



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



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

          # for the post_process_functions
          UpperCamelCase    :  Union[str, Any]							=	2
          UpperCamelCase    :  Union[str, Any]							=	10
          UpperCamelCase    :  Any							=	10
          UpperCamelCase    :  Union[str, Any]							=	3
          UpperCamelCase    :  Optional[int]							=	4
          UpperCamelCase    :  List[Any]							=	num_labels
          UpperCamelCase    :  Optional[Any]							=	do_reduce_labels
          UpperCamelCase    :  Any							=	ignore_index
       def 					snake_case_       (			self       )     ->		List[Any]:
          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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=False       )     ->		Union[str, Any]:
          if not batched:
             UpperCamelCase    :  Optional[int]							=	image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_,			Image.Image       ):
                UpperCamelCase	,     UpperCamelCase    :  Tuple							=	image.size
             else:
                UpperCamelCase	,     UpperCamelCase    :  Tuple							=	image.shape[1], image.shape[2]
             if w < h:
                UpperCamelCase    :  str							=	int(self.size['shortest_edge'] * h / w       )
                UpperCamelCase    :  Dict							=	self.size['shortest_edge']
             elif w > h:
                UpperCamelCase    :  Optional[int]							=	self.size['shortest_edge']
                UpperCamelCase    :  Optional[Any]							=	int(self.size['shortest_edge'] * w / h       )
             else:
                UpperCamelCase    :  List[str]							=	self.size['shortest_edge']
                UpperCamelCase    :  Union[str, Any]							=	self.size['shortest_edge']

          else:
             UpperCamelCase    :  Optional[Any]							=	[]
             for image in image_inputs:
                UpperCamelCase	,     UpperCamelCase    :  Any							=	self.get_expected_values([image]       )
                expected_values.append((expected_height, expected_width)       )
             UpperCamelCase    :  str							=	max(SCREAMING_SNAKE_CASE_,			key=lambda SCREAMING_SNAKE_CASE_       : item[0]       )[0]
             UpperCamelCase    :  str							=	max(SCREAMING_SNAKE_CASE_,			key=lambda SCREAMING_SNAKE_CASE_       : item[1]       )[1]

          return expected_height, expected_width






       def 					snake_case_       (			self       )     ->		int:
          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 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       UpperCAmelCase__				:					List[Any]        =  image_processing_class
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  int							=	OneFormerImageProcessorTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self.image_processing_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[int]							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'image_mean'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'image_std'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'ignore_index'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'class_info_file'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'num_text'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'repo_path'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'metadata'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_reduce_labels'       )       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          pass
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          # Initialize image_processor
          UpperCamelCase    :  List[Any]							=	self.image_processing_class(**self.image_processor_dict       )
          # create random PIL images
          UpperCamelCase    :  Union[str, Any]							=	prepare_image_inputs(self.image_processing_tester,			equal_resolution=SCREAMING_SNAKE_CASE_       )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,			Image.Image       )

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

          UpperCamelCase	,     UpperCamelCase    :  Dict							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

          # Test batched
          UpperCamelCase	,     UpperCamelCase    :  Any							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_,			batched=SCREAMING_SNAKE_CASE_       )

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

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

          UpperCamelCase	,     UpperCamelCase    :  int							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

          # Test batched
          UpperCamelCase	,     UpperCamelCase    :  List[str]							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_,			batched=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	image_processor(
              SCREAMING_SNAKE_CASE_,			['semantic'] * len(SCREAMING_SNAKE_CASE_       ),			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       )     ->		Any:
          # Initialize image_processor
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )
          # create random PyTorch tensors
          UpperCamelCase    :  List[str]							=	prepare_image_inputs(self.image_processing_tester,			equal_resolution=SCREAMING_SNAKE_CASE_,			torchify=SCREAMING_SNAKE_CASE_       )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,			torch.Tensor       )

