


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
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_IMAGE_SEGMENTATION_MAPPING,
      MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
      MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
  )


__UpperCAmelCase       :       int       	= logging.get_logger(__name__)


__UpperCAmelCase       :       Optional[Any]       	= Dict[str, Any]
__UpperCAmelCase       :       List[str]       	= List[Prediction]



@add_end_docstrings(__snake_case)
class       UpperCAmelCase_		(							__snake_case):
       '''simple docstring'''






       def __init__(   self       ,    *__SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              super().__init__(*lowercase_       ,    **lowercase_  )

              if self.framework == "tf":
                     raise ValueError(f"""The {self.__class__} is only available in PyTorch."""  )

              requires_backends(self       ,    '''vision'''  )
              self.check_model_type(
                  dict(
                      MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                      + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                      + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                      + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()  )  )






       def        _lowercase      (   self       ,    **__SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						{}
              UpperCamelCase			:		List[Any]      		=						{}
              if "subtask" in kwargs:
                     UpperCamelCase			:		Any      		=						kwargs["subtask"]
                     UpperCamelCase			:		int      		=						kwargs["subtask"]
              if "threshold" in kwargs:
                     UpperCamelCase			:		List[str]      		=						kwargs["threshold"]
              if "mask_threshold" in kwargs:
                     UpperCamelCase			:		List[str]      		=						kwargs["mask_threshold"]
              if "overlap_mask_area_threshold" in kwargs:
                     UpperCamelCase			:		int      		=						kwargs["overlap_mask_area_threshold"]

              return preprocess_kwargs, {}, postprocess_kwargs






       def __call__(   self       ,    __SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              return super().__call__(lowercase_       ,    **lowercase_  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE=None  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						load_image(lowercase_  )
              UpperCamelCase			:		Any      		=						[(image.height, image.width)]
              if self.model.config.__class__.__name__ == "OneFormerConfig":
                     if subtask is None:
                            UpperCamelCase			:		str      		=						{}
                     else:
                            UpperCamelCase			:		int      		=						{"task_inputs": [subtask]}
                     UpperCamelCase			:		Union[str, Any]      		=						self.image_processor(images=[image]       ,    return_tensors='''pt'''       ,    **lowercase_  )
                     UpperCamelCase			:		Tuple      		=						self.tokenizer(
                         inputs['''task_inputs''']       ,    padding='''max_length'''       ,    max_length=self.model.config.task_seq_len       ,    return_tensors=self.framework       ,    )["input_ids"]
              else:
                     UpperCamelCase			:		Dict      		=						self.image_processor(images=[image]       ,    return_tensors='''pt'''  )
              UpperCamelCase			:		List[Any]      		=						target_size
              return inputs






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		int      		=						model_inputs.pop('''target_size'''  )
              UpperCamelCase			:		int      		=						self.model(**lowercase_  )
              UpperCamelCase			:		Any      		=						target_size
              return model_outputs






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE=None       ,    __SCREAMING_SNAKE_CASE=0.9       ,    __SCREAMING_SNAKE_CASE=0.5       ,    __SCREAMING_SNAKE_CASE=0.5  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						None
              if subtask in {"panoptic", None} and hasattr(self.image_processor       ,    '''post_process_panoptic_segmentation'''  ):
                     UpperCamelCase			:		Dict      		=						self.image_processor.post_process_panoptic_segmentation
              elif subtask in {"instance", None} and hasattr(self.image_processor       ,    '''post_process_instance_segmentation'''  ):
                     UpperCamelCase			:		Dict      		=						self.image_processor.post_process_instance_segmentation

              if fn is not None:
                     UpperCamelCase			:		List[Any]      		=						fn(
                         lowercase_       ,    threshold=lowercase_       ,    mask_threshold=lowercase_       ,    overlap_mask_area_threshold=lowercase_       ,    target_sizes=model_outputs['''target_size''']       ,    )[0]

                     UpperCamelCase			:		Optional[Any]      		=						[]
                     UpperCamelCase			:		int      		=						outputs["segmentation"]

                     for segment in outputs["segments_info"]:
                            UpperCamelCase			:		Union[str, Any]      		=						(segmentation == segment["id"]) * 255
                            UpperCamelCase			:		Tuple      		=						Image.fromarray(mask.numpy().astype(np.uinta  )       ,    mode='''L'''  )
                            UpperCamelCase			:		List[str]      		=						self.model.config.idalabel[segment["label_id"]]
                            UpperCamelCase			:		List[Any]      		=						segment["score"]
                            annotation.append({'''score''': score, '''label''': label, '''mask''': mask}  )

              elif subtask in {"semantic", None} and hasattr(self.image_processor       ,    '''post_process_semantic_segmentation'''  ):
                     UpperCamelCase			:		List[Any]      		=						self.image_processor.post_process_semantic_segmentation(
                         lowercase_       ,    target_sizes=model_outputs['''target_size''']  )[0]

                     UpperCamelCase			:		Tuple      		=						[]
                     UpperCamelCase			:		int      		=						outputs.numpy()
                     UpperCamelCase			:		Tuple      		=						np.unique(lowercase_  )

                     for label in labels:
                            UpperCamelCase			:		Optional[int]      		=						(segmentation == label) * 255
                            UpperCamelCase			:		Dict      		=						Image.fromarray(mask.astype(np.uinta  )       ,    mode='''L'''  )
                            UpperCamelCase			:		int      		=						self.model.config.idalabel[label]
                            annotation.append({'''score''': None, '''label''': label, '''mask''': mask}  )
              else:
                     raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model  )}"""  )
              return annotation


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    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}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[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.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)