



'''simple docstring'''





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			BigBirdConfig
 __lowerCamelCase       :			jnp.dtype		=			jnp.floataa
 __lowerCamelCase       :			bool		=			True



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      super().setup()
      _a           =    nn.Dense(5					,  dtype=self.dtype			)



 def __call__(self					,  *A					,  **A			)       ->	Any:


      """simple docstring"""



      _a           =    super().__call__(*A					,  **A			)
      _a           =    self.cls(outputs[2]			)
      return outputs[:2] + (cls_out,)






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Any		=			FlaxBigBirdForNaturalQuestionsModule





def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     def cross_entropy(__A    ,      __A    ,      __A=None):
          _a           =    logits.shape[-1]
          _a           =    (labels[..., None] == jnp.arange(__A)[None]).astype('''f4''')
          _a           =    jax.nn.log_softmax(__A    ,      axis=-1)
          _a           =    -jnp.sum(labels * logits    ,      axis=-1)
          if reduction is not None:
               _a           =    reduction(__A)
          return loss

     _a           =    partial(__A    ,      reduction=jnp.mean)
     _a           =    cross_entropy(__A    ,      __A)
     _a           =    cross_entropy(__A    ,      __A)
     _a           =    cross_entropy(__A    ,      __A)
     return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			str		=			"google/bigbird-roberta-base"
 __lowerCamelCase       :			int		=			3_000
 __lowerCamelCase       :			int		=			10_500

 __lowerCamelCase       :			int		=			128
 __lowerCamelCase       :			int		=			3

 __lowerCamelCase       :			int		=			1
 __lowerCamelCase       :			int		=			5

 # tx_args
 __lowerCamelCase       :			float		=			3E-5
 __lowerCamelCase       :			float		=			0.0
 __lowerCamelCase       :			int		=			20_000
 __lowerCamelCase       :			float		=			0.00_95

 __lowerCamelCase       :			str		=			"bigbird-roberta-natural-questions"
 __lowerCamelCase       :			str		=			"training-expt"
 __lowerCamelCase       :			str		=			"data/nq-training.jsonl"
 __lowerCamelCase       :			str		=			"data/nq-validation.jsonl"



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      os.makedirs(self.base_dir					,  exist_ok=A			)
      _a           =    os.path.join(self.base_dir					,  self.save_dir			)
      _a           =    self.batch_size_per_device * jax.device_count()






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			int
 __lowerCamelCase       :			int		=			4_096  # no dynamic padding on TPUs



 def __call__(self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.collate_fn(A			)
      _a           =    jax.tree_util.tree_map(A					,  A			)
      return batch



 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a    ,       _a           =    self.fetch_inputs(features['''input_ids''']			)
      _a           =    {
          '''input_ids''': jnp.array(A					,  dtype=jnp.intaa			),
          '''attention_mask''': jnp.array(A					,  dtype=jnp.intaa			),
          '''start_labels''': jnp.array(features['''start_token''']					,  dtype=jnp.intaa			),
          '''end_labels''': jnp.array(features['''end_token''']					,  dtype=jnp.intaa			),
          '''pooled_labels''': jnp.array(features['''category''']					,  dtype=jnp.intaa			),
      }
      return batch



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    [self._fetch_inputs(A			) for ids in input_ids]
      return zip(*A			)



 def    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    [1 for _ in range(len(A			)			)]
      while len(A			) < self.max_length:
           input_ids.append(self.pad_id			)
           attention_mask.append(0			)
      return input_ids, attention_mask





def lowerCAmelCase    (__A    ,      __A    ,      __A=None):



     """simple docstring"""



     if seed is not None:
          _a           =    dataset.shuffle(seed=__A)
     for i in range(len(__A) // batch_size):
          _a           =    dataset[i * batch_size : (i + 1) * batch_size]
          yield dict(__A)




@partial(jax.pmap    ,      axis_name='''batch''')
def lowerCAmelCase    (__A    ,      __A    ,      **__A):



     """simple docstring"""



     def loss_fn(__A):
          _a           =    model_inputs.pop('''start_labels''')
          _a           =    model_inputs.pop('''end_labels''')
          _a           =    model_inputs.pop('''pooled_labels''')

          _a           =    state.apply_fn(**__A    ,      params=__A    ,      dropout_rng=__A    ,      train=__A)
          _a    ,       _a    ,       _a           =    outputs

          return state.loss_fn(
              __A    ,      __A    ,      __A    ,      __A    ,      __A    ,      __A    ,      )

     _a    ,       _a           =    jax.random.split(__A)
     _a           =    jax.value_and_grad(__A)
     _a    ,       _a           =    grad_fn(state.params)
     _a           =    jax.lax.pmean({'''loss''': loss}    ,      axis_name='''batch''')
     _a           =    jax.lax.pmean(__A    ,      '''batch''')

