
import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_UpperCAmelCase   :					List[str]    =			logging.getLogger(__name__)


def  A			(   lowercase					)  ->    Optional[int]:
		'''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			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	argparse.ArgumentParser()
		parser.add_argument(
		    '--model_name_or_path'       ,			default=_lowercase       ,			type=_lowercase       ,			required=_lowercase       ,			)
		parser.add_argument('--prompt'       ,			type=_lowercase       ,			default=''					)
		parser.add_argument('--length'       ,			type=_lowercase       ,			default=20					)
		parser.add_argument('--stop_token'       ,			type=_lowercase       ,			default=_lowercase       ,			help='Token at which text generation is stopped'					)
		parser.add_argument(
		    '--temperature'       ,			type=_lowercase       ,			default=1.0       ,			help='temperature of 1.0 has no effect, lower tend toward greedy sampling'       ,			)
		parser.add_argument(
		    '--repetition_penalty'       ,			type=_lowercase       ,			default=1.0       ,			help='primarily useful for CTRL model; in that case, use 1.2'					)
		parser.add_argument('--k'       ,			type=_lowercase       ,			default=0					)
		parser.add_argument('--penalty_alpha'       ,			type=_lowercase       ,			default=0.0					)
		parser.add_argument('--p'       ,			type=_lowercase       ,			default=0.9					)

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

		parser.add_argument('--seed'       ,			type=_lowercase       ,			default=42       ,			help='random seed for initialization'					)
		parser.add_argument('--no_cuda'       ,			action='store_true'       ,			help='Avoid using CUDA when available'					)
		parser.add_argument(
		    '--fp16'       ,			action='store_true'       ,			help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'       ,			)
		UpperCamelCase            =	parser.parse_args()

		UpperCamelCase            =	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
		UpperCamelCase            =	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(_lowercase					)

		# Initialize the model and tokenizer
		UpperCamelCase            =	AutoTokenizer.from_pretrained(args.model_name_or_path					)
		UpperCamelCase            =	AutoModelForCausalLM.from_pretrained(args.model_name_or_path					)

		# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
		# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
		model.to(args.device					)

		if args.fpaa:
				model.half()

		logger.info(_lowercase					)
		UpperCamelCase            =	args.prompt if args.prompt else input('Model prompt >>> '					)

		UpperCamelCase            =	tokenizer(_lowercase       ,			return_tensors='pt'       ,			add_special_tokens=_lowercase					)
		UpperCamelCase            =	{key: value.to(args.device					) for key, value in inputs.items()}

		UpperCamelCase            =	model.generate(
		    **_lowercase       ,			max_length=args.length + len(inputs['input_ids'][0]					)       ,			penalty_alpha=args.penalty_alpha       ,			top_k=args.k       ,			)

		UpperCamelCase            =	[]
		for generated_sequence_idx, generated_sequence in enumerate(_lowercase					):
				print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''					)
				UpperCamelCase            =	generated_sequence.tolist()

				# Decode text
				UpperCamelCase            =	tokenizer.decode(_lowercase       ,			clean_up_tokenization_spaces=_lowercase       ,			add_special_tokens=_lowercase					)

				# Remove all text after the stop token
				UpperCamelCase            =	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            =	(
				    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]       ,			clean_up_tokenization_spaces=_lowercase					)					) :]
				)

				generated_sequences.append(_lowercase					)
				print(_lowercase					)

		return generated_sequences


if __name__ == "__main__":
					main()

"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			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       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






