






"""simple docstring"""





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					=     logging.get_logger(__name__)


UpperCamelCase					=     Dict[str, Any]
UpperCamelCase					=     List[Prediction]







@add_end_docstrings(lowercase_       )
class 						_a  (		lowercase_       ):





   '''simple docstring'''






   def __init__(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   List[Any]:




          '''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 __lowercase							(       self					, **UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




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

          return preprocess_kwargs, {}, postprocess_kwargs





   def __call__(       self					, UpperCAmelCase_					, **UpperCAmelCase_)			->   Union[Predictions, List[Prediction]]:




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=None)			->   Any:




          '''simple docstring'''




          lowercase__:							str         =     load_image(UpperCAmelCase_)
          lowercase__:							Dict         =     [(image.height, image.width)]
          if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                        lowercase__:							str         =     {}
                 else:
                        lowercase__:							Union[str, Any]         =     {"task_inputs": [subtask]}
                 lowercase__:							Any         =     self.image_processor(images=[image]					, return_tensors="pt"					, **UpperCAmelCase_)
                 lowercase__:							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:
                 lowercase__:							Optional[int]         =     self.image_processor(images=[image]					, return_tensors="pt")
          lowercase__:							int         =     target_size
          return inputs





   def __lowercase							(       self					, UpperCAmelCase_)			->   Any:




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=None					, UpperCAmelCase_=0.9					, UpperCAmelCase_=0.5					, UpperCAmelCase_=0.5)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     None
          if subtask in {"panoptic", None} and hasattr(self.image_processor					, "post_process_panoptic_segmentation"):
                 lowercase__:							Union[str, Any]         =     self.image_processor.post_process_panoptic_segmentation
          elif subtask in {"instance", None} and hasattr(self.image_processor					, "post_process_instance_segmentation"):
                 lowercase__:							Optional[int]         =     self.image_processor.post_process_instance_segmentation

          if fn is not None:
                 lowercase__:							Union[str, Any]         =     fn(
                     UpperCAmelCase_					, threshold=UpperCAmelCase_					, mask_threshold=UpperCAmelCase_					, overlap_mask_area_threshold=UpperCAmelCase_					, target_sizes=model_outputs["target_size"]					, )[0]

                 lowercase__:							List[str]         =     []
                 lowercase__:							str         =     outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                        lowercase__:							str         =     (segmentation == segment["id"]) * 255
                        lowercase__:							List[Any]         =     Image.fromarray(mask.numpy().astype(np.uinta)					, mode="L")
                        lowercase__:							Union[str, Any]         =     self.model.config.idalabel[segment["label_id"]]
                        lowercase__:							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"):
                 lowercase__:							Any         =     self.image_processor.post_process_semantic_segmentation(
                     UpperCAmelCase_					, target_sizes=model_outputs["target_size"])[0]

                 lowercase__:							str         =     []
                 lowercase__:							Tuple         =     outputs.numpy()
                 lowercase__:							Dict         =     np.unique(UpperCAmelCase_)

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








"""simple docstring"""




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 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

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

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

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

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

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

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			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__":
    A__   :						Union[str, Any] 							=     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.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



