

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]="shi-labs/oneformer_demo"			):



     '''simple docstring'''





     with open(hf_hub_download(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     repo_type='dataset'			)	,     'r'			) as f:
          lowercase__    :   Optional[int]      =       json.load(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Union[str, Any]      =       {}
     lowercase__    :   List[str]      =       []
     lowercase__    :   Any      =       []
     for key, info in class_info.items():
          lowercase__    :   List[Any]      =       info['name']
          class_names.append(info['name']			)
          if info["isthing"]:
               thing_ids.append(int(SCREAMING_SNAKE_CASE_			)			)
     lowercase__    :   Optional[Any]      =       thing_ids
     lowercase__    :   Any      =       class_names
     return metadata


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def __init__(      self						,   a						,   a=7						,   a=3						,   a=30						,   a=400						,   a=None						,   a=True						,   a=True						,   a=[0.5, 0.5, 0.5]						,   a=[0.5, 0.5, 0.5]						,   a=10						,   a=False						,   a=255						,   a="shi-labs/oneformer_demo"						,   a="ade20k_panoptic.json"						,   a=10						,   ):
         lowercase__    :   int      =       parent
         lowercase__    :   Optional[Any]      =       batch_size
         lowercase__    :   int      =       num_channels
         lowercase__    :   Dict      =       min_resolution
         lowercase__    :   Union[str, Any]      =       max_resolution
         lowercase__    :   List[Any]      =       do_resize
         lowercase__    :   Optional[int]      =       {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
         lowercase__    :   Union[str, Any]      =       do_normalize
         lowercase__    :   Any      =       image_mean
         lowercase__    :   List[Any]      =       image_std
         lowercase__    :   Dict      =       class_info_file
         lowercase__    :   Union[str, Any]      =       prepare_metadata(a						,   a)
         lowercase__    :   Union[str, Any]      =       num_text
         lowercase__    :   int      =       repo_path

         # for the post_process_functions
         lowercase__    :   Any      =       2
         lowercase__    :   Tuple      =       10
         lowercase__    :   Tuple      =       10
         lowercase__    :   str      =       3
         lowercase__    :   Optional[int]      =       4
         lowercase__    :   int      =       num_labels
         lowercase__    :   Optional[Any]      =       do_reduce_labels
         lowercase__    :   Union[str, Any]      =       ignore_index





    def       snake_case_				(      self):
         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       snake_case_				(      self						,   a						,   a=False):
         if not batched:
              lowercase__    :   Optional[int]      =       image_inputs[0]
              if isinstance(a						,   Image.Image):
                   lowercase__      , lowercase__    :   str      =       image.size
              else:
                   lowercase__      , lowercase__    :   Tuple      =       image.shape[1], image.shape[2]
              if w < h:
                   lowercase__    :   Union[str, Any]      =       int(self.size['shortest_edge'] * h / w)
                   lowercase__    :   Union[str, Any]      =       self.size['shortest_edge']
              elif w > h:
                   lowercase__    :   Union[str, Any]      =       self.size['shortest_edge']
                   lowercase__    :   List[str]      =       int(self.size['shortest_edge'] * w / h)
              else:
                   lowercase__    :   Union[str, Any]      =       self.size['shortest_edge']
                   lowercase__    :   Dict      =       self.size['shortest_edge']

         else:
              lowercase__    :   Dict      =       []
              for image in image_inputs:
                   lowercase__      , lowercase__    :   List[str]      =       self.get_expected_values([image])
                   expected_values.append((expected_height, expected_width))
              lowercase__    :   Optional[int]      =       max(a						,   key=lambda a: item[0])[0]
              lowercase__    :   int      =       max(a						,   key=lambda a: item[1])[1]

         return expected_height, expected_width





    def       snake_case_				(      self):
         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__					(__snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Any														=     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    # only for test_image_processing_common.test_image_proc_to_json_string
    __lowerCamelCase						:       Optional[Any]														=     image_processing_class





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       OneFormerImageProcessorTester(self)





    @property
    def       snake_case_				(      self):
         return self.image_processing_tester.prepare_image_processor_dict()





