
import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__snake_case					:							int           =							logging.getLogger(__name__)

__snake_case					:							int           =							int(10_000)  # Hardcoded max length to avoid infinite loop

__snake_case					:							List[Any]           =							{
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
__snake_case					:							int           =							'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			np.random.seed(args.seed							)
			torch.manual_seed(args.seed							)
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed							)




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			if args.temperature > 0.7:
						logger.info('CTRL typically works better with lower temperatures (and lower top_k).'							)

			UpperCAmelCase__		:Any 		=    tokenizer.encode(UpperCamelCase__     ,						add_special_tokens=UpperCamelCase__							)
			if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()							):
						logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'							)
			return prompt_text




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    hasattr(model.config     ,						'use_lang_emb'							) and model.config.use_lang_emb
			if hasattr(model.config     ,						'lang2id'							) and use_lang_emb:
						UpperCAmelCase__		:Optional[Any] 		=    model.config.langaid.keys()
						if args.xlm_language in available_languages:
									UpperCAmelCase__		:Dict 		=    args.xlm_language
						else:
									UpperCAmelCase__		:Optional[int] 		=    None
									while language not in available_languages:
												UpperCAmelCase__		:str 		=    input('Using XLM. Select language in ' + str(list(UpperCamelCase__							)							) + ' >>> '							)

						UpperCAmelCase__		:Any 		=    model.config.langaid[language]
						# kwargs["language"] = tokenizer.lang2id[language]

			# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
			# XLM masked-language modeling (MLM) models need masked token
			# is_xlm_mlm = "mlm" in args.model_name_or_path
			# if is_xlm_mlm:
			#     kwargs["mask_token_id"] = tokenizer.mask_token_id

			return prompt_text




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			UpperCAmelCase__		:List[Any] 		=    prefix + prompt_text
			return prompt_text




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:str 		=    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			UpperCAmelCase__		:List[str] 		=    prefix + prompt_text
			return prompt_text


__snake_case					:							Any           =							{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			if length < 0 and max_sequence_length > 0:
						UpperCAmelCase__		:Any 		=    max_sequence_length
			elif 0 < max_sequence_length < length:
						UpperCAmelCase__		:Union[str, Any] 		=    max_sequence_length  # No generation bigger than model size
			elif length < 0:
						UpperCAmelCase__		:str 		=    MAX_LENGTH  # avoid infinite loop
			return length




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    None
			if hasattr(UpperCamelCase__     ,						'hidden_size'							):
						UpperCAmelCase__		:List[str] 		=    model_config.hidden_size
			elif hasattr(UpperCamelCase__     ,						'n_embed'							):
						UpperCAmelCase__		:int 		=    model_config.n_embed
			elif hasattr(UpperCamelCase__     ,						'n_embd'							):
						UpperCAmelCase__		:Any 		=    model_config.n_embd

			UpperCAmelCase__		:Union[str, Any] 		=    None
			if hasattr(UpperCamelCase__     ,						'num_attention_heads'							):
						UpperCAmelCase__		:List[str] 		=    model_config.num_attention_heads
			elif hasattr(UpperCamelCase__     ,						'n_head'							):
						UpperCAmelCase__		:str 		=    model_config.n_head

			if embedding_size is None or num_head is None or num_head == 0:
						raise ValueError('Check the model config'							)

			UpperCAmelCase__		:int 		=    int(embedding_size / num_head							)
			if hasattr(UpperCamelCase__     ,						'n_layer'							):
						UpperCAmelCase__		:Any 		=    model_config.n_layer
			elif hasattr(UpperCamelCase__     ,						'num_hidden_layers'							):
						UpperCAmelCase__		:List[str] 		=    model_config.num_hidden_layers
			else:
						raise ValueError('Number of hidden layers couldn\'t be determined from the model config'							)

