
'''simple docstring'''




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=100		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=30		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=[0, 1, 2, 3]		,     )					->							List[str]:






         """simple docstring"""






         A			:  Optional[Any]   =		parent
         A			:  str   =		100
         A			:  Any   =		batch_size
         A			:  int   =		image_size
         A			:  str   =		patch_size
         A			:  Dict   =		num_channels
         A			:  Optional[Any]   =		is_training
         A			:  List[str]   =		use_labels
         A			:  str   =		hidden_size
         A			:  Optional[int]   =		num_hidden_layers
         A			:  str   =		num_attention_heads
         A			:  Optional[int]   =		intermediate_size
         A			:  int   =		hidden_act
         A			:  List[str]   =		hidden_dropout_prob
         A			:  Union[str, Any]   =		attention_probs_dropout_prob
         A			:  Optional[Any]   =		type_sequence_label_size
         A			:  Union[str, Any]   =		initializer_range
         A			:  Optional[Any]   =		scope
         A			:  str   =		out_indices
         A			:  Optional[int]   =		num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         A			:  Optional[Any]   =		(image_size // patch_size) ** 2
         A			:  Optional[int]   =		num_patches + 1





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  Optional[Any]   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         A			:  List[str]   =		None
         A			:  str   =		None
         if self.use_labels:
             A			:  Union[str, Any]   =		ids_tensor([self.batch_size]		,     self.type_sequence_label_size      )
             A			:  Tuple   =		ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels      )

         A			:  Dict   =		self.get_config()

         return config, pixel_values, labels, pixel_labels





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         return BeitConfig(
             vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=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		,     is_decoder=SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  str   =		BeitModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  Union[str, Any]   =		BeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length - 1, self.vocab_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  Dict   =		self.type_sequence_label_size
         A			:  List[Any]   =		BeitForImageClassification(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Tuple   =		model(SCREAMING_SNAKE_CASE		,     labels=SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         A			:  int   =		1
         A			:  Optional[int]   =		BeitForImageClassification(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         A			:  Tuple   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE		,     labels=SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		self.num_labels
         A			:  Dict   =		BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Tuple   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
         A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE		,     labels=SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  str   =		self.prepare_config_and_inputs()
         A,    A,    A,    A			:  str   =		config_and_inputs
         A			:  List[str]   =		{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __magic_name__          =						(
         {
             '''feature-extraction''': BeitModel,
             '''image-classification''': BeitForImageClassification,
             '''image-segmentation''': BeitForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





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






         """simple docstring"""






         A			:  int   =		BeitModelTester(self      )
         A			:  Any   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     has_text_modality=SCREAMING_SNAKE_CASE		,     hidden_size=37      )





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






         """simple docstring"""






         self.config_tester.run_common_tests()





     @unittest.skip(reason='''BEiT does not use inputs_embeds'''      )
     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         pass





     @require_torch_multi_gpu
     @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A,    A			:  str   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  Union[str, Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
             A			:  Dict   =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,     nn.Linear      )      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  List[str]   =		model_class(SCREAMING_SNAKE_CASE      )
             A			:  List[Any]   =		inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A			:  List[Any]   =		[*signature.parameters.keys()]

             A			:  int   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  str   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         if not self.model_tester.is_training:
             return

         A,    A			:  Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  List[str]   =		True

         for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if model_class in [*get_values(SCREAMING_SNAKE_CASE      ), BeitForMaskedImageModeling]:
                 continue

             A			:  Dict   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.train()
             A			:  Dict   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  Dict   =		model(**SCREAMING_SNAKE_CASE      ).loss
             loss.backward()





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






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         A			:  Union[str, Any]   =		False
         A			:  Tuple   =		True

         for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if (
                 model_class in [*get_values(SCREAMING_SNAKE_CASE      ), BeitForMaskedImageModeling]
                 or not model_class.supports_gradient_checkpointing
             ):
                 continue

             A			:  Union[str, Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.gradient_checkpointing_enable()
             model.to(SCREAMING_SNAKE_CASE      )
             model.train()
             A			:  List[Any]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  Any   =		model(**SCREAMING_SNAKE_CASE      ).loss
             loss.backward()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

         A			:  str   =		_config_zero_init(SCREAMING_SNAKE_CASE      )
         for model_class in self.all_model_classes:
             A			:  List[str]   =		model_class(config=SCREAMING_SNAKE_CASE      )
             for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                     continue
                 if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,     )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  Optional[int]   =		BeitModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Tuple   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image







@require_torch
@require_vision
class 					A							(				unittest.TestCase		):





