





import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import (
           MODEL_MAPPING,
           SegformerForImageClassification,
           SegformerForSemanticSegmentation,
           SegformerModel,
       )
       from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import SegformerImageProcessor




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):



 def 							UpperCamelCase     (				self					):
        A__		   =		self.config_class(**self.inputs_dict					)
        self.parent.assertTrue(hasattr(__lowerCamelCase,'''hidden_sizes'''					)					)
        self.parent.assertTrue(hasattr(__lowerCamelCase,'''num_attention_heads'''					)					)
        self.parent.assertTrue(hasattr(__lowerCamelCase,'''num_encoder_blocks'''					)					)




class        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase,__lowerCamelCase=13,__lowerCamelCase=64,__lowerCamelCase=3,__lowerCamelCase=4,__lowerCamelCase=[2, 2, 2, 2],__lowerCamelCase=[8, 4, 2, 1],__lowerCamelCase=[16, 32, 64, 128],__lowerCamelCase=[1, 4, 8, 16],__lowerCamelCase=[1, 2, 4, 8],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.02,__lowerCamelCase=3,__lowerCamelCase=None,):
        A__		   =		parent
        A__		   =		batch_size
        A__		   =		image_size
        A__		   =		num_channels
        A__		   =		num_encoder_blocks
        A__		   =		sr_ratios
        A__		   =		depths
        A__		   =		hidden_sizes
        A__		   =		downsampling_rates
        A__		   =		num_attention_heads
        A__		   =		is_training
        A__		   =		use_labels
        A__		   =		hidden_act
        A__		   =		hidden_dropout_prob
        A__		   =		attention_probs_dropout_prob
        A__		   =		initializer_range
        A__		   =		num_labels
        A__		   =		scope



 def 							UpperCamelCase     (				self					):
        A__		   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        A__		   =		None
        if self.use_labels:
               A__		   =		ids_tensor([self.batch_size, self.image_size, self.image_size],self.num_labels					)

        A__		   =		self.get_config()
        return config, pixel_values, labels



 def 							UpperCamelCase     (				self					):
        return SegformerConfig(
            image_size=self.image_size,num_channels=self.num_channels,num_encoder_blocks=self.num_encoder_blocks,depths=self.depths,hidden_sizes=self.hidden_sizes,num_attention_heads=self.num_attention_heads,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,initializer_range=self.initializer_range,)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		SegformerModel(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase					)
        A__		   =		A__		   =		self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		self.num_labels
        A__		   =		SegformerForSemanticSegmentation(__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase					)
        self.parent.assertEqual(
            result.logits.shape,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)					)
        A__		   =		model(__lowerCamelCase,labels=__lowerCamelCase					)
        self.parent.assertEqual(
            result.logits.shape,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)					)
        self.parent.assertGreater(result.loss,0.0					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		1
        A__		   =		SegformerForSemanticSegmentation(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		torch.randint(0,1,(self.batch_size, self.image_size, self.image_size)					).to(__lowerCamelCase					)
        A__		   =		model(__lowerCamelCase,labels=__lowerCamelCase					)
        self.parent.assertGreater(result.loss,0.0					)




 def 							UpperCamelCase     (				self					):
        A__		   =		self.prepare_config_and_inputs()
        A__   ,      A__   ,      A__		   =		config_and_inputs
        A__		   =		{'''pixel_values''': pixel_values}
        return config, inputs_dict




@require_torch
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      (
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE							=      (
     {
         '''feature-extraction''': SegformerModel,
         '''image-classification''': SegformerForImageClassification,
         '''image-segmentation''': SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE							=      True
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False



 def 							UpperCamelCase     (				self					):
        A__		   =		SegformerModelTester(self					)
        A__		   =		SegformerConfigTester(self,config_class=__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        self.config_tester.run_common_tests()



 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*__lowerCamelCase					)



 @unittest.skip('''SegFormer does not use inputs_embeds'''					)
 def 							UpperCamelCase     (				self					):
        pass



 @unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''					)
 def 							UpperCamelCase     (				self					):
        pass



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()

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

               A__		   =		['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		True

        for model_class in self.all_model_classes:
               A__		   =		True
               A__		   =		False
               A__		   =		True
               A__		   =		model_class(__lowerCamelCase					)
               model.to(__lowerCamelCase					)
               model.eval()
               with torch.no_grad():
                      A__		   =		model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase					)					)
               A__		   =		outputs.attentions

               A__		   =		sum(self.model_tester.depths					)
               self.assertEqual(len(__lowerCamelCase					),__lowerCamelCase					)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               A__		   =		True
               A__		   =		model_class(__lowerCamelCase					)
               model.to(__lowerCamelCase					)
               model.eval()
               with torch.no_grad():
                      A__		   =		model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase					)					)
               A__		   =		outputs.attentions

               self.assertEqual(len(__lowerCamelCase					),__lowerCamelCase					)

               # verify the first attentions (first block, first layer)
               A__		   =		(self.model_tester.image_size // 4) ** 2
               A__		   =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(attentions[0].shape[-3:]					),[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],)

