


"""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      __A     (snake_case__):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Tuple       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		self.config_class(**self.inputs_dict       )
														self.parent.assertTrue(hasattr(lowercase_		,       """hidden_sizes"""       )       )
														self.parent.assertTrue(hasattr(lowercase_		,       """num_attention_heads"""       )       )
														self.parent.assertTrue(hasattr(lowercase_		,       """num_encoder_blocks"""       )       )





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Tuple=13		,       UpperCAmelCase_							:      List[Any]=64		,       UpperCAmelCase_							:      List[Any]=3		,       UpperCAmelCase_							:      Optional[Any]=4		,       UpperCAmelCase_							:      Dict=[2, 2, 2, 2]		,       UpperCAmelCase_							:      Optional[Any]=[8, 4, 2, 1]		,       UpperCAmelCase_							:      str=[16, 32, 64, 128]		,       UpperCAmelCase_							:      Optional[Any]=[1, 4, 8, 16]		,       UpperCAmelCase_							:      Tuple=[1, 2, 4, 8]		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]="gelu"		,       UpperCAmelCase_							:      Dict=0.1		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      str=0.02		,       UpperCAmelCase_							:      Optional[int]=3		,       UpperCAmelCase_							:      List[str]=None		,       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		image_size
														snake_case_           =		num_channels
														snake_case_           =		num_encoder_blocks
														snake_case_           =		sr_ratios
														snake_case_           =		depths
														snake_case_           =		hidden_sizes
														snake_case_           =		downsampling_rates
														snake_case_           =		num_attention_heads
														snake_case_           =		is_training
														snake_case_           =		use_labels
														snake_case_           =		hidden_act
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		initializer_range
														snake_case_           =		num_labels
														snake_case_           =		scope



							def 		lowerCAmelCase       (				self							:      Any       )					->Dict:



														"""simple docstring"""



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

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

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



							def 		lowerCAmelCase       (				self							:      Dict       )					->Union[str, 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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      str       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		SegformerModel(config=lowercase_       )
														model.to(lowercase_       )
														model.eval()
														snake_case_           =		model(lowercase_       )
														snake_case_           =		snake_case_           =		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 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Dict       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.num_labels
														snake_case_           =		SegformerForSemanticSegmentation(lowercase_       )
														model.to(lowercase_       )
														model.eval()
														snake_case_           =		model(lowercase_       )
														self.parent.assertEqual(
														    result.logits.shape		,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
														snake_case_           =		model(lowercase_		,       labels=lowercase_       )
														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 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      int       )					->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      str       )					->Dict:



														"""simple docstring"""



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





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Any							      =    (
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Union[str, Any]							      =    (
							    {
							        """feature-extraction""": SegformerModel,
							        """image-classification""": SegformerForImageClassification,
							        """image-segmentation""": SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__lowercase:		Optional[Any]							      =    True
							__lowercase:		Optional[int]							      =    False
							__lowercase:		int							      =    False
							__lowercase:		Union[str, Any]							      =    False



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		SegformerModelTester(self       )
														snake_case_           =		SegformerConfigTester(self		,       config_class=lowercase_       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase       (				self							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowercase_       )



							def 		lowerCAmelCase       (				self							:      Dict       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase_       )



							def 		lowerCAmelCase       (				self							:      Any       )					->Any:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_segmentation(*lowercase_       )



							@unittest.skip("""SegFormer does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Any       )					->str:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

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



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



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

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

																					snake_case_           =		sum(self.model_tester.depths       )
																					self.assertEqual(len(lowercase_       )		,       lowercase_       )

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

																					self.assertEqual(len(lowercase_       )		,       lowercase_       )

																					# verify the first attentions (first block, first layer)
																					snake_case_           =		(self.model_tester.image_size // 4) ** 2
																					snake_case_           =		(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_           =		(self.model_tester.image_size // 32) ** 2
																					snake_case_           =		(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]		,       )
																					snake_case_           =		len(lowercase_       )