			return num_layer, num_head, num_embedding_size_per_head




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    sparse_model_config(model.config							)
			if model.config.model_type == "bloom":
						UpperCAmelCase__		:Optional[int] 		=    tuple(
						    (
						        torch.empty(int(num_attention_heads * batch_size							)     ,						UpperCamelCase__     ,						UpperCamelCase__							)
						        .to(model.dtype							)
						        .to(model.device							),
						        torch.empty(int(num_attention_heads * batch_size							)     ,						UpperCamelCase__     ,						UpperCamelCase__							)
						        .to(model.dtype							)
						        .to(model.device							),
						    )
						    for _ in range(UpperCamelCase__							)							)
			else:
						UpperCAmelCase__		:str 		=    tuple(
						    (
						        torch.empty(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__							)
						        .to(model.dtype							)
						        .to(model.device							),
						        torch.empty(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__							)
						        .to(model.dtype							)
						        .to(model.device							),
						    )
						    for _ in range(UpperCamelCase__							)							)
			return past_key_values




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    len(UpperCamelCase__							)
			UpperCAmelCase__		:Optional[int] 		=    tokenizer.batch_encode_plus(UpperCamelCase__     ,						return_tensors='pt'							)
			UpperCAmelCase__		:Optional[Any] 		=    dummy_input.to(model.device							)
			if model.config.use_cache:
						UpperCAmelCase__		:Optional[int] 		=    generate_past_key_values(UpperCamelCase__     ,						UpperCamelCase__     ,						1							)
			UpperCAmelCase__		:Optional[int] 		=    torch.cat(
			    [
			        torch.zeros(dummy_input['attention_mask'].shape[0]     ,						1							)
			        .to(dummy_input['attention_mask'].dtype							)
			        .to(model.device							),
			        dummy_input['attention_mask'],
			    ]     ,						-1     ,						)
			return dummy_input


class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    Union[str, Any]												=			("""_optimized""", """_default""")


						def __init__(  self  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:Dict 		=    optimized
									UpperCAmelCase__		:str 		=    default


						def __call__(  self  , *A  , **A	)  ->List[Any]:
									if kwargs["past_key_values"] is None and self._default.config.use_cache:
												UpperCAmelCase__		:Any 		=    generate_past_key_values(self._default  , kwargs['input_ids'].shape[0]  , 0	)
									kwargs.pop('position_ids'  , lowerCAmelCase_	)
									for k in list(kwargs.keys()	):
												if kwargs[k] is None or isinstance(kwargs[k]  , lowerCAmelCase_	):
															kwargs.pop(lowerCAmelCase_	)
									UpperCAmelCase__		:Tuple 		=    self._optimized(**lowerCAmelCase_	)
									UpperCAmelCase__		:Any 		=    outputs[0]
									UpperCAmelCase__		:Optional[int] 		=    outputs[1]
									UpperCAmelCase__		:Dict 		=    CausalLMOutputWithPast(
									    loss=lowerCAmelCase_  , logits=lowerCAmelCase_  , past_key_values=lowerCAmelCase_  , hidden_states=lowerCAmelCase_  , attentions=lowerCAmelCase_  , )
									return fixed_output


						def __getattr__(  self  , A	)  ->Optional[Any]:
									return getattr(self._default  , lowerCAmelCase_	)


						def    A__       (  self  , A  , A=None  , A=None  , A=None  , **A	)  ->Union[str, Any]:
									return self._default.prepare_inputs_for_generation(
									    lowerCAmelCase_  , past_key_values=lowerCAmelCase_  , inputs_embeds=lowerCAmelCase_  , use_cache=lowerCAmelCase_  , **lowerCAmelCase_	)


						def    A__       (  self  , A  , A	)  ->Tuple[Tuple[torch.Tensor]]:
									return self._default._reorder_cache(lowerCAmelCase_  , lowerCAmelCase_	)




