






'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       __A      :       Any      =	'platform'

       import jax
       import jax.numpy as jnp

       from transformers import AutoTokenizer
       from transformers.models.mbart.modeling_flax_mbart import (
           FlaxMBartForConditionalGeneration,
           FlaxMBartForQuestionAnswering,
           FlaxMBartForSequenceClassification,
           FlaxMBartModel,
           shift_tokens_right,
       )


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__=None							,			snake_case__=None							,			snake_case__=None							,			snake_case__=None							,			snake_case__=None							,			snake_case__=None							,			):
      if attention_mask is None:
            __A		   =      np.where(input_ids != config.pad_token_id							,			1							,			0							)
      if decoder_attention_mask is None:
            __A		   =      np.where(decoder_input_ids != config.pad_token_id							,			1							,			0							)
      if head_mask is None:
            __A		   =      np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            __A		   =      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            __A		   =      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }






class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     def __init__(self    :	int				,				lowercase__    :	List[Any]				,				lowercase__    :	List[str]=13				,				lowercase__    :	List[str]=7				,				lowercase__    :	str=True				,				lowercase__    :	Union[str, Any]=False				,				lowercase__    :	Optional[int]=99				,				lowercase__    :	Union[str, Any]=16				,				lowercase__    :	List[Any]=2				,				lowercase__    :	Any=4				,				lowercase__    :	Any=4				,				lowercase__    :	str="gelu"				,				lowercase__    :	Optional[Any]=0.1				,				lowercase__    :	Optional[Any]=0.1				,				lowercase__    :	str=32				,				lowercase__    :	Dict=2				,				lowercase__    :	List[Any]=1				,				lowercase__    :	int=0				,				lowercase__    :	Any=2				,				lowercase__    :	Optional[int]=0.02				,				)  ->       Union[str, Any]:
           __A		   =      parent
           __A		   =      batch_size
           __A		   =      seq_length
           __A		   =      is_training
           __A		   =      use_labels
           __A		   =      vocab_size
           __A		   =      hidden_size
           __A		   =      num_hidden_layers
           __A		   =      num_attention_heads
           __A		   =      intermediate_size
           __A		   =      hidden_act
           __A		   =      hidden_dropout_prob
           __A		   =      attention_probs_dropout_prob
           __A		   =      max_position_embeddings
           __A		   =      eos_token_id
           __A		   =      pad_token_id
           __A		   =      bos_token_id
           __A		   =      decoder_start_token_id
           __A		   =      initializer_range






     def     _UpperCamelCase   (self    :	int					)  ->       Any:
           __A		   =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size					)				,				3				,				self.vocab_size					)
           __A		   =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,				dtype=np.intaa					))				,				-1					)

           __A		   =      shift_tokens_right(lowercase__				,				1					)

           __A		   =      MBartConfig(
               vocab_size=self.vocab_size				,				d_model=self.hidden_size				,				encoder_layers=self.num_hidden_layers				,				decoder_layers=self.num_hidden_layers				,				encoder_attention_heads=self.num_attention_heads				,				decoder_attention_heads=self.num_attention_heads				,				encoder_ffn_dim=self.intermediate_size				,				decoder_ffn_dim=self.intermediate_size				,				dropout=self.hidden_dropout_prob				,				attention_dropout=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				eos_token_id=self.eos_token_id				,				bos_token_id=self.bos_token_id				,				pad_token_id=self.pad_token_id				,				decoder_start_token_id=self.decoder_start_token_id				,				initializer_range=self.initializer_range				,				use_cache=lowercase__				,				)
           __A		   =      prepare_mbart_inputs_dict(lowercase__				,				lowercase__				,				lowercase__					)
           return config, inputs_dict






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       str:
           __A   ,				__A		   =      self.prepare_config_and_inputs()
           return config, inputs_dict






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	int				,				lowercase__    :	Tuple				,				lowercase__    :	Tuple					)  ->       Dict:
           __A		   =      20
           __A		   =      model_class_name(lowercase__					)

           __A		   =      model.encode(inputs_dict["input_ids"]					)

           __A   ,				__A		   =      (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           __A		   =      model.init_cache(decoder_input_ids.shape[0]				,				lowercase__				,				lowercase__					)
           __A		   =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,				dtype="i4"					)

           __A		   =      jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]				,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,				)
           __A		   =      model.decode(
               decoder_input_ids[:, :-1]				,				lowercase__				,				decoder_attention_mask=lowercase__				,				past_key_values=lowercase__				,				decoder_position_ids=lowercase__				,				)