     _a           =    state.apply_gradients(grads=__A)
     return state, metrics, new_drp_rng




@partial(jax.pmap    ,      axis_name='''batch''')
def lowerCAmelCase    (__A    ,      **__A):



     """simple docstring"""



     _a           =    model_inputs.pop('''start_labels''')
     _a           =    model_inputs.pop('''end_labels''')
     _a           =    model_inputs.pop('''pooled_labels''')

     _a           =    state.apply_fn(**__A    ,      params=state.params    ,      train=__A)
     _a    ,       _a    ,       _a           =    outputs

     _a           =    state.loss_fn(__A    ,      __A    ,      __A    ,      __A    ,      __A    ,      __A)
     _a           =    jax.lax.pmean({'''loss''': loss}    ,      axis_name='''batch''')
     return metrics






class __A     (    train_state.TrainState		):



 '''simple docstring'''







 __lowerCamelCase       :			Callable		=			struct.field(pytree_node=A		)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			Args
 __lowerCamelCase       :			Callable
 __lowerCamelCase       :			Callable
 __lowerCamelCase       :			Callable
 __lowerCamelCase       :			Callable
 __lowerCamelCase       :			wandb
 __lowerCamelCase       :			Callable		=			None



 def    a__    (self					,  A					,  A					,  A					,  A=None			)       ->	Tuple:


      """simple docstring"""



      _a           =    model.params
      _a           =    TrainState.create(
          apply_fn=model.__call__					,  params=A					,  tx=A					,  loss_fn=A					,  )
      if ckpt_dir is not None:
           _a    ,       _a    ,       _a    ,       _a    ,       _a           =    restore_checkpoint(A					,  A			)
           _a           =    {
               '''lr''': args.lr,
               '''init_lr''': args.init_lr,
               '''warmup_steps''': args.warmup_steps,
               '''num_train_steps''': num_train_steps,
               '''weight_decay''': args.weight_decay,
           }
           _a    ,       _a           =    build_tx(**A			)
           _a           =    train_state.TrainState(
               step=A					,  apply_fn=model.__call__					,  params=A					,  tx=A					,  opt_state=A					,  )
           _a           =    args
           _a           =    data_collator
           _a           =    lr
           _a           =    params
      _a           =    jax_utils.replicate(A			)
      return state



 def    a__    (self					,  A					,  A					,  A			)       ->	Dict:


      """simple docstring"""



      _a           =    self.args
      _a           =    len(A			) // args.batch_size

      _a           =    jax.random.PRNGKey(0			)
      _a           =    jax.random.split(A					,  jax.device_count()			)
      for epoch in range(args.max_epochs			):
           _a           =    jnp.array(0					,  dtype=jnp.floataa			)
           _a           =    get_batched_dataset(A					,  args.batch_size					,  seed=A			)
           _a           =    0
           for batch in tqdm(A					,  total=A					,  desc=f'''Running EPOCH-{epoch}'''			):
                _a           =    self.data_collator(A			)
                _a    ,       _a    ,       _a           =    self.train_step_fn(A					,  A					,  **A			)
                running_loss += jax_utils.unreplicate(metrics['''loss''']			)
                i += 1
                if i % args.logging_steps == 0:
                     _a           =    jax_utils.unreplicate(state.step			)
                     _a           =    running_loss.item() / i
                     _a           =    self.scheduler_fn(state_step - 1			)

                     _a           =    self.evaluate(A					,  A			)
                     _a           =    {
                         '''step''': state_step.item(),
                         '''eval_loss''': eval_loss.item(),
                         '''tr_loss''': tr_loss,
                         '''lr''': lr.item(),
                     }
                     tqdm.write(str(A			)			)
                     self.logger.log(A					,  commit=A			)

                if i % args.save_steps == 0:
                     self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}'''					,  state=A			)



 def    a__    (self					,  A					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    get_batched_dataset(A					,  self.args.batch_size			)
      _a           =    len(A			) // self.args.batch_size
      _a           =    jnp.array(0					,  dtype=jnp.floataa			)
      _a           =    0
      for batch in tqdm(A					,  total=A					,  desc='''Evaluating ... '''			):
           _a           =    self.data_collator(A			)
           _a           =    self.val_step_fn(A					,  **A			)
           running_loss += jax_utils.unreplicate(metrics['''loss''']			)
           i += 1
      return running_loss / i



 def    a__    (self					,  A					,  A			)       ->	Dict:


      """simple docstring"""



