



"""simple docstring"""





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  _lowerCAmelCase     (					UpperCamelCase__		):





		"""simple docstring"""






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







								'''simple docstring'''

								snake_case_ :			Any            =      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  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=1_3				,     _lowercase=6_4				,     _lowercase=3				,     _lowercase=4				,     _lowercase=[2, 2, 2, 2]				,     _lowercase=[8, 4, 2, 1]				,     _lowercase=[1_6, 3_2, 6_4, 1_2_8]				,     _lowercase=[1, 4, 8, 1_6]				,     _lowercase=[1, 2, 4, 8]				,     _lowercase=True				,     _lowercase=True				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=0.02				,     _lowercase=3				,     _lowercase=None				,     )			->    str:







								'''simple docstring'''

								snake_case_ :			str            =      parent
								snake_case_ :			List[str]            =      batch_size
								snake_case_ :			List[Any]            =      image_size
								snake_case_ :			Optional[Any]            =      num_channels
								snake_case_ :			str            =      num_encoder_blocks
								snake_case_ :			Optional[Any]            =      sr_ratios
								snake_case_ :			str            =      depths
								snake_case_ :			Optional[int]            =      hidden_sizes
								snake_case_ :			int            =      downsampling_rates
								snake_case_ :			Dict            =      num_attention_heads
								snake_case_ :			int            =      is_training
								snake_case_ :			Any            =      use_labels
								snake_case_ :			Optional[int]            =      hidden_act
								snake_case_ :			List[Any]            =      hidden_dropout_prob
								snake_case_ :			Any            =      attention_probs_dropout_prob
								snake_case_ :			Optional[Any]            =      initializer_range
								snake_case_ :			str            =      num_labels
								snake_case_ :			Union[str, Any]            =      scope





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







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								snake_case_ :			Optional[Any]            =      None
								if self.use_labels:
														snake_case_ :			List[str]            =      ids_tensor([self.batch_size, self.image_size, self.image_size]				,     self.num_labels )

								snake_case_ :			List[str]            =      self.get_config()
								return config, pixel_values, labels





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







								'''simple docstring'''

								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				,     _lowercase				,     _lowercase				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      SegformerModel(config=__lowerCamelCase )
								model.to(__lowerCamelCase )
								model.eval()
								snake_case_ :			Any            =      model(__lowerCamelCase )
								snake_case_ :			Optional[int]            =      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				,     _lowercase				,     _lowercase				,     _lowercase )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Dict            =      self.num_labels
								snake_case_ :			Tuple            =      SegformerForSemanticSegmentation(__lowerCamelCase )
								model.to(__lowerCamelCase )
								model.eval()
								snake_case_ :			List[Any]            =      model(__lowerCamelCase )
								self.parent.assertEqual(
								    result.logits.shape				,     (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
								snake_case_ :			Dict            =      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				,     _lowercase				,     _lowercase				,     _lowercase )			->    str:







								'''simple docstring'''

								snake_case_ :			str            =      1
								snake_case_ :			Tuple            =      SegformerForSemanticSegmentation(config=__lowerCamelCase )
								model.to(__lowerCamelCase )
								model.eval()
								snake_case_ :			Tuple            =      torch.randint(0				,     1				,     (self.batch_size, self.image_size, self.image_size) ).to(__lowerCamelCase )
								snake_case_ :			int            =      model(__lowerCamelCase				,     labels=__lowerCamelCase )
								self.parent.assertGreater(result.loss				,     0.0 )





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







								'''simple docstring'''

								snake_case_ :			int            =      self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_ :			Any            =      config_and_inputs
								snake_case_ :			Any            =      {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class  _lowerCAmelCase     (					UpperCamelCase__	,					UpperCamelCase__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							(
		    (
		        SegformerModel,
		        SegformerForSemanticSegmentation,
		        SegformerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_lowerCamelCase      	=							(
		    {
		        '''feature-extraction''': SegformerModel,
		        '''image-classification''': SegformerForImageClassification,
		        '''image-segmentation''': SegformerForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_lowerCamelCase      	=							True
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False





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







								'''simple docstring'''

								snake_case_ :			Tuple            =      SegformerModelTester(self )
								snake_case_ :			List[Any]            =      SegformerConfigTester(self				,     config_class=__lowerCamelCase )





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







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase )





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_binary_image_segmentation(*__lowerCamelCase )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      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 )			->    Union[str, Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""" )
		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								pass





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_ :			str            =      ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]				,     __lowerCamelCase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Any            =      self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_ :			str            =      True

								for model_class in self.all_model_classes:
														snake_case_ :			str            =      True
														snake_case_ :			Tuple            =      False
														snake_case_ :			str            =      True
														snake_case_ :			int            =      model_class(__lowerCamelCase )
														model.to(__lowerCamelCase )
														model.eval()
														with torch.no_grad():
																				snake_case_ :			Optional[Any]            =      model(**self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase ) )
														snake_case_ :			str            =      outputs.attentions

														snake_case_ :			Optional[int]            =      sum(self.model_tester.depths )
														self.assertEqual(len(__lowerCamelCase )				,     __lowerCamelCase )

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

														self.assertEqual(len(__lowerCamelCase )				,     __lowerCamelCase )

