"""simple docstring"""







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a					:					List[Any]=			get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase      (   __lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				int   =		ReformerTokenizer
				UpperCAmelCase    :				Any   =		ReformerTokenizerFast
				UpperCAmelCase    :				Optional[int]   =		True
				UpperCAmelCase    :				Optional[int]   =		False
				UpperCAmelCase    :				Dict   =		True



				def  _lowercase					(self    :				Any)   ->     List[Any]:
						super().setUp()

						__snake_case      :							List[Any]						=			ReformerTokenizer(_a     ,  keep_accents=_a)
						tokenizer.save_pretrained(self.tmpdirname)



				def  _lowercase					(self    :				int)   ->     int:
						__snake_case      :							Optional[Any]						=			'<s>'
						__snake_case      :							Union[str, Any]						=			1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a)     ,  _a)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a)     ,  _a)



				def  _lowercase					(self    :				Tuple)   ->     int:
						__snake_case      :							str						=			list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]     ,  '<unk>')
						self.assertEqual(vocab_keys[1]     ,  '<s>')
						self.assertEqual(vocab_keys[-1]     ,  'j')
						self.assertEqual(len(_a)     ,  10_00)



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
						self.assertEqual(self.get_tokenizer().vocab_size     ,  10_00)



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
						if not self.test_rust_tokenizer:
								return

						__snake_case      :							Optional[int]						=			self.get_tokenizer()
						__snake_case      :							Optional[int]						=			self.get_rust_tokenizer()

						__snake_case      :							Union[str, Any]						=			'I was born in 92000, and this is falsé.'

						__snake_case      :							Dict						=			tokenizer.tokenize(_a)
						__snake_case      :							Any						=			rust_tokenizer.tokenize(_a)
						self.assertListEqual(_a     ,  _a)

						__snake_case      :							Union[str, Any]						=			tokenizer.encode(_a     ,  add_special_tokens=_a)
						__snake_case      :							Optional[Any]						=			rust_tokenizer.encode(_a     ,  add_special_tokens=_a)
						self.assertListEqual(_a     ,  _a)

						__snake_case      :							int						=			self.get_rust_tokenizer()
						__snake_case      :							str						=			tokenizer.encode(_a)
						__snake_case      :							Tuple						=			rust_tokenizer.encode(_a)
						self.assertListEqual(_a     ,  _a)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Union[str, Any]=15)   ->     Any:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__snake_case      :							Tuple						=			self.rust_tokenizer_class.from_pretrained(_a     ,  **_a)

										# Simple input
										__snake_case      :							List[Any]						=			'This is a simple input'
										__snake_case      :							Any						=			['This is a simple input 1', 'This is a simple input 2']
										__snake_case      :							List[str]						=			('This is a simple input', 'This is a pair')
										__snake_case      :							Optional[int]						=			[
										    ('This is a simple input 1', 'This is a simple input 2'),
										    ('This is a simple pair 1', 'This is a simple pair 2'),
										]

										# Simple input tests
										self.assertRaises(_a     ,  tokenizer_r.encode     ,  _a     ,  max_length=_a     ,  padding='max_length')

										# Simple input
										self.assertRaises(_a     ,  tokenizer_r.encode_plus     ,  _a     ,  max_length=_a     ,  padding='max_length')

										# Simple input
										self.assertRaises(
										    _a     ,  tokenizer_r.batch_encode_plus     ,  _a     ,  max_length=_a     ,  padding='max_length'     ,  )

										# Pair input
										self.assertRaises(_a     ,  tokenizer_r.encode     ,  _a     ,  max_length=_a     ,  padding='max_length')

										# Pair input
										self.assertRaises(_a     ,  tokenizer_r.encode_plus     ,  _a     ,  max_length=_a     ,  padding='max_length')

										# Pair input
										self.assertRaises(
										    _a     ,  tokenizer_r.batch_encode_plus     ,  _a     ,  max_length=_a     ,  padding='max_length'     ,  )



				def  _lowercase					(self    :				Optional[Any])   ->     str:
						pass



				def  _lowercase					(self    :				int)   ->     Optional[Any]:
						__snake_case      :							Dict						=			ReformerTokenizer(_a     ,  keep_accents=_a)

						__snake_case      :							str						=			tokenizer.tokenize('This is a test')
						self.assertListEqual(_a     ,  ['▁This', '▁is', '▁a', '▁t', 'est'])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(_a)     ,  [2_85, 46, 10, 1_70, 3_82]     ,  )

