




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_		:					Any          =  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_		:					int          =  250004
UpperCAmelCase_		:					List[str]          =  250020




@require_sentencepiece
@require_tokenizers
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       MBartTokenizer
						UpperCamelCase      =       MBartTokenizerFast
						UpperCamelCase      =       True
						UpperCamelCase      =       True

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__						:						List[str]										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)
										tokenizer.save_pretrained(self.tmpdirname			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)

										__magic_name__						:						int										=tokenizer.tokenize("""This is a test"""			)
										self.assertListEqual(__snake_case     ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__snake_case			)     ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,			)

										__magic_name__						:						Optional[int]										=tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
										self.assertListEqual(
										    __snake_case     ,			[
										        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""",
										        """.""",
										    ]     ,			)
										__magic_name__						:						Optional[Any]										=tokenizer.convert_tokens_to_ids(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]     ,			)

										__magic_name__						:						Any										=tokenizer.convert_ids_to_tokens(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        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>""",
										        """.""",
										    ]     ,			)


						def      A__    (		self :Tuple			):
										'''simple docstring'''

										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										__magic_name__						:						Tuple										=(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
																		__magic_name__						:						List[Any]										=self.rust_tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)
																		__magic_name__						:						List[str]										=self.tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)

																		__magic_name__						:						Optional[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
																		__magic_name__						:						int										=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						List[Any]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=True
																		__magic_name__						:						List[str]										=tempfile.mkdtemp()

																		__magic_name__						:						Optional[int]										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						int										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=False
																		__magic_name__						:						List[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						List[str]										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						str										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						Optional[int]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		__A   (      unittest.TestCase      ):
						UpperCamelCase      =       """facebook/mbart-large-en-ro"""
						UpperCamelCase      =       [
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						UpperCamelCase      =       [
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						UpperCamelCase      =       [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]

						@classmethod
						def      A__    (		cls :str			):
										'''simple docstring'''

										__magic_name__						:						MBartTokenizer										=MBartTokenizer.from_pretrained(
										    cls.checkpoint_name     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""			)
										__magic_name__						:						Any										=1
										return cls

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]     ,			25_00_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]     ,			25_00_04			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]     ,			25_00_20			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										self.assertIn(__snake_case     ,			self.tokenizer.all_special_ids			)
										__magic_name__						:						Union[str, Any]										=[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										__magic_name__						:						Optional[int]										=self.tokenizer.decode(__snake_case     ,			skip_special_tokens=__snake_case			)
										__magic_name__						:						List[str]										=self.tokenizer.decode(generated_ids[1:]     ,			skip_special_tokens=__snake_case			)
										self.assertEqual(__snake_case     ,			__snake_case			)
										self.assertNotIn(self.tokenizer.eos_token     ,			__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						str										=["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]     ,			__snake_case			)
										__magic_name__						:						Dict										=10
										__magic_name__						:						Optional[Any]										=self.tokenizer(__snake_case     ,			max_length=__snake_case     ,			truncation=__snake_case			).input_ids[0]
										self.assertEqual(ids[-2]     ,			2			)
										self.assertEqual(ids[-1]     ,			__snake_case			)
										self.assertEqual(len(__snake_case			)     ,			__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)     ,			[25_00_26, 25_00_01]			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=tempfile.mkdtemp()
										__magic_name__						:						Dict										=self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__snake_case			)
										__magic_name__						:						Dict										=MBartTokenizer.from_pretrained(__snake_case			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,			__snake_case			)

						@require_torch
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer(self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										__magic_name__						:						str										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

						@require_torch
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.tokenizer(
										    self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=len(self.expected_src_tokens			)     ,			return_tensors="""pt"""     ,			)

										__magic_name__						:						Any										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										self.assertIsInstance(__snake_case     ,			__snake_case			)

										self.assertEqual((2, 14)     ,			batch.input_ids.shape			)
										self.assertEqual((2, 14)     ,			batch.attention_mask.shape			)
										__magic_name__						:						int										=batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)
										self.assertEqual(2     ,			batch.decoder_input_ids[0, -1]			)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens     ,			[]			)
										self.assertEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id, EN_CODE]			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.tokenizer(self.src_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=3     ,			return_tensors="""pt"""			)
										__magic_name__						:						Tuple										=self.tokenizer(
										    text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=10     ,			return_tensors="""pt"""			)
										__magic_name__						:						List[Any]										=targets["""input_ids"""]
										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			self.tokenizer.pad_token_id			)

										self.assertEqual(batch.input_ids.shape[1]     ,			3			)
										self.assertEqual(batch.decoder_input_ids.shape[1]     ,			10			)