               # verify the last attentions (last block, last layer)
               A__		   =		(self.model_tester.image_size // 32) ** 2
               A__		   =		(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
               self.assertListEqual(
                   list(attentions[-1].shape[-3:]					),[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],)
               A__		   =		len(__lowerCamelCase					)

               # Check attention is always last and order is fine
               A__		   =		True
               A__		   =		True
               A__		   =		model_class(__lowerCamelCase					)
               model.to(__lowerCamelCase					)
               model.eval()
               with torch.no_grad():
                      A__		   =		model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase					)					)

               self.assertEqual(out_len + 1,len(__lowerCamelCase					)					)

               A__		   =		outputs.attentions

               self.assertEqual(len(__lowerCamelCase					),__lowerCamelCase					)
               # verify the first attentions (first block, first layer)
               A__		   =		(self.model_tester.image_size // 4) ** 2
               A__		   =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]					),[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],)



 def 							UpperCamelCase     (				self					):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
               A__		   =		model_class(__lowerCamelCase					)
               model.to(__lowerCamelCase					)
               model.eval()

               with torch.no_grad():
                      A__		   =		model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase					)					)

               A__		   =		outputs.hidden_states

               A__		   =		self.model_tester.num_encoder_blocks
               self.assertEqual(len(__lowerCamelCase					),__lowerCamelCase					)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]					),[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ],)

        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__		   =		True
               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               A__		   =		True

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        if not self.model_tester.is_training:
               return

        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		True

        for model_class in self.all_model_classes:
               if model_class in get_values(__lowerCamelCase					):
                      continue

               A__		   =		model_class(__lowerCamelCase					)
               model.to(__lowerCamelCase					)
               model.train()
               A__		   =		self._prepare_for_class(__lowerCamelCase,__lowerCamelCase,return_labels=__lowerCamelCase					)
               A__		   =		model(**__lowerCamelCase					).loss
               loss.backward()



 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
 def 							UpperCamelCase     (				self					):
        pass




 @slow
 def 							UpperCamelCase     (				self					):
        for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__		   =		SegformerModel.from_pretrained(__lowerCamelCase					)
               self.assertIsNotNone(__lowerCamelCase					)


def       UpperCamelCase__(			)->List[str]:
       A__		   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       return image




@require_torch
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 @slow
 def 							UpperCamelCase     (				self					):
        # only resize + normalize
        A__		   =		SegformerImageProcessor(
            image_scale=(512, 512),keep_ratio=__lowerCamelCase,align=__lowerCamelCase,do_random_crop=__lowerCamelCase					)
        A__		   =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''					).to(
            __lowerCamelCase					)

        A__		   =		prepare_img()
        A__		   =		image_processor(images=__lowerCamelCase,return_tensors='''pt'''					)
        A__		   =		encoded_inputs.pixel_values.to(__lowerCamelCase					)

        with torch.no_grad():
               A__		   =		model(__lowerCamelCase					)

        A__		   =		torch.Size((1, model.config.num_labels, 128, 128)					)
        self.assertEqual(outputs.logits.shape,__lowerCamelCase					)

        A__		   =		torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]					).to(__lowerCamelCase					)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3],__lowerCamelCase,atol=1E-4					)					)



 @slow
 def 							UpperCamelCase     (				self					):
        # only resize + normalize
        A__		   =		SegformerImageProcessor(
            image_scale=(512, 512),keep_ratio=__lowerCamelCase,align=__lowerCamelCase,do_random_crop=__lowerCamelCase					)
        A__		   =		SegformerForSemanticSegmentation.from_pretrained(
            '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''					).to(__lowerCamelCase					)

        A__		   =		prepare_img()
        A__		   =		image_processor(images=__lowerCamelCase,return_tensors='''pt'''					)
        A__		   =		encoded_inputs.pixel_values.to(__lowerCamelCase					)

        with torch.no_grad():
               A__		   =		model(__lowerCamelCase					)

        A__		   =		torch.Size((1, model.config.num_labels, 128, 128)					)
        self.assertEqual(outputs.logits.shape,__lowerCamelCase					)

        A__		   =		torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]					).to(__lowerCamelCase					)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3],__lowerCamelCase,atol=1E-1					)					)




 @slow
 def 							UpperCamelCase     (				self					):
        # only resize + normalize
        A__		   =		SegformerImageProcessor(
            image_scale=(512, 512),keep_ratio=__lowerCamelCase,align=__lowerCamelCase,do_random_crop=__lowerCamelCase					)
        A__		   =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''					).to(
            __lowerCamelCase					)

        A__		   =		prepare_img()
        A__		   =		image_processor(images=__lowerCamelCase,return_tensors='''pt'''					)
        A__		   =		encoded_inputs.pixel_values.to(__lowerCamelCase					)

        with torch.no_grad():
               A__		   =		model(__lowerCamelCase					)

        A__		   =		outputs.logits.detach().cpu()

        A__		   =		image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase,target_sizes=[(500, 300)]					)
        A__		   =		torch.Size((500, 300)					)
        self.assertEqual(segmentation[0].shape,__lowerCamelCase					)

        A__		   =		image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase					)
        A__		   =		torch.Size((128, 128)					)
        self.assertEqual(segmentation[0].shape,__lowerCamelCase					)












import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                   ],)



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 							UpperCamelCase     (				self					):
        pass