           __A		   =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,				dtype="i4"					)
           __A		   =      model.decode(
               decoder_input_ids[:, -1:]				,				lowercase__				,				decoder_attention_mask=lowercase__				,				past_key_values=outputs_cache.past_key_values				,				decoder_position_ids=lowercase__				,				)

           __A		   =      model.decode(lowercase__				,				lowercase__					)

           __A		   =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1e-3				,				msg=f"Max diff is {diff}"					)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Any				,				lowercase__    :	Tuple				,				lowercase__    :	int					)  ->       List[Any]:
           __A		   =      20
           __A		   =      model_class_name(lowercase__					)

           __A		   =      model.encode(inputs_dict["input_ids"]					)

           __A   ,				__A		   =      (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           __A		   =      jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
               ]				,				axis=-1				,				)

           __A		   =      model.init_cache(decoder_input_ids.shape[0]				,				lowercase__				,				lowercase__					)
           __A		   =      jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]				,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,				)

           __A		   =      model.decode(
               decoder_input_ids[:, :-1]				,				lowercase__				,				decoder_attention_mask=lowercase__				,				past_key_values=lowercase__				,				decoder_position_ids=lowercase__				,				)
           __A		   =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,				dtype="i4"					)
           __A		   =      model.decode(
               decoder_input_ids[:, -1:]				,				lowercase__				,				past_key_values=outputs_cache.past_key_values				,				decoder_attention_mask=lowercase__				,				decoder_position_ids=lowercase__				,				)

           __A		   =      model.decode(lowercase__				,				lowercase__				,				decoder_attention_mask=lowercase__					)

           __A		   =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1e-3				,				msg=f"Max diff is {diff}"					)







@require_flax
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     _snake_case             =  99






     def     _UpperCamelCase   (self    :	Tuple					)  ->       List[str]:
           __A		   =      np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]				,				dtype=np.intaa				,				)

           __A		   =      input_ids.shape[0]
           __A		   =      MBartConfig(
               vocab_size=self.vocab_size				,				d_model=24				,				encoder_layers=2				,				decoder_layers=2				,				encoder_attention_heads=2				,				decoder_attention_heads=2				,				encoder_ffn_dim=32				,				decoder_ffn_dim=32				,				max_position_embeddings=48				,				eos_token_id=2				,				pad_token_id=1				,				bos_token_id=0				,				)
           return config, input_ids, batch_size






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Optional[Any]:
           __A   ,				__A   ,				__A		   =      self._get_config_and_data()
           __A		   =      FlaxMBartForSequenceClassification(lowercase__					)
           __A		   =      model(input_ids=lowercase__				,				decoder_input_ids=lowercase__					)
           __A		   =      (batch_size, config.num_labels)
           self.assertEqual(outputs["logits"].shape				,				lowercase__					)






     def     _UpperCamelCase   (self    :	str					)  ->       Any:
           __A   ,				__A   ,				__A		   =      self._get_config_and_data()
           __A		   =      FlaxMBartForQuestionAnswering(lowercase__					)
           __A		   =      model(input_ids=lowercase__					)

           self.assertEqual(outputs["start_logits"].shape				,				input_ids.shape					)
           self.assertEqual(outputs["end_logits"].shape				,				input_ids.shape					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Dict:
           __A   ,				__A   ,				__A		   =      self._get_config_and_data()
           __A		   =      FlaxMBartForConditionalGeneration(lowercase__					)
           __A		   =      lm_model(input_ids=lowercase__					)
           __A		   =      (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["logits"].shape				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A		   =      MBartConfig(
               vocab_size=self.vocab_size				,				d_model=14				,				encoder_layers=2				,				decoder_layers=2				,				encoder_attention_heads=2				,				decoder_attention_heads=2				,				encoder_ffn_dim=8				,				decoder_ffn_dim=8				,				max_position_embeddings=48				,				)
           __A		   =      FlaxMBartForConditionalGeneration(lowercase__					)
           __A		   =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,				dtype=np.intaa					)
           __A		   =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,				dtype=np.intaa					)
           __A		   =      lm_model(input_ids=lowercase__				,				decoder_input_ids=lowercase__					)
           __A		   =      (*summary.shape, config.vocab_size)
           self.assertEqual(outputs["logits"].shape				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       Dict:
           __A		   =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,				dtype=np.intaa					)
           __A		   =      shift_tokens_right(lowercase__				,				1					)
           __A		   =      np.equal(lowercase__				,				1					).astype(np.floataa					).sum()
           __A		   =      np.equal(lowercase__				,				1					).astype(np.floataa					).sum()
           self.assertEqual(shifted.shape				,				input_ids.shape					)
           self.assertEqual(lowercase__				,				n_pad_before - 1					)
           self.assertTrue(np.equal(shifted[:, 0]				,				2					).all()					)