						@require_torch
						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.tokenizer._build_translation_inputs(
										    """A test"""     ,			return_tensors="""pt"""     ,			src_lang="""en_XX"""     ,			tgt_lang="""ar_AR"""			)

										self.assertEqual(
										    nested_simplify(__snake_case			)     ,			{
										        # A, test, EOS, en_XX
										        """input_ids""": [[62, 30_34, 2, 25_00_04]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 25_00_01,
										    }     ,			)








import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=3			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_0_0_0			,						lowerCAmelCase__=[3, 3, 6, 4]			,						lowerCAmelCase__=[4_8, 5_6, 1_1_2, 2_2_0]			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_channels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					is_training
										SCREAMING_SNAKE_CASE_		:     str					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     int					=					num_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					layer_depths
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					embed_dims






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size]			,						self.num_labels       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_config()

										return config, pixel_values, labels






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return SwiftFormerConfig(
										    depths=self.layer_depths			,						embed_dims=self.embed_dims			,						mlp_ratio=4			,						downsamples=[True, True, True, True]			,						hidden_act='gelu'			,						num_labels=self.num_labels			,						down_patch_size=3			,						down_stride=2			,						down_pad=1			,						drop_rate=0.0			,						drop_path_rate=0.0			,						use_layer_scale=lowerCAmelCase__			,						layer_scale_init_value=1E-5			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerModel(config=lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     int					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7)       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.num_labels
										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(lowerCAmelCase__			,						labels=lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()

										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										((SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_))		:     Optional[Any]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		:     str					=					{'pixel_values': pixel_values}
										return config, inputs_dict







@require_torch
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      =						(
							    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     str					=					ConfigTester(
										    self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__			,						hidden_size=3_7			,						num_attention_heads=1_2			,						num_hidden_layers=1_2			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__			,						nn.Linear       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     int					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_		:     int					=					[*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_		:     Tuple					=					['pixel_values']
													self.assertListEqual(arg_names[:1]			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SwiftFormerModel.from_pretrained(lowerCAmelCase__       )
													self.assertIsNotNone(lowerCAmelCase__       )






							@unittest.skip(reason='SwiftFormer does not output attentions'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Tuple					=					model_class(lowerCAmelCase__       )
													model.to(lowerCAmelCase__       )
													model.eval()

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:     str					=					model(**self._prepare_for_class(lowerCAmelCase__			,						lowerCAmelCase__       )       )

													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs.hidden_states

													SCREAMING_SNAKE_CASE_		:     Any					=					8
													self.assertEqual(len(lowerCAmelCase__       )			,						lowerCAmelCase__       )  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(lowerCAmelCase__       )       ):
																self.assertEqual(
																    hidden_states[i].shape			,						torch.Size(
																        [
																            self.model_tester.batch_size,
																            self.model_tester.embed_dims[i // 2],
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																        ]       )			,						)

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

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					True
													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )

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

													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def _config_zero_init(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     List[str]					=					copy.deepcopy(lowerCAmelCase__       )
													for key in configs_no_init.__dict__.keys():
																if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						1E-10       )
																if isinstance(getattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )			,						lowerCAmelCase__       ):
																			SCREAMING_SNAKE_CASE_		:     int					=					_config_zero_init(getattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
													return configs_no_init

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

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					_config_zero_init(lowerCAmelCase__       )
										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(config=lowerCAmelCase__       )
													for name, param in model.named_parameters():
																if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1E9) / 1E9).round().item()			,						[0.0, 1.0]			,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,						)






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





										"""simple docstring"""




										pass







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
			return image







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





							"""simple docstring"""







							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(lowerCAmelCase__       )

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

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

										# verify the logits
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Size((1, 1_0_0_0)       )
										self.assertEqual(outputs.logits.shape			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )