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

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

																					snake_case_           =		outputs.attentions

																					self.assertEqual(len(lowercase_       )		,       lowercase_       )
																					# verify the first attentions (first block, first layer)
																					snake_case_           =		(self.model_tester.image_size // 4) ** 2
																					snake_case_           =		(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 		lowerCAmelCase       (				self							:      List[Any]       )					->List[Any]:



														"""simple docstring"""



														def check_hidden_states_output(UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       ):
																					snake_case_           =		model_class(lowercase_       )
																					model.to(lowercase_       )
																					model.eval()

																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(lowercase_		,       lowercase_       )       )

																					snake_case_           =		outputs.hidden_states

																					snake_case_           =		self.model_tester.num_encoder_blocks
																					self.assertEqual(len(lowercase_       )		,       lowercase_       )

																					# 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_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

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

																					check_hidden_states_output(lowercase_		,       lowercase_		,       lowercase_       )



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



														"""simple docstring"""



														if not self.model_tester.is_training:
																					return

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

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

																					snake_case_           =		model_class(lowercase_       )
																					model.to(lowercase_       )
																					model.train()
																					snake_case_           =		self._prepare_for_class(lowercase_		,       lowercase_		,       return_labels=lowercase_       )
																					snake_case_           =		model(**lowercase_       ).loss
																					loss.backward()



							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



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







def 						_a				(      )    ->				Union[str, Any]:
							snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_           =		SegformerImageProcessor(
														    image_scale=(512, 512)		,       keep_ratio=lowercase_		,       align=lowercase_		,       do_random_crop=lowercase_       )
														snake_case_           =		SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""       ).to(
														    lowercase_       )

														snake_case_           =		prepare_img()
														snake_case_           =		image_processor(images=lowercase_		,       return_tensors="""pt"""       )
														snake_case_           =		encoded_inputs.pixel_values.to(lowercase_       )

														with torch.no_grad():
																					snake_case_           =		model(lowercase_       )

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

														snake_case_           =		torch.tensor(
														    [
														        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
														        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
														        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
														    ]       ).to(lowercase_       )
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,       lowercase_		,       atol=1E-4       )       )



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



														"""simple docstring"""



														snake_case_           =		SegformerImageProcessor(
														    image_scale=(512, 512)		,       keep_ratio=lowercase_		,       align=lowercase_		,       do_random_crop=lowercase_       )
														snake_case_           =		SegformerForSemanticSegmentation.from_pretrained(
														    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""       ).to(lowercase_       )

														snake_case_           =		prepare_img()
														snake_case_           =		image_processor(images=lowercase_		,       return_tensors="""pt"""       )
														snake_case_           =		encoded_inputs.pixel_values.to(lowercase_       )

														with torch.no_grad():
																					snake_case_           =		model(lowercase_       )

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

														snake_case_           =		torch.tensor(
														    [
														        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
														        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
														        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
														    ]       ).to(lowercase_       )
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,       lowercase_		,       atol=1E-1       )       )



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



														"""simple docstring"""



														snake_case_           =		SegformerImageProcessor(
														    image_scale=(512, 512)		,       keep_ratio=lowercase_		,       align=lowercase_		,       do_random_crop=lowercase_       )
														snake_case_           =		SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""       ).to(
														    lowercase_       )

														snake_case_           =		prepare_img()
														snake_case_           =		image_processor(images=lowercase_		,       return_tensors="""pt"""       )
														snake_case_           =		encoded_inputs.pixel_values.to(lowercase_       )

														with torch.no_grad():
																					snake_case_           =		model(lowercase_       )

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

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

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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    [
				    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BioGptForCausalLM',
				    'BioGptForTokenClassification',
				    'BioGptForSequenceClassification',
				    'BioGptModel',
				    'BioGptPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
				from .tokenization_biogpt import BioGptTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_biogpt import (
								    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BioGptForCausalLM,
								    BioGptForSequenceClassification,
								    BioGptForTokenClassification,
								    BioGptModel,
								    BioGptPreTrainedModel,
								)


else:
				import sys

				__SCREAMING_SNAKE_CASE							:    Dict		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