      _a           =    jax_utils.unreplicate(A			)
      print(f'''SAVING CHECKPOINT IN {save_dir}'''					,  end=''' ... '''			)
      self.model_save_fn(A					,  params=state.params			)
      with open(os.path.join(A					,  '''opt_state.msgpack'''			)					,  '''wb'''			) as f:
           f.write(to_bytes(state.opt_state			)			)
      joblib.dump(self.args					,  os.path.join(A					,  '''args.joblib'''			)			)
      joblib.dump(self.data_collator					,  os.path.join(A					,  '''data_collator.joblib'''			)			)
      with open(os.path.join(A					,  '''training_state.json'''			)					,  '''w'''			) as f:
           json.dump({'''step''': state.step.item()}					,  A			)
      print('''DONE'''			)





def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     print(F'''RESTORING CHECKPOINT FROM {save_dir}'''    ,      end=''' ... ''')
     with open(os.path.join(__A    ,      '''flax_model.msgpack''')    ,      '''rb''') as f:
          _a           =    from_bytes(state.params    ,      f.read())

     with open(os.path.join(__A    ,      '''opt_state.msgpack''')    ,      '''rb''') as f:
          _a           =    from_bytes(state.opt_state    ,      f.read())

     _a           =    joblib.load(os.path.join(__A    ,      '''args.joblib'''))
     _a           =    joblib.load(os.path.join(__A    ,      '''data_collator.joblib'''))

     with open(os.path.join(__A    ,      '''training_state.json''')    ,      '''r''') as f:
          _a           =    json.load(__A)
     _a           =    training_state['''step''']

     print('''DONE''')
     return params, opt_state, step, args, data_collator




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    num_train_steps - warmup_steps
     _a           =    optax.linear_schedule(init_value=__A    ,      end_value=__A    ,      transition_steps=__A)
     _a           =    optax.linear_schedule(init_value=__A    ,      end_value=1e-7    ,      transition_steps=__A)
     _a           =    optax.join_schedules(schedules=[warmup_fn, decay_fn]    ,      boundaries=[warmup_steps])
     return lr




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     def weight_decay_mask(__A):
          _a           =    traverse_util.flatten_dict(__A)
          _a           =    {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
          return traverse_util.unflatten_dict(__A)

     _a           =    scheduler_fn(__A    ,      __A    ,      __A    ,      __A)

     _a           =    optax.adamw(learning_rate=__A    ,      weight_decay=__A    ,      mask=__A)
     return tx, lr











import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  if is_vision_available():
    from transformers import OneFormerImageProcessor
    from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
    from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
  from PIL import Image




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case="shi-labs/oneformer_demo" )				->						Any:
       with open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) as f:
              _UpperCAmelCase							=    json.load(__snake_case )
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for key, info in class_info.items():
              _UpperCAmelCase							=    info["""name"""]
              class_names.append(info["""name"""] )
              if info["isthing"]:
                     thing_ids.append(int(__snake_case ) )
       _UpperCAmelCase							=    thing_ids
       _UpperCAmelCase							=    class_names
       return metadata
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]=7       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			Union[str, Any]=30       ,				lowerCamelCase		:			Optional[int]=400       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Union[str, Any]=255       ,				lowerCamelCase		:			Tuple="shi-labs/oneformer_demo"       ,				lowerCamelCase		:			Tuple="ade20k_panoptic.json"       ,				lowerCamelCase		:			Optional[Any]=10       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    class_info_file
         _UpperCAmelCase							=    prepare_metadata(lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    num_text
         _UpperCAmelCase							=    repo_path

         # for the post_process_functions
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    4
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    do_reduce_labels
         _UpperCAmelCase							=    ignore_index


  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """simple docstring"""
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "num_labels": self.num_labels,
             "do_reduce_labels": self.do_reduce_labels,
             "ignore_index": self.ignore_index,
             "class_info_file": self.class_info_file,
             "metadata": self.metadata,
             "num_text": self.num_text,
         }


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple=False							)						->	Any:


         """simple docstring"""
         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(lowerCamelCase       ,				Image.Image							):
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
                else:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[1], image.shape[2]
                if w < h:
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * h / w							)
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                elif w > h:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * w / h							)
                else:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]

         else:
                _UpperCAmelCase							=    []
                for image in image_inputs:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_expected_values([image]							)
                       expected_values.append((expected_height, expected_width)							)
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[0]							)[0]
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[1]							)[1]

         return expected_height, expected_width





  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         return OneFormerForUniversalSegmentationOutput(
             # +1 for null class
             class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)							)       ,				masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)							)       ,				)



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  _lowerCamelCase    =  image_processing_class


  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    OneFormerImageProcessorTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return self.image_processing_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""ignore_index"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""class_info_file"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""num_text"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""repo_path"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""metadata"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_reduce_labels"""							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processing_tester.batch_size,
                 self.image_processing_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				np.ndarray							)

         # Test not batched input
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processing_tester.batch_size,
                 self.image_processing_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				torch.Tensor							)