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(a						,   'image_mean'))
         self.assertTrue(hasattr(a						,   'image_std'))
         self.assertTrue(hasattr(a						,   'do_normalize'))
         self.assertTrue(hasattr(a						,   'do_resize'))
         self.assertTrue(hasattr(a						,   'size'))
         self.assertTrue(hasattr(a						,   'ignore_index'))
         self.assertTrue(hasattr(a						,   'class_info_file'))
         self.assertTrue(hasattr(a						,   'num_text'))
         self.assertTrue(hasattr(a						,   'repo_path'))
         self.assertTrue(hasattr(a						,   'metadata'))
         self.assertTrue(hasattr(a						,   'do_reduce_labels'))





    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         # Initialize image_processor
         lowercase__    :   Tuple      =       self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__    :   Optional[Any]      =       prepare_image_inputs(self.image_processing_tester						,   equal_resolution=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   Image.Image)

         # Test not batched input
         lowercase__    :   Dict      =       image_processor(image_inputs[0]						,   ['semantic']						,   return_tensors='pt').pixel_values

         lowercase__      , lowercase__    :   str      =       self.image_processing_tester.get_expected_values(a)

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

         # Test batched
         lowercase__      , lowercase__    :   Optional[Any]      =       self.image_processing_tester.get_expected_values(a						,   batched=a)

         lowercase__    :   Optional[int]      =       image_processor(
             a						,   ['semantic'] * len(a)						,   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       snake_case_				(      self):
         # Initialize image_processor
         lowercase__    :   int      =       self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__    :   Union[str, Any]      =       prepare_image_inputs(self.image_processing_tester						,   equal_resolution=a						,   numpify=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   np.ndarray)

         # Test not batched input
         lowercase__    :   Optional[int]      =       image_processor(image_inputs[0]						,   ['semantic']						,   return_tensors='pt').pixel_values

         lowercase__      , lowercase__    :   Optional[int]      =       self.image_processing_tester.get_expected_values(a)

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

         # Test batched
         lowercase__      , lowercase__    :   Dict      =       self.image_processing_tester.get_expected_values(a						,   batched=a)

         lowercase__    :   str      =       image_processor(
             a						,   ['semantic'] * len(a)						,   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       snake_case_				(      self):
         # Initialize image_processor
         lowercase__    :   str      =       self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__    :   int      =       prepare_image_inputs(self.image_processing_tester						,   equal_resolution=a						,   torchify=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   torch.Tensor)

         # Test not batched input
         lowercase__    :   List[Any]      =       image_processor(image_inputs[0]						,   ['semantic']						,   return_tensors='pt').pixel_values

         lowercase__      , lowercase__    :   List[Any]      =       self.image_processing_tester.get_expected_values(a)

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

         # Test batched
         lowercase__      , lowercase__    :   Optional[Any]      =       self.image_processing_tester.get_expected_values(a						,   batched=a)

         lowercase__    :   Dict      =       image_processor(
             a						,   ['semantic'] * len(a)						,   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       snake_case_				(      self						,   a=False						,   a=False						,   a="np"):
         lowercase__    :   List[str]      =       self.image_processing_class(**self.image_processor_dict)
         # prepare image and target
         lowercase__    :   Any      =       self.image_processing_tester.num_labels
         lowercase__    :   Optional[Any]      =       None
         lowercase__    :   List[str]      =       None
         lowercase__    :   List[str]      =       prepare_image_inputs(self.image_processing_tester						,   equal_resolution=a)
         if with_segmentation_maps:
              lowercase__    :   str      =       num_labels
              if is_instance_map:
                   lowercase__    :   List[Any]      =       list(range(a)) * 2
                   lowercase__    :   Union[str, Any]      =       dict(enumerate(a))
              lowercase__    :   str      =       [
                  np.random.randint(0						,   high * 2						,   (img.size[1], img.size[0])).astype(np.uinta) for img in image_inputs
              ]
              if segmentation_type == "pil":
                   lowercase__    :   List[Any]      =       [Image.fromarray(a) for annotation in annotations]