						__snake_case      :							Tuple						=			tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    _a     ,  [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '9',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        'é',
						        '.',
						    ]     ,  )
						__snake_case      :							Optional[Any]						=			tokenizer.convert_tokens_to_ids(_a)
						self.assertListEqual(
						    _a     ,  [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,  )

						__snake_case      :							List[str]						=			tokenizer.convert_ids_to_tokens(_a)
						self.assertListEqual(
						    _a     ,  [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '<unk>',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        '<unk>',
						        '.',
						    ]     ,  )



				@cached_property
				def  _lowercase					(self    :				List[Any])   ->     str:
						return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')



				@slow
				def  _lowercase					(self    :				Dict)   ->     Optional[Any]:
						__snake_case      :							List[str]						=			'Hello World!'
						__snake_case      :							Any						=			[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

						self.assertListEqual(_a     ,  self.big_tokenizer.encode(_a))



				@slow
				def  _lowercase					(self    :				List[Any])   ->     str:
						__snake_case      :							Dict						=			(
						    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'
						    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
						)
						__snake_case      :							Dict						=			[
						    1_08,
						    2_65,
						    24,
						    1_11,
						    4,
						    2_58,
						    1_56,
						    35,
						    28,
						    2_75,
						    3,
						    2_59,
						    2_97,
						    2_60,
						    84,
						    4,
						    35,
						    1_10,
						    44,
						    8,
						    2_59,
						    91,
						    2_68,
						    21,
						    11,
						    2_09,
						    2_74,
						    1_09,
						    2_66,
						    2_77,
						    1_17,
						    86,
						    93,
						    3_15,
						    2_58,
						    2_78,
						    2_58,
						    2_77,
						    2_58,
						    0,
						    2_58,
						    2_88,
						    2_58,
						    3_19,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    2_87,
						    2_58,
						    3_15,
						    2_58,
						    2_89,
						    2_58,
						    2_78,
						    99,
						    2_69,
						    2_66,
						    2_62,
						    8,
						    2_59,
						    2_41,
						    4,
						    2_17,
						    2_30,
						    2_68,
						    2_66,
						    55,
						    1_68,
						    1_06,
						    75,
						    1_93,
						    2_66,
						    2_23,
						    27,
						    49,
						    26,
						    2_82,
						    25,
						    2_64,
						    2_99,
						    19,
						    26,
						    0,
						    2_58,
						    2_77,
						    1_17,
						    86,
						    93,
						    1_76,
						    1_83,
						    2_70,
						    11,
						    2_62,
						    42,
						    61,
						    2_65,
						]

						self.assertListEqual(_a     ,  self.big_tokenizer.encode(_a))



				@require_torch
				@slow
				def  _lowercase					(self    :				int)   ->     Tuple:
						import torch

						from transformers import ReformerConfig, ReformerModel

						# Build sequence
						__snake_case      :							Dict						=			list(self.big_tokenizer.get_vocab().keys())[:10]
						__snake_case      :							Dict						=			' '.join(_a)
						__snake_case      :							Any						=			self.big_tokenizer.encode_plus(_a     ,  return_tensors='pt')
						__snake_case      :							Optional[Any]						=			self.big_tokenizer.batch_encode_plus([sequence, sequence]     ,  return_tensors='pt')

						__snake_case      :							List[str]						=			ReformerConfig()
						# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
						__snake_case      :							List[str]						=			encoded_sequence['input_ids'].shape
						__snake_case      :							Tuple						=			ReformerModel(_a)

						# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
								model(**_a)
								model(**_a)






				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
						# fmt: off
						__snake_case      :							int						=			{'input_ids': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# This tokenizer does not know some characters like ")".
						# That is the reason why we use very simple texts here.
						# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
						__snake_case      :							Optional[Any]						=			[
						    'This is a very simple sentence.',
						    'The quick brown fox jumps over the lazy dog.',
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=_a     ,  model_name='google/reformer-crime-and-punishment'     ,  revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'     ,  padding=_a     ,  sequences=_a     ,  )






