





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							:      Any						=	logging.get_logger(__name__)


_lowerCAmelCase							:      List[str]						=	Dict[str, Any]
_lowerCAmelCase							:      str						=	List[Prediction]


@add_end_docstrings(lowerCamelCase__							)
class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     def __init__(							self       :Dict			,      *snake_case       :Dict			,      **snake_case       :Any  ):





        '''simple docstring'''




        super().__init__(*snake_case			,      **snake_case  )

        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 						SCREAMING_SNAKE_CASE		(							self       :Any			,      **snake_case       :Tuple  ):





        '''simple docstring'''




        A_					:				str          =			{}
        A_					:				Tuple          =			{}
        if "subtask" in kwargs:
           A_					:				Optional[Any]          =			kwargs["subtask"]
           A_					:				List[str]          =			kwargs["subtask"]
        if "threshold" in kwargs:
           A_					:				List[Any]          =			kwargs["threshold"]
        if "mask_threshold" in kwargs:
           A_					:				List[str]          =			kwargs["mask_threshold"]
        if "overlap_mask_area_threshold" in kwargs:
           A_					:				List[Any]          =			kwargs["overlap_mask_area_threshold"]

        return preprocess_kwargs, {}, postprocess_kwargs



     def __call__(							self       :List[str]			,      snake_case       :Optional[int]			,      **snake_case       :Optional[Any]  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :Union[str, Any]			,      snake_case       :Dict=None  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Dict  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			model_inputs.pop("target_size"  )
        A_					:				Tuple          =			self.model(**snake_case  )
        A_					:				List[str]          =			target_size
        return model_outputs





     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :Any			,      snake_case       :List[str]=None			,      snake_case       :Any=0.9			,      snake_case       :Optional[int]=0.5			,      snake_case       :Optional[Any]=0.5  ):





        '''simple docstring'''




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

        if fn is not None:
           A_					:				Optional[int]          =			fn(
               snake_case			,      threshold=snake_case			,      mask_threshold=snake_case			,      overlap_mask_area_threshold=snake_case			,      target_sizes=model_outputs["target_size"]			,      )[0]

           A_					:				List[Any]          =			[]
           A_					:				Optional[int]          =			outputs["segmentation"]

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

        elif subtask in {"semantic", None} and hasattr(self.image_processor			,      "post_process_semantic_segmentation"  ):
           A_					:				Dict          =			self.image_processor.post_process_semantic_segmentation(
               snake_case			,      target_sizes=model_outputs["target_size"]  )[0]

           A_					:				Any          =			[]
           A_					:				Optional[int]          =			outputs.numpy()
           A_					:				Any          =			np.unique(snake_case  )

           for label in labels:
              A_					:				Tuple          =			(segmentation == label) * 255
              A_					:				List[str]          =			Image.fromarray(mask.astype(np.uinta  )			,      mode="L"  )
              A_					:				Dict          =			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 flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   A_					:				Optional[Any]          =			fnet_pretraining_model.state_dict()

   A_					:				Optional[int]          =			state_dict["fnet.embeddings.position_ids"]
   A_					:				Union[str, Any]          =			{"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
   A_					:				Dict          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

   # Encoder Layers
   for layer in range(config.num_hidden_layers	):
      A_					:				List[str]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

      A_					:				Any          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
      A_					:				Tuple          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

   # Pooler Layers
   A_					:				Optional[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
   A_					:				List[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

   # Masked LM Layers
   A_					:				Optional[Any]          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]	).T
   A_					:				int          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]	)
   A_					:				Tuple          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]	)
   A_					:				List[str]          =			torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_bias"]	)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(_lowerCAmelCase	)

   # Save PreTrained
   print(f"Saving pretrained model to {save_path}"	)
   fnet_pretraining_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Tuple						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--fnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained FNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 _lowerCAmelCase							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
