
						UpperCamelCase__				:					Optional[Any]					=					True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase__				:					Union[str, Any]					=					WavaVecaFeatureExtractor(
						    feature_size=1     ,      sampling_rate=16_000     ,      padding_value=0     ,      do_normalize=lowerCamelCase_     ,      return_attention_mask=lowerCamelCase_     ,      )
						UpperCamelCase__				:					Tuple					=					WavaVecaProcessor(feature_extractor=lowerCamelCase_     ,      tokenizer=lowerCamelCase_)
						processor.save_pretrained(lowerCamelCase_)

				UpperCamelCase__				:					Dict					=					UniSpeechForCTC(lowerCamelCase_)
		else:
				UpperCamelCase__				:					List[Any]					=					UniSpeechForPreTraining(lowerCamelCase_)

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

		UpperCamelCase__				:					int					=					model[0].eval()

		recursively_load_weights(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						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__          =						parser.parse_args()
		convert_unispeech_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)




