


"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
__snake_case         =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					{}
    with open(lowercase		,							"r"				) as file:
        for line_number, line in enumerate(lowercase				):
            snake_case					:   str         =					line.strip()
            if line:
                snake_case					:   Optional[int]         =					line.split()
                snake_case					:   Dict         =					line_number
                snake_case					:   Any         =					words[0]
                snake_case					:   str         =					value
    return result







def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						int		,							lowercase						:						Union[str, Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						str				)			->		Union[str, Any]:


    """simple docstring"""





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

    snake_case					:   Dict         =					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowercase				):
            snake_case					:   int         =					PARAM_MAPPING[full_name.split("."				)[-1]]
            snake_case					:   Optional[int]         =					"param"

    if weight_type is not None and weight_type != "param":
        snake_case					:   List[str]         =					getattr(lowercase		,							lowercase				).shape
    elif weight_type is not None and weight_type == "param":
        snake_case					:   Any         =					hf_pointer
        for attribute in hf_param_name.split("."				):
            snake_case					:   int         =					getattr(lowercase		,							lowercase				)
        snake_case					:   Tuple         =					shape_pointer.shape

        # let's reduce dimension
        snake_case					:   Dict         =					value[0]
    else:
        snake_case					:   int         =					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":
        snake_case					:   Tuple         =					value
    elif weight_type == "weight_g":
        snake_case					:   List[Any]         =					value
    elif weight_type == "weight_v":
        snake_case					:   List[str]         =					value
    elif weight_type == "bias":
        snake_case					:   Any         =					value
    elif weight_type == "param":
        for attribute in hf_param_name.split("."				):
            snake_case					:   Dict         =					getattr(lowercase		,							lowercase				)
        snake_case					:   Optional[Any]         =					value
    else:
        snake_case					:   Any         =					value

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







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						int		,							lowercase						:						Union[str, Any]		,							lowercase						:						Optional[Any]				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowercase				):
            snake_case					:   Dict         =					PARAM_MAPPING[full_name.split("."				)[-1]]
            snake_case					:   Optional[int]         =					"param"

    if weight_type is not None and weight_type != "param":
        snake_case					:   List[str]         =					".".join([key, weight_type]				)
    elif weight_type is not None and weight_type == "param":
        snake_case					:   List[Any]         =					".".join([key, hf_param_name]				)
    else:
        snake_case					:   Union[str, Any]         =					key

    snake_case					:   str         =					value if "lm_head" in full_key else value[0]


__snake_case         =					{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						str		,							lowercase						:						Optional[int]=None		,							lowercase						:						List[str]=None				)			->		str:


    """simple docstring"""





    snake_case					:   Any         =					False
    for key, mapped_key in MAPPING.items():
        snake_case					:   Optional[int]         =					"wav2vec2." + 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]:
            snake_case					:   Optional[Any]         =					True
            if "*" in mapped_key:
                snake_case					:   str         =					name.split(lowercase				)[0].split("."				)[-2]
                snake_case					:   int         =					mapped_key.replace("*"		,							lowercase				)
            if "weight_g" in name:
                snake_case					:   Optional[int]         =					"weight_g"
            elif "weight_v" in name:
                snake_case					:   Tuple         =					"weight_v"
            elif "bias" in name:
                snake_case					:   Union[str, Any]         =					"bias"
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                snake_case					:   Optional[int]         =					"weight"
            else:
                snake_case					:   Union[str, Any]         =					None
            if hf_dict is not None:
                rename_dict(lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)
            else:
                set_recursively(lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)
            return is_used
    return is_used







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Optional[int]				)			->		Dict:


    """simple docstring"""





    snake_case					:   Optional[int]         =					[]
    snake_case					:   Union[str, Any]         =					fairseq_model.state_dict()

    snake_case					:   int         =					hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case					:   int         =					False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase		,							lowercase		,							lowercase		,							lowercase		,							hf_model.config.feat_extract_norm == "group"		,							)
            snake_case					:   Union[str, Any]         =					True
        else:
            snake_case					:   List[str]         =					load_wavaveca_layer(lowercase		,							lowercase		,							lowercase				)
        if not is_used:
            unused_weights.append(lowercase				)

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







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Tuple		,							lowercase						:						int		,							lowercase						:						Tuple		,							lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





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







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						List[Any]		,							lowercase						:						Union[str, Any]=None		,							lowercase						:						Any=None		,							lowercase						:						Dict=True		,							lowercase						:						str=False				)			->		Union[str, Any]:


