






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.gptj.modeling_tf_gptj import (
           TFGPTJForCausalLM,
           TFGPTJForQuestionAnswering,
           TFGPTJForSequenceClassification,
           TFGPTJModel,
           shape_list,
       )






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	Union[str, Any]				,				lowercase__    :	List[str]					)  ->       Any:
          __A		   =      parent
          __A		   =      13
          __A		   =      7
          __A		   =      True
          __A		   =      True
          __A		   =      True
          __A		   =      True
          __A		   =      True
          __A		   =      99
          __A		   =      32
          __A		   =      4
          __A		   =      2
          __A		   =      4
          __A		   =      37
          __A		   =      "gelu"
          __A		   =      0.1
          __A		   =      0.1
          __A		   =      512
          __A		   =      16
          __A		   =      2
          __A		   =      0.02
          __A		   =      3
          __A		   =      4
          __A		   =      None
          __A		   =      self.vocab_size - 1
          __A		   =      self.vocab_size - 1
          __A		   =      self.vocab_size - 1






     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[Any]:
          __A		   =      ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

          __A		   =      None
          if self.use_input_mask:
               __A		   =      random_attention_mask([self.batch_size, self.seq_length]					)

          __A		   =      None
          if self.use_token_type_ids:
               __A		   =      ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size					)

          __A		   =      None
          if self.use_mc_token_ids:
               __A		   =      ids_tensor([self.batch_size, self.num_choices]				,				self.seq_length					)

          __A		   =      None
          __A		   =      None
          __A		   =      None
          if self.use_labels:
               __A		   =      ids_tensor([self.batch_size]				,				self.type_sequence_label_size					)
               __A		   =      ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels					)
               __A		   =      ids_tensor([self.batch_size]				,				self.num_choices					)

          __A		   =      GPTJConfig(
              vocab_size=self.vocab_size				,				n_embd=self.hidden_size				,				n_layer=self.num_hidden_layers				,				n_head=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				n_positions=self.max_position_embeddings				,				type_vocab_size=self.type_vocab_size				,				initializer_range=self.initializer_range				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				rotary_dim=self.rotary_dim				,				return_dict=lowercase__				,				)

          __A		   =      ids_tensor([self.num_hidden_layers, self.num_attention_heads]				,				2					)

          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	int				,				lowercase__    :	int				,				lowercase__    :	Optional[int]				,				lowercase__    :	Any				,				lowercase__    :	str				,				*lowercase__    :	Tuple					)  ->       Optional[int]:
          __A		   =      TFGPTJModel(config=lowercase__					)
          __A		   =      {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          __A		   =      model(lowercase__					)

          __A		   =      [input_ids, None, input_mask]  # None is the input for 'past'
          __A		   =      model(lowercase__					)

          __A		   =      model(lowercase__					)

          self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)					)






     def     _UpperCamelCase   (self    :	int				,				lowercase__    :	Optional[int]				,				lowercase__    :	int				,				lowercase__    :	Tuple				,				lowercase__    :	str				,				lowercase__    :	Dict				,				*lowercase__    :	int					)  ->       List[Any]:
          __A		   =      TFGPTJModel(config=lowercase__					)

          # first forward pass
          __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)
          __A		   =      model(lowercase__				,				token_type_ids=lowercase__					)
          __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)

          self.parent.assertTrue(len(lowercase__					) == len(lowercase__					)					)
          self.parent.assertTrue(len(lowercase__					) == len(lowercase__					) + 1					)

          __A   ,				__A		   =      outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          __A		   =      ids_tensor((self.batch_size, 1)				,				config.vocab_size					)
          __A		   =      ids_tensor([self.batch_size, 1]				,				self.type_vocab_size					)

          # append to next input_ids and token_type_ids
          __A		   =      tf.concat([input_ids, next_tokens]				,				axis=-1					)
          __A		   =      tf.concat([token_type_ids, next_token_types]				,				axis=-1					)

          __A		   =      model(lowercase__				,				token_type_ids=lowercase__					)["last_hidden_state"]
          __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				past_key_values=lowercase__					)[
              "last_hidden_state"
          ]

          # select random slice
          __A		   =      int(ids_tensor((1,)				,				shape_list(lowercase__					)[-1]					)					)
          __A		   =      output_from_no_past[:, -1, random_slice_idx]
          __A		   =      output_from_past[:, 0, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(lowercase__				,				lowercase__				,				rtol=1e-6					)






     def     _UpperCamelCase   (self    :	List[str]				,				lowercase__    :	str				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	int				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Tuple				,				*lowercase__    :	Optional[int]					)  ->       List[Any]:
          __A		   =      TFGPTJModel(config=lowercase__					)

          # create attention mask
          __A		   =      self.seq_length // 2
          __A		   =      tf.ones((self.batch_size, half_seq_length)				,				dtype=tf.intaa					)
          __A		   =      tf.zeros((self.batch_size, self.seq_length - half_seq_length)				,				dtype=tf.intaa					)
          __A		   =      tf.concat([attn_mask_begin, attn_mask_end]				,				axis=1					)

