

import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__												=						get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       XLMProphetNetTokenizer
							UpperCAmelCase__			:		Optional[int]             =       False
							UpperCAmelCase__			:		List[Any]             =       True


							def 							__lowercase							(     self			)       ->       int:
												super().setUp()

												# We have a SentencePiece fixture for testing
												_a      :       List[Any]   =						XLMProphetNetTokenizer(_a			,				keep_accents=_a			)
												tokenizer.save_pretrained(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Tuple   =						'''[PAD]'''
												_a      :       int   =						0

												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			)       ->       str:
												_a      :       Any   =						list(self.get_tokenizer().get_vocab().keys()			)

												self.assertEqual(vocab_keys[0]			,				'''[PAD]'''			)
												self.assertEqual(vocab_keys[1]			,				'''[CLS]'''			)
												self.assertEqual(vocab_keys[-1]			,				'''j'''			)
												self.assertEqual(len(_a			)			,				1_0_1_2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.assertEqual(self.get_tokenizer().vocab_size			,				1_0_1_2			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Tuple   =						XLMProphetNetTokenizer(_a			,				keep_accents=_a			)

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

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_a			)			,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,				)

												_a      :       Optional[int]   =						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''',
												        '''.''',
												    ]			,				)
												_a      :       List[Any]   =						tokenizer.convert_tokens_to_ids(_a			)
												self.assertListEqual(
												    _a			,				[
												        value + tokenizer.fairseq_offset
												        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
												    ]			,				)

												_a      :       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[str]:
												return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)


							@slow
							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       str   =						'''Hello World!'''
												_a      :       Tuple   =						[3_5_3_8_9, 6_6_7_2, 4_9, 2]
												self.assertListEqual(_a			,				self.big_tokenizer.encode(_a			)			)




							@slow
							def 							__lowercase							(     self			)       ->       str:
												# fmt: off
												_a      :       str   =						{'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=_a			,				model_name='''microsoft/xprophetnet-large-wiki100-cased'''			,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''			,				)






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 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=3			,				_a=True			,				_a=True			,				_a=0.1			,				_a=0.1			,				_a=2_2_4			,				_a=1_0_0_0			,				_a=[3, 3, 6, 4]			,				_a=[4_8, 5_6, 1_1_2, 2_2_0]			,				)       ->       Tuple:
												_a      :       Dict   =						parent
												_a      :       Optional[int]   =						batch_size
												_a      :       Optional[Any]   =						num_channels
												_a      :       Union[str, Any]   =						is_training
												_a      :       Tuple   =						use_labels
												_a      :       Dict   =						hidden_dropout_prob
												_a      :       List[Any]   =						attention_probs_dropout_prob
												_a      :       Dict   =						num_labels
												_a      :       List[str]   =						image_size
												_a      :       Dict   =						layer_depths
												_a      :       str   =						embed_dims


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       int   =						None
												if self.use_labels:
																	_a      :       Optional[Any]   =						ids_tensor([self.batch_size]			,				self.num_labels			)

												_a      :       Dict   =						self.get_config()

												return config, pixel_values, labels


							def 							__lowercase							(     self			)       ->       int:
												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=_a			,				layer_scale_init_value=1e-5			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       str:
												_a      :       List[Any]   =						SwiftFormerModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[int]   =						model(_a			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.num_labels
												_a      :       Optional[int]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[str]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)

												_a      :       Union[str, Any]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()

												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
												_a      :       Optional[Any]   =						model(_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)




							def 							__lowercase							(     self			)       ->       Tuple:
												((_a) ,   (_a) ,   (_a))      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[int]             =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Optional[Any]             =       False
							UpperCAmelCase__			:		str             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						SwiftFormerModelTester(self			)
												_a      :       int   =						ConfigTester(
												    self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			,				num_attention_heads=1_2			,				num_hidden_layers=1_2			,				)


							def 							__lowercase							(     self			)       ->       int:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Any   =						model_class(_a			)
																	_a      :       int   =						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_a			,				nn.Linear			)			)


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       List[str]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Any   =						SwiftFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							@unittest.skip(reason='''SwiftFormer does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       int:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

																	with torch.no_grad():
																						_a      :       Union[str, Any]   =						model(**self._prepare_for_class(_a			,				_a			)			)

																	_a      :       Optional[Any]   =						outputs.hidden_states

																	_a      :       Union[str, Any]   =						8
																	self.assertEqual(len(_a			)			,				_a			)  # 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(_a			)			):
																						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),
																						        ]			)			,				)

												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       str:
												def _config_zero_init(_a			):
																	_a      :       List[Any]   =						copy.deepcopy(_a			)
																	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(_a			,				_a			,				1e-1_0			)
																						if isinstance(getattr(_a			,				_a			,				_a			)			,				_a			):
																											_a      :       int   =						_config_zero_init(getattr(_a			,				_a			)			)
																											setattr(_a			,				_a			,				_a			)
																	return configs_no_init

												_a ,   _a      :       Any   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       Dict   =						_config_zero_init(_a			)
												for model_class in self.all_model_classes:
																	_a      :       Dict   =						model_class(config=_a			)
																	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 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



def   __UpperCAmelCase					(      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       str:
												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''			) if is_vision_available() else None




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Any   =						prepare_img()
												_a      :       Any   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Optional[Any]   =						model(**_a			)

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       int   =						torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]			).to(_a			)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_a			,				atol=1e-4			)			)