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  str   =		BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      ).to(SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		self.default_image_processor
         A			:  int   =		prepare_img()
         A			:  Any   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE      )

         # prepare bool_masked_pos
         A			:  Tuple   =		torch.ones((1, 196)		,     dtype=torch.bool      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  Any   =		model(pixel_values=SCREAMING_SNAKE_CASE		,     bool_masked_pos=SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		outputs.logits

         # verify the logits
         A			:  List[Any]   =		torch.Size((1, 196, 8192)      )
         self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  Any   =		torch.tensor(
             [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      ).to(SCREAMING_SNAKE_CASE      )

         self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-2      )      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Optional[int]   =		BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      ).to(SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		self.default_image_processor
         A			:  List[Any]   =		prepare_img()
         A			:  str   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  Tuple   =		model(**SCREAMING_SNAKE_CASE      )
         A			:  Any   =		outputs.logits

         # verify the logits
         A			:  Union[str, Any]   =		torch.Size((1, 1000)      )
         self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  Dict   =		torch.tensor([-1.2_385, -1.0_987, -1.0_108]      ).to(SCREAMING_SNAKE_CASE      )

         self.assertTrue(torch.allclose(logits[0, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )

         A			:  List[Any]   =		281
         self.assertEqual(logits.argmax(-1      ).item()		,     SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Optional[int]   =		BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      ).to(
             SCREAMING_SNAKE_CASE      )

         A			:  Dict   =		self.default_image_processor
         A			:  str   =		prepare_img()
         A			:  Dict   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  List[str]   =		model(**SCREAMING_SNAKE_CASE      )
         A			:  int   =		outputs.logits

         # verify the logits
         A			:  int   =		torch.Size((1, 21841)      )
         self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  Optional[Any]   =		torch.tensor([1.6_881, -0.2_787, 0.5_901]      ).to(SCREAMING_SNAKE_CASE      )

         self.assertTrue(torch.allclose(logits[0, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )

         A			:  int   =		2396
         self.assertEqual(logits.argmax(-1      ).item()		,     SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  str   =		BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
         A			:  Optional[int]   =		model.to(SCREAMING_SNAKE_CASE      )

         A			:  int   =		BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE		,     size=640		,     do_center_crop=SCREAMING_SNAKE_CASE      )

         A			:  int   =		load_dataset('''hf-internal-testing/fixtures_ade20k'''		,     split='''test'''      )
         A			:  Union[str, Any]   =		Image.open(ds[0]['''file''']      )
         A			:  Tuple   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		outputs.logits

         # verify the logits
         A			:  List[Any]   =		torch.Size((1, 150, 160, 160)      )
         self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		version.parse(PIL.__version__      ) < version.parse('''9.0.0'''      )

         if is_pillow_less_than_a:
             A			:  Tuple   =		torch.tensor(
                 [
                     [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                     [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                     [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                 ]		,     device=SCREAMING_SNAKE_CASE		,     )
         else:
             A			:  Union[str, Any]   =		torch.tensor(
                 [
                     [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
                     [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
                     [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
                 ]		,     device=SCREAMING_SNAKE_CASE		,     )

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
         A			:  Optional[Any]   =		model.to(SCREAMING_SNAKE_CASE      )

         A			:  Optional[Any]   =		BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE		,     size=640		,     do_center_crop=SCREAMING_SNAKE_CASE      )

         A			:  Union[str, Any]   =		load_dataset('''hf-internal-testing/fixtures_ade20k'''		,     split='''test'''      )
         A			:  Any   =		Image.open(ds[0]['''file''']      )
         A			:  Dict   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  List[str]   =		model(**SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		outputs.logits.detach().cpu()

         A			:  Any   =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE		,     target_sizes=[(500, 300)]      )
         A			:  Dict   =		torch.Size((500, 300)      )
         self.assertEqual(segmentation[0].shape		,     SCREAMING_SNAKE_CASE      )

         A			:  str   =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		torch.Size((160, 160)      )
         self.assertEqual(segmentation[0].shape		,     SCREAMING_SNAKE_CASE      )






'''simple docstring'''




import fire

from utils import calculate_rouge, save_json







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__=None						,							**snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		[x.strip() for x in open(snake_case__		).readlines()]
    A			:  Tuple   =		[x.strip() for x in open(snake_case__		).readlines()][: len(snake_case__		)]
    A			:  Union[str, Any]   =		calculate_rouge(snake_case__						,							snake_case__						,							**snake_case__		)
    if save_path is not None:
        save_json(snake_case__						,							snake_case__						,							indent=snake_case__		)
    return metrics  # these print nicely


if __name__ == "__main__":
      fire.Fire(calculate_rouge_path)