          # first forward pass
          __A   ,				__A		   =      model(lowercase__				,				attention_mask=lowercase__					).to_tuple()

          # create hypothetical next token and extent to next_input_ids
          __A		   =      ids_tensor((self.batch_size, 1)				,				config.vocab_size					)

          # change a random masked slice from input_ids
          __A		   =      ids_tensor((1,)				,				lowercase__					).numpy() + 1
          __A		   =      ids_tensor((self.batch_size, self.seq_length)				,				config.vocab_size					)
          __A		   =      tf.range(self.seq_length					) == (self.seq_length - random_seq_idx_to_change)
          __A		   =      tf.transpose(
              tf.broadcast_to(tf.expand_dims(lowercase__				,				-1					)				,				(self.seq_length, self.batch_size)					)					)
          __A		   =      tf.where(lowercase__				,				lowercase__				,				lowercase__					)

          # append to next input_ids and attn_mask
          __A		   =      tf.concat([input_ids, next_tokens]				,				axis=-1					)
          __A		   =      tf.concat([attn_mask, tf.ones((shape_list(lowercase__					)[0], 1)				,				dtype=tf.intaa					)]				,				axis=1					)

          # get two different outputs
          __A		   =      model(lowercase__				,				attention_mask=lowercase__					)["last_hidden_state"]
          __A		   =      model(lowercase__				,				past_key_values=lowercase__				,				attention_mask=lowercase__					)[
              "last_hidden_state"
          ]

          # select random slice
          __A		   =      int(ids_tensor((1,)				,				shape_list(lowercase__					)[-1]					)					)
          __A		   =      output_from_no_past[:, -1, random_slice_idx]
          __A		   =      output_from_past[:, 0, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(lowercase__				,				lowercase__				,				rtol=1e-12					)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Dict				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Tuple				,				lowercase__    :	Union[str, Any]				,				*lowercase__    :	Optional[Any]					)  ->       List[Any]:
          __A		   =      TFGPTJModel(config=lowercase__					)

          __A		   =      input_ids[:1, :]
          __A		   =      input_mask[:1, :]
          __A		   =      token_type_ids[:1, :]
          __A		   =      1

          # first forward pass
          __A		   =      model(lowercase__				,				attention_mask=lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)

          __A   ,				__A		   =      outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          __A		   =      ids_tensor((self.batch_size, 3)				,				config.vocab_size					)
          __A		   =      ids_tensor((self.batch_size, 3)				,				2					)
          __A		   =      ids_tensor((self.batch_size, 3)				,				self.type_vocab_size					)

          # append to next input_ids and token_type_ids
          __A		   =      tf.concat([input_ids, next_tokens]				,				axis=-1					)
          __A		   =      tf.concat([input_mask, next_attn_mask]				,				axis=-1					)
          __A		   =      tf.concat([token_type_ids, next_token_types]				,				axis=-1					)

          __A		   =      model(
              lowercase__				,				token_type_ids=lowercase__				,				attention_mask=lowercase__					)["last_hidden_state"]
          __A		   =      model(
              lowercase__				,				token_type_ids=lowercase__				,				attention_mask=lowercase__				,				past_key_values=lowercase__				,				)["last_hidden_state"]
          self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]					)

          # select random slice
          __A		   =      int(ids_tensor((1,)				,				shape_list(lowercase__					)[-1]					)					)
          __A		   =      output_from_no_past[:, -3:, random_slice_idx]
          __A		   =      output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(lowercase__				,				lowercase__				,				rtol=1e-3					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	Optional[Any]				,				*lowercase__    :	List[Any]					)  ->       Optional[Any]:
          __A		   =      TFGPTJForCausalLM(config=lowercase__					)
          __A		   =      {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          __A		   =      model(lowercase__					)
          self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
          __A		   =      self.prepare_config_and_inputs()

          (
              (
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,				(
              __A				
          )   ,								
          )		   =      config_and_inputs

          __A		   =      {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







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







     _snake_case             =  (
         (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
         if is_tf_available()
         else ()
     )

     _snake_case             =  (TFGPTJForCausalLM,) if is_tf_available() else ()
     _snake_case             =  (
         {
             'feature-extraction': TFGPTJModel,
             'question-answering': TFGPTJForQuestionAnswering,
             'text-classification': TFGPTJForSequenceClassification,
             'text-generation': TFGPTJForCausalLM,
             'zero-shot': TFGPTJForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     _snake_case             =  False
     _snake_case             =  False
     _snake_case             =  False
     _snake_case             =  False