         lowercase__    :   int      =       image_processor(
             a						,   ['semantic'] * len(a)						,   a						,   return_tensors='pt'						,   instance_id_to_semantic_id=a						,   pad_and_return_pixel_mask=a						,   )

         return inputs





    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         def common(a=False						,   a=None):
              lowercase__    :   int      =       self.comm_get_image_processor_inputs(
                  with_segmentation_maps=a						,   is_instance_map=a						,   segmentation_type=a)

              lowercase__    :   Tuple      =       inputs['mask_labels']
              lowercase__    :   Union[str, Any]      =       inputs['class_labels']
              lowercase__    :   Optional[Any]      =       inputs['pixel_values']
              lowercase__    :   Dict      =       inputs['text_inputs']

              # check the batch_size
              for mask_label, class_label, text_input in zip(a						,   a						,   a):
                   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(a)						,   self.image_processing_tester.num_text)

         common()
         common(is_instance_map=a)
         common(is_instance_map=a						,   segmentation_type='pil')
         common(is_instance_map=a						,   segmentation_type='pil')





    def       snake_case_				(      self):
         lowercase__    :   Optional[Any]      =       np.zeros((20, 50))
         lowercase__    :   Optional[Any]      =       1
         lowercase__    :   Optional[Any]      =       1
         lowercase__    :   Optional[int]      =       1

         lowercase__    :   Dict      =       binary_mask_to_rle(a)
         self.assertEqual(len(a)						,   4)
         self.assertEqual(rle[0]						,   21)
         self.assertEqual(rle[1]						,   45)





    def       snake_case_				(      self):
         lowercase__    :   str      =       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'						,   )
         lowercase__    :   Any      =       self.image_processing_tester.get_fake_oneformer_outputs()

         lowercase__    :   Optional[Any]      =       fature_extractor.post_process_semantic_segmentation(a)

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

         lowercase__    :   Tuple      =       [(1, 4) for i in range(self.image_processing_tester.batch_size)]
         lowercase__    :   List[Any]      =       fature_extractor.post_process_semantic_segmentation(a						,   target_sizes=a)

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





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       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'						,   )
         lowercase__    :   Dict      =       self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase__    :   Optional[int]      =       image_processor.post_process_instance_segmentation(a						,   threshold=0)

         self.assertTrue(len(a) == 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'])						,   a)
              self.assertEqual(
                  el['segmentation'].shape						,   (self.image_processing_tester.height, self.image_processing_tester.width))





    def       snake_case_				(      self):
         lowercase__    :   Any      =       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'						,   )
         lowercase__    :   int      =       self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase__    :   Any      =       image_processor.post_process_panoptic_segmentation(a						,   threshold=0)

         self.assertTrue(len(a) == 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'])						,   a)
              self.assertEqual(
                  el['segmentation'].shape						,   (self.image_processing_tester.height, self.image_processing_tester.width))




# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {}
snake_case_			  = {}
snake_case_			  = {}

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    type	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None	,     ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       aliases if aliases is not None else []
     if format_type in _FORMAT_TYPES:
          logger.warning(
              f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""			)
     lowercase__    :   Any      =       formatter_cls
     for alias in set(aliases + [format_type]			):
          if alias in _FORMAT_TYPES_ALIASES:
               logger.warning(
                   f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""			)
          lowercase__    :   int      =       format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Exception	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       aliases if aliases is not None else []
     for alias in set(aliases + [format_type]			):
          lowercase__    :   Any      =       unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
       from .torch_formatter import TorchFormatter

       _register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
       snake_case_			  = ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
       _register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
       from .tf_formatter import TFFormatter

       _register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
       snake_case_			  = ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
       _register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
       from .jax_formatter import JaxFormatter

       _register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
       snake_case_			  = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
       _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]			):



     '''simple docstring'''





     if format_type in _FORMAT_TYPES_ALIASES:
          return _FORMAT_TYPES_ALIASES[format_type]
     else:
          return format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]	,     **SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   Tuple      =       get_format_type_from_alias(SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
          raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
     else:
          raise ValueError(
              f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got '{format_type}'"""			)

