



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,
		)


snake_case     :    int						=    logging.get_logger(__name__)


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




@add_end_docstrings(__lowerCamelCase    )
class 				lowerCAmelCase__	(						__lowerCamelCase    ):







					def __init__(							self   :  Optional[int]      ,		*_A   :  Optional[Any]      ,		**_A   :  Optional[int]):
											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   :  Any      ,		**_A   :  List[str]):
											A__						:				str         =			{}
											A__						:				Optional[int]         =			{}
											if "subtask" in kwargs:
																	A__						:				str         =			kwargs["subtask"]
																	A__						:				Union[str, Any]         =			kwargs["subtask"]
											if "threshold" in kwargs:
																	A__						:				Optional[Any]         =			kwargs["threshold"]
											if "mask_threshold" in kwargs:
																	A__						:				Any         =			kwargs["mask_threshold"]
											if "overlap_mask_area_threshold" in kwargs:
																	A__						:				Tuple         =			kwargs["overlap_mask_area_threshold"]

											return preprocess_kwargs, {}, postprocess_kwargs







					def __call__(							self   :  Dict      ,		_A   :  Dict      ,		**_A   :  List[Any]):
											return super().__call__(UpperCamelCase_      ,		**UpperCamelCase_)







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







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											A__						:				List[Any]         =			model_inputs.pop("target_size")
											A__						:				int         =			self.model(**UpperCamelCase_)
											A__						:				Optional[Any]         =			target_size
											return model_outputs







					def 			_lowercase      (							self   :  int      ,		_A   :  Tuple      ,		_A   :  Tuple=None      ,		_A   :  Optional[int]=0.9      ,		_A   :  str=0.5      ,		_A   :  Optional[int]=0.5):
											A__						:				Optional[Any]         =			None
											if subtask in {"panoptic", None} and hasattr(self.image_processor      ,		"post_process_panoptic_segmentation"):
																	A__						:				Any         =			self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor      ,		"post_process_instance_segmentation"):
																	A__						:				List[str]         =			self.image_processor.post_process_instance_segmentation

											if fn is not None:
																	A__						:				List[str]         =			fn(
																	    UpperCamelCase_      ,		threshold=UpperCamelCase_      ,		mask_threshold=UpperCamelCase_      ,		overlap_mask_area_threshold=UpperCamelCase_      ,		target_sizes=model_outputs["target_size"]      ,		)[0]

																	A__						:				int         =			[]
																	A__						:				Optional[int]         =			outputs["segmentation"]

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

																	A__						:				Optional[Any]         =			[]
																	A__						:				Union[str, Any]         =			outputs.numpy()
																	A__						:				int         =			np.unique(UpperCamelCase_)

																	for label in labels:
																							A__						:				Any         =			(segmentation == label) * 255
																							A__						:				Tuple         =			Image.fromarray(mask.astype(np.uinta)      ,		mode="L")
																							A__						:				Tuple         =			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    lowerCamelCase__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

       # Embeddings
       # Position IDs
       __lowerCamelCase  =		fnet_pretraining_model.state_dict()

       __lowerCamelCase  =		state_dict["""fnet.embeddings.position_ids"""]
       __lowerCamelCase  =		{"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

       # Encoder Layers
       for layer in range(config.num_hidden_layers      ):
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

       # Pooler Layers
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

       # Masked LM Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

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

       # Load State Dict
       fnet_pretraining_model.load_state_dict(A__      )

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


if __name__ == "__main__":
       UpperCAmelCase_   							=      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.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