    """simple docstring"""





    if config_path is not None:
        snake_case					:   List[Any]         =					WavaVecaConfig.from_pretrained(lowercase				)
    else:
        snake_case					:   str         =					WavaVecaConfig()

    if is_seq_class:
        snake_case					:   Union[str, Any]         =					read_txt_into_dict(lowercase				)
        snake_case					:   Dict         =					idalabel
        snake_case					:   int         =					WavaVecaForSequenceClassification(lowercase				)
        snake_case					:   Optional[Any]         =					WavaVecaFeatureExtractor(
            feature_size=1		,							sampling_rate=1_6000		,							padding_value=0		,							do_normalize=lowercase		,							return_attention_mask=lowercase		,							)
        feature_extractor.save_pretrained(lowercase				)

    elif is_finetuned:
        if dict_path:
            snake_case					:   List[str]         =					Dictionary.load(lowercase				)

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

            # fairseq has the <pad> and <s> switched
            snake_case					:   Dict         =					0
            snake_case					:   str         =					1
            with open(lowercase		,							"w"		,							encoding="utf-8"				) as vocab_handle:
                json.dump(lowercase		,							lowercase				)
            snake_case					:   List[str]         =					WavaVecaCTCTokenizer(
                lowercase		,							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=lowercase		,							)
            snake_case					:   Dict         =					True if config.feat_extract_norm == "layer" else False
            snake_case					:   List[Any]         =					WavaVecaFeatureExtractor(
                feature_size=1		,							sampling_rate=1_6000		,							padding_value=0		,							do_normalize=lowercase		,							return_attention_mask=lowercase		,							)
            snake_case					:   Tuple         =					WavaVecaProcessor(feature_extractor=lowercase		,							tokenizer=lowercase				)
            processor.save_pretrained(lowercase				)

        snake_case					:   Optional[int]         =					WavaVecaForCTC(lowercase				)
    else:
        snake_case					:   Optional[int]         =					WavaVecaForPreTraining(lowercase				)

    if is_finetuned or is_seq_class:
        snake_case      ,snake_case      ,snake_case					:   List[str]         =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,							arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
    else:
        snake_case					:   Optional[Any]         =					argparse.Namespace(task="audio_pretraining"				)
        snake_case					:   List[str]         =					fairseq.tasks.setup_task(lowercase				)

        snake_case      ,snake_case      ,snake_case					:   int         =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,							task=lowercase				)

    snake_case					:   Optional[Any]         =					model[0].eval()

    recursively_load_weights(lowercase		,							lowercase		,							not is_finetuned				)

    hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
  __snake_case         =					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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  __snake_case         =					parser.parse_args()

  __snake_case         =					not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )









"""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


__snake_case         =					NewType("""DataClass""", Any)
__snake_case         =					NewType("""DataClassType""", Any)







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		Tuple:


    """simple docstring"""





    if isinstance(lowercase		,							lowercase				):
        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 						__lowerCAmelCase		(		lowercase						:						list				)			->		Callable[[str], Any]:


    """simple docstring"""





    snake_case					:   Any         =					{str(lowercase				): choice for choice in choices}
    return lambda lowercase				: str_to_choice.get(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		*,
    lowercase						:						Union[str, List[str]] = None		,							lowercase						:						str = None		,							lowercase						:						Any = dataclasses.MISSING		,							lowercase						:						Callable[[], Any] = dataclasses.MISSING		,							lowercase						:						dict = None		,							**lowercase						:						Tuple		,							)			->		dataclasses.Field:


    """simple docstring"""





    if metadata is None:
        # Important, don't use as default param in function signature because dict is mutable and shared across function calls
        snake_case					:   Any         =					{}
    if aliases is not None:
        snake_case					:   List[str]         =					aliases
    if help is not None:
        snake_case					:   Union[str, Any]         =					help

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







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Iterable[DataClassType]







   def __init__(			self		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       if "formatter_class" not in kwargs:
           snake_case					:   List[Any]         =					ArgumentDefaultsHelpFormatter
       super().__init__(**UpperCamelCase__		)
       if dataclasses.is_dataclass(UpperCamelCase__		):
           snake_case					:   str         =					[dataclass_types]
       snake_case					:   Any         =					list(UpperCamelCase__		)
       for dtype in self.dataclass_types:
           self._add_dataclass_arguments(UpperCamelCase__		)