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[Any] 		=    argparse.ArgumentParser()
			parser.add_argument(
			    '--model_type'     ,						default=UpperCamelCase__     ,						type=UpperCamelCase__     ,						required=UpperCamelCase__     ,						help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)     ,						)
			parser.add_argument(
			    '--model_name_or_path'     ,						default=UpperCamelCase__     ,						type=UpperCamelCase__     ,						required=UpperCamelCase__     ,						help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)     ,						)

			parser.add_argument('--prompt'     ,						type=UpperCamelCase__     ,						default=''							)
			parser.add_argument('--length'     ,						type=UpperCamelCase__     ,						default=20							)
			parser.add_argument('--stop_token'     ,						type=UpperCamelCase__     ,						default=UpperCamelCase__     ,						help='Token at which text generation is stopped'							)

			parser.add_argument(
			    '--temperature'     ,						type=UpperCamelCase__     ,						default=1.0     ,						help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,						)
			parser.add_argument(
			    '--repetition_penalty'     ,						type=UpperCamelCase__     ,						default=1.0     ,						help='primarily useful for CTRL model; in that case, use 1.2'							)
			parser.add_argument('--k'     ,						type=UpperCamelCase__     ,						default=0							)
			parser.add_argument('--p'     ,						type=UpperCamelCase__     ,						default=0.9							)

			parser.add_argument('--prefix'     ,						type=UpperCamelCase__     ,						default=''     ,						help='Text added prior to input.'							)
			parser.add_argument('--padding_text'     ,						type=UpperCamelCase__     ,						default=''     ,						help='Deprecated, the use of `--prefix` is preferred.'							)
			parser.add_argument('--xlm_language'     ,						type=UpperCamelCase__     ,						default=''     ,						help='Optional language when used with the XLM model.'							)

			parser.add_argument('--seed'     ,						type=UpperCamelCase__     ,						default=42     ,						help='random seed for initialization'							)
			parser.add_argument('--no_cuda'     ,						action='store_true'     ,						help='Avoid using CUDA when available'							)
			parser.add_argument('--num_return_sequences'     ,						type=UpperCamelCase__     ,						default=1     ,						help='The number of samples to generate.'							)
			parser.add_argument(
			    '--fp16'     ,						action='store_true'     ,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,						)
			parser.add_argument('--jit'     ,						action='store_true'     ,						help='Whether or not to use jit trace to accelerate inference'							)
			UpperCAmelCase__		:Any 		=    parser.parse_args()

			UpperCAmelCase__		:Optional[int] 		=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
			UpperCAmelCase__		:Optional[Any] 		=    0 if args.no_cuda else torch.cuda.device_count()

			logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""							)

			set_seed(UpperCamelCase__							)

			# Initialize the model and tokenizer
			try:
						UpperCAmelCase__		:Union[str, Any] 		=    args.model_type.lower()
						UpperCAmelCase__		,       UpperCAmelCase__		:Dict 		=    MODEL_CLASSES[args.model_type]
			except KeyError:
						raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'							)

			UpperCAmelCase__		:Tuple 		=    tokenizer_class.from_pretrained(args.model_name_or_path							)
			if tokenizer.pad_token is None:
						UpperCAmelCase__		:Optional[Any] 		=    tokenizer.eos_token
			UpperCAmelCase__		:Union[str, Any] 		=    model_class.from_pretrained(args.model_name_or_path							)
			model.to(args.device							)

			if args.fpaa:
						model.half()
			UpperCAmelCase__		:Dict 		=    getattr(model.config     ,						'max_position_embeddings'     ,						0							)
			UpperCAmelCase__		:Union[str, Any] 		=    adjust_length_to_model(args.length     ,						max_sequence_length=UpperCamelCase__							)
			logger.info(UpperCamelCase__							)

			UpperCAmelCase__		:List[str] 		=    args.prompt if args.prompt else input('Model prompt >>> '							)

			# Different models need different input formatting and/or extra arguments
			UpperCAmelCase__		:Any 		=    args.model_type in PREPROCESSING_FUNCTIONS.keys()
			if requires_preprocessing:
						UpperCAmelCase__		:List[str] 		=    PREPROCESSING_FUNCTIONS.get(args.model_type							)
						UpperCAmelCase__		:List[str] 		=    prepare_input(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__							)

						if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
									UpperCAmelCase__		:Optional[Any] 		=    {'add_space_before_punct_symbol': True}
						else:
									UpperCAmelCase__		:Optional[Any] 		=    {}

