


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__			:      List[Any]								=get_tests_dir('fixtures/test_sentencepiece_bpe.model')







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						BartphoTokenizer
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						True






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										SCREAMING_SNAKE_CASE_		:     str					=					['▁This', '▁is', '▁a', '▁t', 'est']
										SCREAMING_SNAKE_CASE_		:     Any					=					dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{'unk_token': '<unk>'}

										SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['monolingual_vocab_file']       )
										with open(self.monolingual_vocab_file			,						'w'			,						encoding='utf-8'       ) as fp:
													for token in vocab_tokens:
																fp.write(F'''{token} {vocab_tokens[token]}\n'''       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					BartphoTokenizer(lowerCAmelCase__			,						self.monolingual_vocab_file			,						**self.special_tokens_map       )
										tokenizer.save_pretrained(self.tmpdirname       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										kwargs.update(self.special_tokens_map       )
										return BartphoTokenizer.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					'This is a là test'
										SCREAMING_SNAKE_CASE_		:     int					=					'This is a<unk><unk> test'
										return input_text, output_text






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					BartphoTokenizer(lowerCAmelCase__			,						self.monolingual_vocab_file			,						**self.special_tokens_map       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'This is a là test'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					'▁This ▁is ▁a ▁l à ▁t est'.split()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.tokenize(lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokens + [tokenizer.unk_token]
										SCREAMING_SNAKE_CASE_		:     Dict					=					[4, 5, 6, 3, 3, 7, 8, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )			,						lowerCAmelCase__       )









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       )       )