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       snake_case					:   Tuple         =					F'--{field.name}'
       snake_case					:   Optional[Any]         =					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"		)

       snake_case					:   int         =					kwargs.pop("aliases"		,			[]		)
       if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Tuple         =					[aliases]

       snake_case					:   Tuple         =					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
               snake_case					:   Union[str, Any]         =					field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
               snake_case					:   str         =					getattr(field.type		,			"__origin__"		,			field.type		)
           elif bool not in field.type.__args__:
               # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
               snake_case					:   List[Any]         =					(
                   field.type.__args__[0] if isinstance(UpperCamelCase__		,			field.type.__args__[1]		) else field.type.__args__[1]
               )
               snake_case					:   Tuple         =					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)
       snake_case					:   Optional[int]         =					{}
       if origin_type is Literal or (isinstance(field.type		,			UpperCamelCase__		) and issubclass(field.type		,			UpperCamelCase__		)):
           if origin_type is Literal:
               snake_case					:   Any         =					field.type.__args__
           else:
               snake_case					:   Dict         =					[x.value for x in field.type]

           snake_case					:   List[str]         =					make_choice_type_function(kwargs["choices"]		)

           if field.default is not dataclasses.MISSING:
               snake_case					:   Any         =					field.default
           else:
               snake_case					:   List[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
           snake_case					:   Optional[Any]         =					copy(UpperCamelCase__		)

           # Hack because type=bool in argparse does not behave as we want.
           snake_case					:   Optional[Any]         =					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.
               snake_case					:   Union[str, 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
               snake_case					:   Any         =					default
               # This tells argparse we accept 0 or 1 value after --field_name
               snake_case					:   Optional[int]         =					"?"
               # This is the value that will get picked if we do --field_name (without value)
               snake_case					:   Any         =					True
       elif isclass(UpperCamelCase__		) and issubclass(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   List[Any]         =					field.type.__args__[0]
           snake_case					:   Tuple         =					"+"
           if field.default_factory is not dataclasses.MISSING:
               snake_case					:   Union[str, Any]         =					field.default_factory()
           elif field.default is dataclasses.MISSING:
               snake_case					:   Dict         =					True
       else:
           snake_case					:   List[str]         =					field.type
           if field.default is not dataclasses.MISSING:
               snake_case					:   List[Any]         =					field.default
           elif field.default_factory is not dataclasses.MISSING:
               snake_case					:   Optional[Any]         =					field.default_factory()
           else:
               snake_case					:   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]):
           snake_case					:   Optional[Any]         =					False
           parser.add_argument(F'--no_{field.name}'		,			action="store_false"		,			dest=field.name		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       if hasattr(UpperCamelCase__		,			"_argument_group_name"		):
           snake_case					:   Optional[Any]         =					self.add_argument_group(dtype._argument_group_name		)
       else:
           snake_case					:   int         =					self

       try:
           snake_case					:   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__		):
               snake_case					:   Dict         =					".".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
           snake_case					:   Optional[int]         =					type_hints[field.name]
           self._parse_dataclass_field(UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
           snake_case					:   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
               snake_case					:   str         =					ArgumentParser()
               args_file_parser.add_argument(UpperCamelCase__		,			type=UpperCamelCase__		,			action="append"		)

               # Use only remaining args for further parsing (remove the args_file_flag)
               snake_case      ,snake_case					:   Dict         =					args_file_parser.parse_known_args(args=UpperCamelCase__		)
               snake_case					:   int         =					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]		)

           snake_case					:   Optional[Any]         =					[]
           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
           snake_case					:   Dict         =					file_args + args if args is not None else file_args + sys.argv[1:]
       snake_case      ,snake_case					:   Any         =					self.parse_known_args(args=UpperCamelCase__		)
       snake_case					:   List[str]         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Optional[int]         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Optional[int]         =					{k: v for k, v in vars(UpperCamelCase__		).items() if k in keys}
           for k in keys:
               delattr(UpperCamelCase__		,			UpperCamelCase__		)
           snake_case					:   List[str]         =					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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   int         =					set(args.keys()		)
       snake_case					:   int         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Tuple         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Dict         =					{k: v for k, v in args.items() if k in keys}
           unused_keys.difference_update(inputs.keys()		)
           snake_case					:   Optional[Any]         =					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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       with open(Path(UpperCamelCase__		)		,			encoding="utf-8"		) as open_json_file:
           snake_case					:   Optional[Any]         =					json.loads(open_json_file.read()		)
       snake_case					:   int         =					self.parse_dict(UpperCamelCase__		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.parse_dict(yaml.safe_load(Path(UpperCamelCase__		).read_text()		)		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)





