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,
		)


__lowerCamelCase :     List[Any]          =  logging.get_logger(__name__)


__lowerCamelCase :     int          =  Dict[str, Any]
__lowerCamelCase :     List[str]          =  List[Prediction]






@add_end_docstrings(UpperCAmelCase_ )
class 		a__						( UpperCAmelCase_ ):

					def __init__(   self				:	str,*_A				:	List[Any],**_A				:	int   ):







							"""simple docstring"""






							super().__init__(*__UpperCamelCase,**__UpperCamelCase   )

							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     __UpperCamelCase       (   self				:	List[str],**_A				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   {}
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   {}
							if "subtask" in kwargs:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   kwargs["subtask"]
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   kwargs["subtask"]
							if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   kwargs["threshold"]
							if "mask_threshold" in kwargs:
									SCREAMING_SNAKE_CASE_  :		str    				=   kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
									SCREAMING_SNAKE_CASE_  :		Any    				=   kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs

					def __call__(   self				:	Union[str, Any],_A				:	Any,**_A				:	List[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	Optional[Any],_A				:	Tuple=None   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	int,_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model_inputs.pop("target_size"   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   self.model(**__UpperCamelCase   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   target_size
							return model_outputs






					def     __UpperCamelCase       (   self				:	Dict,_A				:	int,_A				:	Any=None,_A				:	Any=0.9,_A				:	int=0.5,_A				:	List[Any]=0.5   ):







							"""simple docstring"""






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

							if fn is not None:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   fn(
									    __UpperCamelCase,threshold=__UpperCamelCase,mask_threshold=__UpperCamelCase,overlap_mask_area_threshold=__UpperCamelCase,target_sizes=model_outputs["target_size"],)[0]

									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   []
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   outputs["segmentation"]

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

							elif subtask in {"semantic", None} and hasattr(self.image_processor,"post_process_semantic_segmentation"   ):
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.image_processor.post_process_semantic_segmentation(
									    __UpperCamelCase,target_sizes=model_outputs["target_size"]   )[0]

									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   []
									SCREAMING_SNAKE_CASE_  :		Any    				=   outputs.numpy()
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   np.unique(__UpperCamelCase   )

									for label in labels:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   (segmentation == label) * 255
											SCREAMING_SNAKE_CASE_  :		Any    				=   Image.fromarray(mask.astype(np.uinta   ),mode="L"   )
											SCREAMING_SNAKE_CASE_  :		Optional[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

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