						UpperCAmelCase__		:int 		=    tokenizer.encode(
						    UpperCamelCase__     ,						add_special_tokens=UpperCamelCase__     ,						return_tensors='pt'     ,						**UpperCamelCase__							)
			else:
						UpperCAmelCase__		:Union[str, Any] 		=    args.prefix if args.prefix else args.padding_text
						UpperCAmelCase__		:Tuple 		=    tokenizer.encode(prefix + prompt_text     ,						add_special_tokens=UpperCamelCase__     ,						return_tensors='pt'							)
			UpperCAmelCase__		:Tuple 		=    encoded_prompt.to(args.device							)

			if encoded_prompt.size()[-1] == 0:
						UpperCAmelCase__		:Dict 		=    None
			else:
						UpperCAmelCase__		:Union[str, Any] 		=    encoded_prompt

			if args.jit:
						UpperCAmelCase__		:Optional[Any] 		=    ['enable jit']
						UpperCAmelCase__		:List[Any] 		=    prepare_jit_inputs(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__							)
						torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__							)
						UpperCAmelCase__		:Optional[Any] 		=    False
						if hasattr(UpperCamelCase__     ,						'forward'							):
									UpperCAmelCase__		:Tuple 		=    inspect.signature(model.forward							)
						else:
									UpperCAmelCase__		:Optional[Any] 		=    inspect.signature(model.__call__							)
						UpperCAmelCase__		:int 		=    tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__     ,						UpperCamelCase__							) is not None							)
						UpperCAmelCase__		:Any 		=    torch.jit.trace(UpperCamelCase__     ,						UpperCamelCase__     ,						strict=UpperCamelCase__							)
						UpperCAmelCase__		:str 		=    torch.jit.freeze(traced_model.eval()							)
						traced_model(*UpperCamelCase__							)
						traced_model(*UpperCamelCase__							)

						UpperCAmelCase__		:List[str] 		=    _ModelFallbackWrapper(UpperCamelCase__     ,						UpperCamelCase__							)

			UpperCAmelCase__		:Union[str, Any] 		=    model.generate(
			    input_ids=UpperCamelCase__     ,						max_length=args.length + len(encoded_prompt[0]							)     ,						temperature=args.temperature     ,						top_k=args.k     ,						top_p=args.p     ,						repetition_penalty=args.repetition_penalty     ,						do_sample=UpperCamelCase__     ,						num_return_sequences=args.num_return_sequences     ,						)

			# Remove the batch dimension when returning multiple sequences
			if len(output_sequences.shape							) > 2:
						output_sequences.squeeze_()

			UpperCAmelCase__		:int 		=    []

			for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__							):
						print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""							)
						UpperCAmelCase__		:Dict 		=    generated_sequence.tolist()

						# Decode text
						UpperCAmelCase__		:List[Any] 		=    tokenizer.decode(UpperCamelCase__     ,						clean_up_tokenization_spaces=UpperCamelCase__							)

						# Remove all text after the stop token
						UpperCAmelCase__		:Tuple 		=    text[: text.find(args.stop_token							) if args.stop_token else None]

						# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
						UpperCAmelCase__		:List[str] 		=    (
						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]     ,						clean_up_tokenization_spaces=UpperCamelCase__							)							) :]
						)

						generated_sequences.append(UpperCamelCase__							)
						print(UpperCamelCase__							)

			return generated_sequences


if __name__ == "__main__":
						main()












import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

					_snake_case						=					hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'    ):
										_snake_case						=					convert_classification(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForAudioFrameClassification'    ):
										_snake_case						=					convert_diarization(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForXVector'    ):
										_snake_case						=					convert_xvector(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					else:
										raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''    )

					if hf_config.use_weighted_layer_sum:
										_snake_case						=					checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(UpperCamelCase__    )
					hf_model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
					UpperCAmelCase_   				=  argparse.ArgumentParser()
					parser.add_argument(
					    """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
					)
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
					parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
					UpperCAmelCase_   				=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