         # Test not batched input
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processing_tester.batch_size,
                 self.image_processing_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Union[str, Any]=False       ,				lowerCamelCase		:			Union[str, Any]="np"							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # prepare image and target
         _UpperCAmelCase							=    self.image_processing_tester.num_labels
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         if with_segmentation_maps:
                _UpperCAmelCase							=    num_labels
                if is_instance_map:
                       _UpperCAmelCase							=    list(range(lowerCamelCase							)							) * 2
                       _UpperCAmelCase							=    dict(enumerate(lowerCamelCase							)							)
                _UpperCAmelCase							=    [
                    np.random.randint(0       ,				high * 2       ,				(img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
                ]
                if segmentation_type == "pil":
                       _UpperCAmelCase							=    [Image.fromarray(lowerCamelCase							) for annotation in annotations]

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				lowerCamelCase       ,				return_tensors="""pt"""       ,				instance_id_to_semantic_id=lowerCamelCase       ,				pad_and_return_pixel_mask=lowerCamelCase       ,				)

         return inputs


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         def common(lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			List[Any]=None							):
                _UpperCAmelCase							=    self.comm_get_image_processor_inputs(
                    with_segmentation_maps=lowerCamelCase       ,				is_instance_map=lowerCamelCase       ,				segmentation_type=lowerCamelCase							)

                _UpperCAmelCase							=    inputs["""mask_labels"""]
                _UpperCAmelCase							=    inputs["""class_labels"""]
                _UpperCAmelCase							=    inputs["""pixel_values"""]
                _UpperCAmelCase							=    inputs["""text_inputs"""]

                # check the batch_size
                for mask_label, class_label, text_input in zip(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							):
                       self.assertEqual(mask_label.shape[0]       ,				class_label.shape[0]							)
                       # this ensure padding has happened
                       self.assertEqual(mask_label.shape[1:]       ,				pixel_values.shape[2:]							)
                       self.assertEqual(len(lowerCamelCase							)       ,				self.image_processing_tester.num_text							)

         common()
         common(is_instance_map=lowerCamelCase							)
         common(is_instance_map=lowerCamelCase       ,				segmentation_type="""pil"""							)
         common(is_instance_map=lowerCamelCase       ,				segmentation_type="""pil"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    np.zeros((20, 50)							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1

         _UpperCAmelCase							=    binary_mask_to_rle(lowerCamelCase							)
         self.assertEqual(len(lowerCamelCase							)       ,				4							)
         self.assertEqual(rle[0]       ,				21							)
         self.assertEqual(rle[1]       ,				45							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(
             num_labels=self.image_processing_tester.num_classes       ,				max_seq_length=77       ,				task_seq_length=77       ,				class_info_file="""ade20k_panoptic.json"""       ,				num_text=self.image_processing_tester.num_text       ,				repo_path="""shi-labs/oneformer_demo"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()

         _UpperCAmelCase							=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase							)

         self.assertEqual(len(lowerCamelCase							)       ,				self.image_processing_tester.batch_size							)
         self.assertEqual(
             segmentation[0].shape       ,				(
                 self.image_processing_tester.height,
                 self.image_processing_tester.width,
             )       ,				)

         _UpperCAmelCase							=    [(1, 4) for i in range(self.image_processing_tester.batch_size							)]
         _UpperCAmelCase							=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase       ,				target_sizes=lowerCamelCase							)

         self.assertEqual(segmentation[0].shape       ,				target_sizes[0]							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(
             num_labels=self.image_processing_tester.num_classes       ,				max_seq_length=77       ,				task_seq_length=77       ,				class_info_file="""ade20k_panoptic.json"""       ,				num_text=self.image_processing_tester.num_text       ,				repo_path="""shi-labs/oneformer_demo"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    image_processor.post_process_instance_segmentation(lowerCamelCase       ,				threshold=0							)

         self.assertTrue(len(lowerCamelCase							) == self.image_processing_tester.batch_size							)
         for el in segmentation:
                self.assertTrue("""segmentation""" in el							)
                self.assertTrue("""segments_info""" in el							)
                self.assertEqual(type(el["""segments_info"""]							)       ,				lowerCamelCase							)
                self.assertEqual(
                    el["""segmentation"""].shape       ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)





  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(
             num_labels=self.image_processing_tester.num_classes       ,				max_seq_length=77       ,				task_seq_length=77       ,				class_info_file="""ade20k_panoptic.json"""       ,				num_text=self.image_processing_tester.num_text       ,				repo_path="""shi-labs/oneformer_demo"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    image_processor.post_process_panoptic_segmentation(lowerCamelCase       ,				threshold=0							)

         self.assertTrue(len(lowerCamelCase							) == self.image_processing_tester.batch_size							)
         for el in segmentation:
                self.assertTrue("""segmentation""" in el							)
                self.assertTrue("""segments_info""" in el							)
                self.assertEqual(type(el["""segments_info"""]							)       ,				lowerCamelCase							)
                self.assertEqual(
                    el["""segmentation"""].shape       ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)