@require_flax
class 					a__    (			lowerCamelCase ,						unittest.TestCase ,						lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  True
     _snake_case             =  (
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     _snake_case             =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Dict:
           __A		   =      FlaxMBartModelTester(self					)






     def     _UpperCamelCase   (self    :	str					)  ->       str:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowercase__				,				lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowercase__				,				lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                       __A		   =      self._prepare_for_class(lowercase__				,				lowercase__					)
                       __A		   =      model_class(lowercase__					)

                       @jax.jit
                       def encode_jitted(lowercase__    :	Tuple				,				lowercase__    :	List[str]=None				,				**lowercase__    :	str					):
                             return model.encode(input_ids=lowercase__				,				attention_mask=lowercase__					)

                       with self.subTest("JIT Enabled"					):
                             __A		   =      encode_jitted(**lowercase__					).to_tuple()

                       with self.subTest("JIT Disabled"					):
                             with jax.disable_jit():
                                   __A		   =      encode_jitted(**lowercase__					).to_tuple()

                       self.assertEqual(len(lowercase__					)				,				len(lowercase__					)					)
                       for jitted_output, output in zip(lowercase__				,				lowercase__					):
                             self.assertEqual(jitted_output.shape				,				output.shape					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                       __A		   =      model_class(lowercase__					)
                       __A		   =      model.encode(inputs_dict["input_ids"]				,				inputs_dict["attention_mask"]					)

                       __A		   =      {
                           "decoder_input_ids": inputs_dict["decoder_input_ids"],
                           "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                           "encoder_outputs": encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(lowercase__    :	Dict				,				lowercase__    :	Optional[int]				,				lowercase__    :	str					):
                             return model.decode(
                                 decoder_input_ids=lowercase__				,				decoder_attention_mask=lowercase__				,				encoder_outputs=lowercase__				,				)

                       with self.subTest("JIT Enabled"					):
                             __A		   =      decode_jitted(**lowercase__					).to_tuple()

                       with self.subTest("JIT Disabled"					):
                             with jax.disable_jit():
                                   __A		   =      decode_jitted(**lowercase__					).to_tuple()

                       self.assertEqual(len(lowercase__					)				,				len(lowercase__					)					)
                       for jitted_output, output in zip(lowercase__				,				lowercase__					):
                             self.assertEqual(jitted_output.shape				,				output.shape					)






     @slow
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[int]:
           for model_class_name in self.all_model_classes:
                 __A		   =      model_class_name.from_pretrained("facebook/mbart-large-cc25"				,				from_pt=lowercase__					)
                 # FlaxMBartForSequenceClassification expects eos token in input_ids
                 __A		   =      np.ones((1, 1)					) * model.config.eos_token_id
                 __A		   =      model(lowercase__					)
                 self.assertIsNotNone(lowercase__					)







@require_flax
@require_sentencepiece
@require_tokenizers
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     _snake_case             =  [
         ' UN Chief Says There Is No Military Solution in Syria',
     ]
     _snake_case             =  [
         'Şeful ONU declară că nu există o soluţie militară în Siria',
     ]
     _snake_case             =  'facebook/mbart-large-en-ro'






     @cached_property
     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[Any]:
           return AutoTokenizer.from_pretrained(self.model_name					)






     @cached_property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       str:
           __A		   =      FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,				from_pt=lowercase__					)
           return model






     def     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       Any:
           __A		   =      self.translate_src_text(**lowercase__					)
           self.assertListEqual(self.expected_text				,				lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]				,				**lowercase__    :	Union[str, Any]					)  ->       Optional[int]:
           __A		   =      self.tokenizer(self.src_text				,				**lowercase__				,				return_tensors="np"					)
           __A		   =      self.model.generate(
               model_inputs.input_ids				,				attention_mask=model_inputs.attention_mask				,				decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]				,				early_stopping=lowercase__				,				num_beams=2				,				).sequences
           __A		   =      self.tokenizer.batch_decode(lowercase__				,				skip_special_tokens=lowercase__					)
           return generated_words






     @slow
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Tuple:
           self._assert_generated_batch_equal_expected()










'''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,
       )


__A      :       int      =	logging.get_logger(__name__)


__A      :       Optional[int]      =	Dict[str, Any]
__A      :       Any      =	List[Prediction]






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           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     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






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






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

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

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

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

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      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


