





import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    FocalNetBackbone,
							    FocalNetForImageClassification,
							    FocalNetForMaskedImageModeling,
							    FocalNetModel,
							)
							from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=32   , UpperCAmelCase_=2   , UpperCAmelCase_=3   , UpperCAmelCase_=16   , UpperCAmelCase_=[32, 64, 128]   , UpperCAmelCase_=[1, 2, 1]   , UpperCAmelCase_=[2, 2, 4]   , UpperCAmelCase_=2   , UpperCAmelCase_=2.0   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=False   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , UpperCAmelCase_=True   , UpperCAmelCase_=10   , UpperCAmelCase_=8   , UpperCAmelCase_=["stage1", "stage2"]   , UpperCAmelCase_=[1, 2]   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =embed_dim
								lowerCamelCase	 =hidden_sizes
								lowerCamelCase	 =depths
								lowerCamelCase	 =num_heads
								lowerCamelCase	 =window_size
								lowerCamelCase	 =mlp_ratio
								lowerCamelCase	 =qkv_bias
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =drop_path_rate
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =use_absolute_embeddings
								lowerCamelCase	 =patch_norm
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =is_training
								lowerCamelCase	 =scope
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =encoder_stride
								lowerCamelCase	 =out_features
								lowerCamelCase	 =out_indices


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								lowerCamelCase	 =None
								if self.use_labels:
												lowerCamelCase	 =ids_tensor([self.batch_size]   , self.type_sequence_label_size							)

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								return FocalNetConfig(
								    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , embed_dim=self.embed_dim   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , num_heads=self.num_heads   , window_size=self.window_size   , mlp_ratio=self.mlp_ratio   , qkv_bias=self.qkv_bias   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , drop_path_rate=self.drop_path_rate   , hidden_act=self.hidden_act   , use_absolute_embeddings=self.use_absolute_embeddings   , path_norm=self.patch_norm   , layer_norm_eps=self.layer_norm_eps   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , out_features=self.out_features   , out_indices=self.out_indices   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								lowerCamelCase	 =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
								lowerCamelCase	 =int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, expected_seq_len, expected_dim)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							)   , len(config.out_features							)							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							)   , [self.batch_size, self.image_size, 8, 8]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							)   , len(config.out_features							)							)
								self.parent.assertListEqual(model.channels   , config.hidden_sizes[:-1]							)

								# verify backbone works with out_features=None
								lowerCamelCase	 =None
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							)   , 1							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							)   , [self.batch_size, self.image_size * 2, 4, 4]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							)   , 1							)
								self.parent.assertListEqual(model.channels   , [config.hidden_sizes[-1]]							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetForMaskedImageModeling(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(
								    result.reconstruction.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForMaskedImageModeling(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.reconstruction.shape   , (self.batch_size, 1, self.image_size, self.image_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =self.type_sequence_label_size
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(
				    (
				        FocalNetModel,
				        FocalNetForImageClassification,
				        FocalNetForMaskedImageModeling,
				        FocalNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				__A      =				(
				    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
				    if is_torch_available()
				    else {}
				)
				__A      =				False

				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , embed_dim=37   , has_text_modality=UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


				def _snake_case       (		self							):
								return


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_							)


				@unittest.skip(reason="""FocalNet does not use inputs_embeds"""							)
				def _snake_case       (		self							):
								pass


				@unittest.skip(reason="""FocalNet does not use feedforward chunking"""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)							)
												lowerCamelCase	 =model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCAmelCase_   , nn.Linear							)							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												lowerCamelCase	 =inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowerCamelCase	 =[*signature.parameters.keys()]