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

          UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

          # Test batched
          UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_,			batched=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	image_processor(
              SCREAMING_SNAKE_CASE_,			['semantic'] * len(SCREAMING_SNAKE_CASE_       ),			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,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_="np"       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	self.image_processing_class(**self.image_processor_dict       )
          # prepare image and target
          UpperCamelCase    :  Optional[int]							=	self.image_processing_tester.num_labels
          UpperCamelCase    :  List[str]							=	None
          UpperCamelCase    :  Dict							=	None
          UpperCamelCase    :  Any							=	prepare_image_inputs(self.image_processing_tester,			equal_resolution=SCREAMING_SNAKE_CASE_       )
          if with_segmentation_maps:
             UpperCamelCase    :  List[Any]							=	num_labels
             if is_instance_map:
                UpperCamelCase    :  Dict							=	list(range(SCREAMING_SNAKE_CASE_       )       ) * 2
                UpperCamelCase    :  Tuple							=	dict(enumerate(SCREAMING_SNAKE_CASE_       )       )
             UpperCamelCase    :  Optional[int]							=	[
                 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    :  Dict							=	[Image.fromarray(SCREAMING_SNAKE_CASE_       ) for annotation in annotations]

          UpperCamelCase    :  Tuple							=	image_processor(
              SCREAMING_SNAKE_CASE_,			['semantic'] * len(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			return_tensors='pt',			instance_id_to_semantic_id=SCREAMING_SNAKE_CASE_,			pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_,			)

          return inputs
       def 					snake_case_       (			self       )     ->		int:
          pass
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          def common(SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=None       ):
             UpperCamelCase    :  Union[str, Any]							=	self.comm_get_image_processor_inputs(
                 with_segmentation_maps=SCREAMING_SNAKE_CASE_,			is_instance_map=SCREAMING_SNAKE_CASE_,			segmentation_type=SCREAMING_SNAKE_CASE_       )

             UpperCamelCase    :  Dict							=	inputs['mask_labels']
             UpperCamelCase    :  Optional[int]							=	inputs['class_labels']
             UpperCamelCase    :  Tuple							=	inputs['pixel_values']
             UpperCamelCase    :  Optional[int]							=	inputs['text_inputs']

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

          common()
          common(is_instance_map=SCREAMING_SNAKE_CASE_       )
          common(is_instance_map=SCREAMING_SNAKE_CASE_,			segmentation_type='pil'       )
          common(is_instance_map=SCREAMING_SNAKE_CASE_,			segmentation_type='pil'       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  str							=	np.zeros((20, 50)       )
          UpperCamelCase    :  Optional[Any]							=	1
          UpperCamelCase    :  Dict							=	1
          UpperCamelCase    :  Any							=	1

          UpperCamelCase    :  Dict							=	binary_mask_to_rle(SCREAMING_SNAKE_CASE_       )
          self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			4       )
          self.assertEqual(rle[0],			21       )
          self.assertEqual(rle[1],			45       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  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',			)
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCamelCase    :  Optional[int]							=	fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_       )

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

          UpperCamelCase    :  List[Any]							=	[(1, 4) for i in range(self.image_processing_tester.batch_size       )]
          UpperCamelCase    :  int							=	fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_,			target_sizes=SCREAMING_SNAKE_CASE_       )

          self.assertEqual(segmentation[0].shape,			target_sizes[0]       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	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    :  Optional[int]							=	self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCamelCase    :  Optional[Any]							=	image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE_,			threshold=0       )

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






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[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',			)
          UpperCamelCase    :  int							=	self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCamelCase    :  List[Any]							=	image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE_,			threshold=0       )

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[Any]        =  "speech_to_text_2"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					Any        =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=1_0000,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1024,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  List[str]							=	d_model
          UpperCamelCase    :  List[str]							=	decoder_ffn_dim
          UpperCamelCase    :  Optional[Any]							=	decoder_layers
          UpperCamelCase    :  Any							=	decoder_attention_heads
          UpperCamelCase    :  Tuple							=	dropout
          UpperCamelCase    :  str							=	attention_dropout
          UpperCamelCase    :  str							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	init_std
          UpperCamelCase    :  Tuple							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	use_cache
          UpperCamelCase    :  Any							=	decoder_layers
          UpperCamelCase    :  Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          UpperCamelCase    :  Union[str, Any]							=	max_target_positions

          super().__init__(
              pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			decoder_start_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)



