





from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase__						)
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							int:







				'''simple docstring'''





				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )
				self.check_model_type(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,**_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							{}, {}
				if padding is not None:
							__lowercase										=							padding
				if truncation is not None:
							__lowercase										=							truncation
				if top_k is not None:
							__lowercase										=							top_k
				return preprocess_params, {}, postprocess_params


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				if isinstance(_lowerCamelCase     ,(Image.Image, str) ) and isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							{'''image''': image, '''question''': question}
				else:
							__lowercase										=							image
				__lowercase										=							super().__call__(_lowerCamelCase     ,**_lowerCamelCase )
				return results


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=False     ,_lowerCamelCase=False )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							load_image(inputs['''image'''] )
				__lowercase										=							self.tokenizer(
				    inputs['''question''']     ,return_tensors=self.framework     ,padding=_lowerCamelCase     ,truncation=_lowerCamelCase )
				__lowercase										=							self.image_processor(images=_lowerCamelCase     ,return_tensors=self.framework )
				model_inputs.update(_lowerCamelCase )
				return model_inputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							self.model(**_lowerCamelCase )
				return model_outputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=5 )							->							Tuple:







				'''simple docstring'''





				if top_k > self.model.config.num_labels:
							__lowercase										=							self.model.config.num_labels

				if self.framework == "pt":
							__lowercase										=							model_outputs.logits.sigmoid()[0]
							__lowercase						,		__lowercase										=							probs.topk(_lowerCamelCase )
				else:
							raise ValueError(f"Unsupported framework: {self.framework}" )

				__lowercase										=							scores.tolist()
				__lowercase										=							ids.tolist()
				return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase     ,_lowerCamelCase )]












'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''projector.weight''']
			__lowercase										=							downstream_dict['''projector.bias''']
			__lowercase										=							downstream_dict['''model.post_net.linear.weight''']
			__lowercase										=							downstream_dict['''model.post_net.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''model.linear.weight''']
			__lowercase										=							downstream_dict['''model.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''connector.weight''']
			__lowercase										=							downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						__lowercase										=							downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__lowercase										=							downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)

			__lowercase										=							checkpoint['''Downstream''']

			__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_		,       return_attention_mask=lowerCamelCase_		,       do_normalize=lowerCamelCase_				)

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

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

			hf_feature_extractor.save_pretrained(lowerCamelCase_				)
			hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    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.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