"""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 		UpperCamelCase      (   lowercase						):



				def  _lowercase					(self    :				Any)   ->     List[Any]:
					__snake_case      :							Any						=			self.config_class(**self.inputs_dict)
					self.parent.assertTrue(hasattr(_A     ,  'hidden_sizes'))
					self.parent.assertTrue(hasattr(_A     ,  'num_attention_heads'))
					self.parent.assertTrue(hasattr(_A     ,  'num_encoder_blocks'))







class 		UpperCamelCase      :



				def __init__(self    :				Optional[int]     ,  _A    :				Any     ,  _A    :				str=13     ,  _A    :				List[str]=64     ,  _A    :				List[Any]=3     ,  _A    :				Any=4     ,  _A    :				List[str]=[2, 2, 2, 2]     ,  _A    :				Tuple=[8, 4, 2, 1]     ,  _A    :				List[str]=[16, 32, 64, 1_28]     ,  _A    :				int=[1, 4, 8, 16]     ,  _A    :				List[str]=[1, 2, 4, 8]     ,  _A    :				Dict=True     ,  _A    :				Any=True     ,  _A    :				List[str]="gelu"     ,  _A    :				Optional[int]=0.1     ,  _A    :				Union[str, Any]=0.1     ,  _A    :				List[Any]=0.02     ,  _A    :				str=3     ,  _A    :				int=None     ,  )   ->     List[Any]:
					__snake_case      :							int						=			parent
					__snake_case      :							List[Any]						=			batch_size
					__snake_case      :							Optional[int]						=			image_size
					__snake_case      :							List[str]						=			num_channels
					__snake_case      :							Any						=			num_encoder_blocks
					__snake_case      :							Dict						=			sr_ratios
					__snake_case      :							Any						=			depths
					__snake_case      :							Tuple						=			hidden_sizes
					__snake_case      :							Tuple						=			downsampling_rates
					__snake_case      :							Union[str, Any]						=			num_attention_heads
					__snake_case      :							Optional[int]						=			is_training
					__snake_case      :							Any						=			use_labels
					__snake_case      :							List[Any]						=			hidden_act
					__snake_case      :							Union[str, Any]						=			hidden_dropout_prob
					__snake_case      :							Tuple						=			attention_probs_dropout_prob
					__snake_case      :							List[Any]						=			initializer_range
					__snake_case      :							Union[str, Any]						=			num_labels
					__snake_case      :							Dict						=			scope



				def  _lowercase					(self    :				List[Any])   ->     Tuple:
					__snake_case      :							Optional[Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

					__snake_case      :							Dict						=			self.get_config()
					return config, pixel_values, labels



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					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  _lowercase					(self    :				List[str]     ,  _A    :				Union[str, Any]     ,  _A    :				List[Any]     ,  _A    :				Union[str, Any])   ->     int:
					__snake_case      :							Union[str, Any]						=			SegformerModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case      :							str						=			model(_A)
					__snake_case      :							List[Any]						=			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  _lowercase					(self    :				Tuple     ,  _A    :				Dict     ,  _A    :				Any     ,  _A    :				int)   ->     str:
					__snake_case      :							Any						=			self.num_labels
					__snake_case      :							List[str]						=			SegformerForSemanticSegmentation(_A)
					model.to(_A)
					model.eval()
					__snake_case      :							Dict						=			model(_A)
					self.parent.assertEqual(
					    result.logits.shape     ,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
					__snake_case      :							Dict						=			model(_A     ,  labels=_A)
					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  _lowercase					(self    :				Optional[Any]     ,  _A    :				Dict     ,  _A    :				Optional[int]     ,  _A    :				str)   ->     List[Any]:
					__snake_case      :							List[Any]						=			1
					__snake_case      :							str						=			SegformerForSemanticSegmentation(config=_A)
					model.to(_A)
					model.eval()
					__snake_case      :							Any						=			torch.randint(0     ,  1     ,  (self.batch_size, self.image_size, self.image_size)).to(_A)
					__snake_case      :							Any						=			model(_A     ,  labels=_A)
					self.parent.assertGreater(result.loss     ,  0.0)






				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			self.prepare_config_and_inputs()
					__snake_case		,    __snake_case		,    __snake_case      :							str						=			config_and_inputs
					__snake_case      :							List[str]						=			{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class 		UpperCamelCase      (   lowercase				,     lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				List[str]   =		(
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase    :				Dict   =		(
				    {
				        """feature-extraction""": SegformerModel,
				        """image-classification""": SegformerForImageClassification,
				        """image-segmentation""": SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase    :				Optional[int]   =		True
				UpperCAmelCase    :				Optional[int]   =		False
				UpperCAmelCase    :				List[Any]   =		False
				UpperCAmelCase    :				List[Any]   =		False