														# verify the first attentions (first block, first layer)
														snake_case_ :			Dict            =      (self.model_tester.image_size // 4) ** 2
														snake_case_ :			Optional[Any]            =      (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)
														snake_case_ :			Optional[Any]            =      (self.model_tester.image_size // 3_2) ** 2
														snake_case_ :			Optional[Any]            =      (self.model_tester.image_size // (3_2 * 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]				,     )
														snake_case_ :			int            =      len(__lowerCamelCase )

														# Check attention is always last and order is fine
														snake_case_ :			List[Any]            =      True
														snake_case_ :			Dict            =      True
														snake_case_ :			str            =      model_class(__lowerCamelCase )
														model.to(__lowerCamelCase )
														model.eval()
														with torch.no_grad():
																				snake_case_ :			Dict            =      model(**self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase ) )

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

														snake_case_ :			Tuple            =      outputs.attentions

														self.assertEqual(len(__lowerCamelCase )				,     __lowerCamelCase )
														# verify the first attentions (first block, first layer)
														snake_case_ :			Dict            =      (self.model_tester.image_size // 4) ** 2
														snake_case_ :			Any            =      (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 )			->    Any:







								'''simple docstring'''

								def check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase ):
														snake_case_ :			Dict            =      model_class(__lowerCamelCase )
														model.to(__lowerCamelCase )
														model.eval()

														with torch.no_grad():
																				snake_case_ :			Optional[Any]            =      model(**self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase ) )

														snake_case_ :			Dict            =      outputs.hidden_states

														snake_case_ :			Optional[Any]            =      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,
														    ]				,     )

								snake_case_      ,		snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs_for_common()

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

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														snake_case_ :			List[str]            =      True

														check_hidden_states_output(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								if not self.model_tester.is_training:
														return

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

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

														snake_case_ :			Dict            =      model_class(__lowerCamelCase )
														model.to(__lowerCamelCase )
														model.train()
														snake_case_ :			Optional[Any]            =      self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase				,     return_labels=__lowerCamelCase )
														snake_case_ :			List[str]            =      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 )			->    str:







								'''simple docstring'''

								pass





		@slow
		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_ :			int            =      SegformerModel.from_pretrained(__lowerCamelCase )
														self.assertIsNotNone(__lowerCamelCase )




def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			str            =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
						return image






@require_torch
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@slow
		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)				,     keep_ratio=__lowerCamelCase				,     align=__lowerCamelCase				,     do_random_crop=__lowerCamelCase )
								snake_case_ :			Any            =      SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
								    __lowerCamelCase )

								snake_case_ :			Dict            =      prepare_img()
								snake_case_ :			str            =      image_processor(images=__lowerCamelCase				,     return_tensors="""pt""" )
								snake_case_ :			Optional[int]            =      encoded_inputs.pixel_values.to(__lowerCamelCase )

								with torch.no_grad():
														snake_case_ :			Optional[Any]            =      model(__lowerCamelCase )

								snake_case_ :			Dict            =      torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8) )
								self.assertEqual(outputs.logits.shape				,     __lowerCamelCase )

								snake_case_ :			str            =      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 )			->    str:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)				,     keep_ratio=__lowerCamelCase				,     align=__lowerCamelCase				,     do_random_crop=__lowerCamelCase )
								snake_case_ :			Any            =      SegformerForSemanticSegmentation.from_pretrained(
								    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024""" ).to(__lowerCamelCase )

								snake_case_ :			Optional[Any]            =      prepare_img()
								snake_case_ :			List[Any]            =      image_processor(images=__lowerCamelCase				,     return_tensors="""pt""" )
								snake_case_ :			Optional[Any]            =      encoded_inputs.pixel_values.to(__lowerCamelCase )

								with torch.no_grad():
														snake_case_ :			str            =      model(__lowerCamelCase )

								snake_case_ :			List[str]            =      torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8) )
								self.assertEqual(outputs.logits.shape				,     __lowerCamelCase )

								snake_case_ :			Optional[Any]            =      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 )			->    str:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)				,     keep_ratio=__lowerCamelCase				,     align=__lowerCamelCase				,     do_random_crop=__lowerCamelCase )
								snake_case_ :			Dict            =      SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
								    __lowerCamelCase )

								snake_case_ :			str            =      prepare_img()
								snake_case_ :			Tuple            =      image_processor(images=__lowerCamelCase				,     return_tensors="""pt""" )
								snake_case_ :			List[Any]            =      encoded_inputs.pixel_values.to(__lowerCamelCase )

								with torch.no_grad():
														snake_case_ :			Tuple            =      model(__lowerCamelCase )

								snake_case_ :			Union[str, Any]            =      outputs.logits.detach().cpu()

								snake_case_ :			Tuple            =      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase				,     target_sizes=[(5_0_0, 3_0_0)] )
								snake_case_ :			Dict            =      torch.Size((5_0_0, 3_0_0) )
								self.assertEqual(segmentation[0].shape				,     __lowerCamelCase )

								snake_case_ :			int            =      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase )
								snake_case_ :			List[str]            =      torch.Size((1_2_8, 1_2_8) )
								self.assertEqual(segmentation[0].shape				,     __lowerCamelCase )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__: List[Any]							   =						{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Any							   =						[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       a__: Optional[Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