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Dict				,				lowercase__    :	List[str]				,				lowercase__    :	List[Any]				,				lowercase__    :	Optional[int]				,				lowercase__    :	Any					)  ->       Optional[Any]:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast"					)
          ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

          return False






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[Any]:
          __A		   =      TFGPTJModelTester(self					)
          __A		   =      ConfigTester(self				,				config_class=lowercase__				,				n_embd=37					)






     def     _UpperCamelCase   (self    :	Any					)  ->       str:
          self.config_tester.run_common_tests()






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Any:
          __A		   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gptj_model(*lowercase__					)






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[Any]:
          __A		   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gptj_model_past(*lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
          __A		   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       List[Any]:
          __A		   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowercase__					)






     def     _UpperCamelCase   (self    :	int					)  ->       str:
          __A		   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gptj_lm_head_model(*lowercase__					)






     @slow
     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices("GPU"					)					) > 0				,				"skip testing on GPU for now to avoid GPU OOM."				,				)
     def     _UpperCamelCase   (self    :	Any					)  ->       str:
          __A		   =      TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"				,				from_pt=lowercase__					)
          self.assertIsNotNone(lowercase__					)






     @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."					)
     def     _UpperCamelCase   (self    :	int					)  ->       int:
          super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[int]:
          __A		   =      TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				,				from_pt=lowercase__					)
          __A		   =      tf.convert_to_tensor([[464, 3290]]				,				dtype=tf.intaa					)  # The dog
          # fmt: off
          # The dog is a man's best friend. It is a loyal companion, and it is a friend
          __A		   =      [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
          # fmt: on
          __A		   =      model.generate(lowercase__				,				do_sample=lowercase__					)
          self.assertListEqual(output_ids[0].numpy().tolist()				,				lowercase__					)






     def     _UpperCamelCase   (self    :	int					)  ->       int:
          __A		   =      AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"					)
          __A		   =      TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"				,				from_pt=lowercase__					)

          __A		   =      tokenizer("Today is a nice day and"				,				return_tensors="tf"					)
          # forces the generation to happen on CPU, to avoid GPU-related quirks
          with tf.device(":/CPU:0"					):
               __A		   =      model.generate(**lowercase__				,				do_sample=lowercase__				,				seed=[42, 0]					)
          __A		   =      tokenizer.decode(output_ids[0]				,				skip_special_tokens=lowercase__					)

          __A		   =      "Today is a nice day and I’m going to go for a walk. I’"
          self.assertEqual(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
          __A		   =      TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"				,				from_pt=lowercase__					)
          __A		   =      AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"					)

          __A		   =      "left"

          # Define PAD Token = EOS Token = 50256
          __A		   =      tokenizer.eos_token
          __A		   =      model.config.eos_token_id

          # use different length sentences to test batching
          __A		   =      [
              "Hello, my dog is a little",
              "Today, I",
          ]
          __A		   =      [
              "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
              "Today, I’m going to be talking about a topic that’",
          ]
          return model, tokenizer, sentences, expected_output_sentences






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[str]:
          # Confirms that we get the expected results with left-padded beam search
          __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

          __A		   =      tokenizer(lowercase__				,				return_tensors="tf"				,				padding=lowercase__					)
          __A		   =      model.generate(**lowercase__				,				do_sample=lowercase__				,				num_beams=2					)
          __A		   =      tokenizer.batch_decode(lowercase__				,				skip_special_tokens=lowercase__					)
          self.assertListEqual(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Any:
          # Confirms that left-padding is working properly
          __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

          __A		   =      tokenizer(lowercase__				,				return_tensors="tf"				,				padding=lowercase__					)
          __A		   =      tokenizer(sentences[0]				,				return_tensors="tf"					)
          __A		   =      model.generate(**lowercase__				,				do_sample=lowercase__				,				num_beams=2					)
          __A		   =      (
              shape_list(inputs_non_padded["input_ids"]					)[-1]
              - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]				,				tf.intaa					)					).numpy()
          )
          __A		   =      tokenizer(sentences[1]				,				return_tensors="tf"					)
          __A		   =      model.generate(
              **lowercase__				,				do_sample=lowercase__				,				num_beams=2				,				max_length=model.config.max_length - num_paddings					)
          __A		   =      tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=lowercase__					)
          __A		   =      tokenizer.decode(output_padded[0]				,				skip_special_tokens=lowercase__					)
          self.assertListEqual(lowercase__				,				[non_padded_sentence, padded_sentence]					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       int:
          # Confirms that XLA is working properly
          __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

          __A		   =      tokenizer(lowercase__				,				return_tensors="tf"				,				padding=lowercase__					)
          __A		   =      tf.function(model.generate				,				jit_compile=lowercase__					)
          __A		   =      xla_generate(**lowercase__				,				do_sample=lowercase__				,				num_beams=2					)
          __A		   =      tokenizer.batch_decode(lowercase__				,				skip_special_tokens=lowercase__					)
          self.assertListEqual(lowercase__				,				lowercase__					)










'''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