				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					__snake_case      :							Optional[int]						=			SegformerModelTester(self)
					__snake_case      :							Any						=			SegformerConfigTester(self     ,  config_class=_A)



				def  _lowercase					(self    :				List[str])   ->     Optional[int]:
					self.config_tester.run_common_tests()



				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					__snake_case      :							int						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def  _lowercase					(self    :				Optional[int])   ->     str:
					__snake_case      :							List[Any]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_binary_image_segmentation(*_A)



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
					__snake_case      :							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_segmentation(*_A)



				@unittest.skip('SegFormer does not use inputs_embeds')
				def  _lowercase					(self    :				Union[str, Any])   ->     str:
					pass



				@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
				def  _lowercase					(self    :				int)   ->     str:
					pass



				def  _lowercase					(self    :				List[str])   ->     Any:
					__snake_case		,    __snake_case      :							Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

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

						__snake_case      :							Optional[int]						=			['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  _A)



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					__snake_case		,    __snake_case      :							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							Optional[int]						=			True

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

						__snake_case      :							int						=			sum(self.model_tester.depths)
						self.assertEqual(len(_A)     ,  _A)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__snake_case      :							int						=			True
						__snake_case      :							Union[str, Any]						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							List[str]						=			model(**self._prepare_for_class(_A     ,  _A))
						__snake_case      :							Optional[int]						=			outputs.attentions

						self.assertEqual(len(_A)     ,  _A)

						# verify the first attentions (first block, first layer)
						__snake_case      :							Optional[int]						=			(self.model_tester.image_size // 4) ** 2
						__snake_case      :							Tuple						=			(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      :							int						=			(self.model_tester.image_size // 32) ** 2
						__snake_case      :							Any						=			(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      :							int						=			len(_A)

						# Check attention is always last and order is fine
						__snake_case      :							Any						=			True
						__snake_case      :							Tuple						=			True
						__snake_case      :							Optional[Any]						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							Any						=			model(**self._prepare_for_class(_A     ,  _A))

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

						__snake_case      :							List[Any]						=			outputs.attentions

						self.assertEqual(len(_A)     ,  _A)
						# verify the first attentions (first block, first layer)
						__snake_case      :							Any						=			(self.model_tester.image_size // 4) ** 2
						__snake_case      :							List[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  _lowercase					(self    :				str)   ->     List[str]:
					def check_hidden_states_output(_A    :				Union[str, Any]     ,  _A    :				List[str]     ,  _A    :				Tuple):
						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.eval()

						with torch.no_grad():
							__snake_case      :							Optional[int]						=			model(**self._prepare_for_class(_A     ,  _A))

						__snake_case      :							List[str]						=			outputs.hidden_states

						__snake_case      :							Tuple						=			self.model_tester.num_encoder_blocks
						self.assertEqual(len(_A)     ,  _A)

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

					for model_class in self.all_model_classes:
						__snake_case      :							List[str]						=			True
						check_hidden_states_output(_A     ,  _A     ,  _A)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__snake_case      :							Any						=			True

						check_hidden_states_output(_A     ,  _A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     int:
					if not self.model_tester.is_training:
						return

					__snake_case		,    __snake_case      :							Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							int						=			True

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

						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.train()
						__snake_case      :							str						=			self._prepare_for_class(_A     ,  _A     ,  return_labels=_A)
						__snake_case      :							Dict						=			model(**_A).loss
						loss.backward()



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






				@slow
				def  _lowercase					(self    :				Any)   ->     List[str]:
					for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							List[Any]						=			SegformerModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







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

	'''simple docstring'''





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

@require_torch
class 		UpperCamelCase      (   unittest.TestCase						):



				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					# only resize + normalize
					__snake_case      :							List[str]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Tuple						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							List[str]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[int]						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							Tuple						=			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(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-4))



				@slow
				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# only resize + normalize
					__snake_case      :							int						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained(
					    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(_A)

					__snake_case      :							str						=			prepare_img()
					__snake_case      :							Union[str, Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							str						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Any						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							List[Any]						=			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(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-1))






				@slow
				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					# only resize + normalize
					__snake_case      :							List[Any]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Optional[Any]						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Optional[Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							Union[str, Any]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[Any]						=			outputs.logits.detach().cpu()

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A     ,  target_sizes=[(5_00, 3_00)])
					__snake_case      :							Any						=			torch.Size((5_00, 3_00))
					self.assertEqual(segmentation[0].shape     ,  _A)

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A)
					__snake_case      :							str						=			torch.Size((1_28, 1_28))
					self.assertEqual(segmentation[0].shape     ,  _A)