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =model_class(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								with torch.no_grad():
												lowerCamelCase	 =model(**self._prepare_for_class(UpperCAmelCase_   , UpperCAmelCase_							)							)

								lowerCamelCase	 =outputs.hidden_states

								lowerCamelCase	 =getattr(
								    self.model_tester   , """expected_num_hidden_layers"""   , len(self.model_tester.depths							) + 1							)
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								# FocalNet has a different seq_length
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )

								lowerCamelCase	 =outputs.reshaped_hidden_states
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =reshaped_hidden_states[0].shape
								lowerCamelCase	 =(
								    reshaped_hidden_states[0].view(UpperCAmelCase_   , UpperCAmelCase_   , height * width							).permute(0   , 2   , 1							)
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)

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

												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase	 =3

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCamelCase	 =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)


				@slow
				def _snake_case       (		self							):
								for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase	 =FocalNetModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =_config_zero_init(UpperCAmelCase_							)
								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(config=UpperCAmelCase_							)
												for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																				self.assertIn(
																				    ((param.data.mean() * 1E9).round() / 1E9).item()   , [0.0, 1.0]   , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   , )




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


				@cached_property
				def _snake_case       (		self							):
								# TODO update organization
								return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""							) if is_vision_available() else None


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""							).to(UpperCAmelCase_							)
								lowerCamelCase	 =self.default_image_processor

								lowerCamelCase	 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								lowerCamelCase	 =image_processor(images=UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

								# verify the logits
								lowerCamelCase	 =torch.Size((1, 1000)							)
								self.assertEqual(outputs.logits.shape   , UpperCAmelCase_							)
								lowerCamelCase	 =torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]							).to(UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]   , UpperCAmelCase_   , atol=1E-4							)							)
								self.assertTrue(outputs.logits.argmax(dim=-1							).item()   , 281							)




@require_torch
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				(FocalNetBackbone,) if is_torch_available() else ()
				__A      =				FocalNetConfig

				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)












import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCAmelCase__						:			Union[str, Any]									=get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__						:			Union[str, Any]									=12_80_22
UpperCAmelCase__						:			Optional[Any]									=12_80_28

@require_sentencepiece
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				MaMaaaTokenizer
				__A      =				False
				__A      =				False
				__A      =				True


				def _snake_case       (		self							):
								super().setUp()

								lowerCamelCase	 =["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
								lowerCamelCase	 =dict(zip(UpperCAmelCase_   , range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase	 =Path(self.tmpdirname							)
								save_json(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
								tokenizer.save_pretrained(self.tmpdirname							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return (
								    "This is a test",
								    "This is a test",
								)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""</s>"""
								lowerCamelCase	 =0

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


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =list(tokenizer.get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]   , """</s>"""							)
								self.assertEqual(vocab_keys[1]   , """<unk>"""							)
								self.assertEqual(vocab_keys[-1]   , """<s>"""							)
								self.assertEqual(len(UpperCAmelCase_							)   , tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


				@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =tokenizer.tokenize("""This is a test"""							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , [2, 3, 4, 5, 6]   , )

								lowerCamelCase	 =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								lowerCamelCase	 =tokenizer.convert_tokens_to_string(UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , """This is a test"""							)


				@slow
				def _snake_case       (		self							):
								# fmt: off
								lowerCamelCase	 ={"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], """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, 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], [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, 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=UpperCAmelCase_   , model_name="""facebook/m2m100_418M"""   , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   , )




@require_torch
@require_sentencepiece
@require_tokenizers
class       __A     (						unittest.TestCase    ):
				__A      =				"""facebook/m2m100_418M"""
				__A      =				[
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__A      =				[
				    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
				    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
				]

				# fmt: off
				__A      =				[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name   , src_lang="""en"""   , tgt_lang="""fr"""							)
								lowerCamelCase	 =1
								return cls


				def _snake_case       (		self							):
								self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)   , 128006							)
								self.assertEqual(self.tokenizer.get_lang_id("""en"""							)   , 128022							)
								self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)   , 128076							)
								self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)   , 128063							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer.get_vocab()
								self.assertEqual(len(UpperCAmelCase_							)   , self.tokenizer.vocab_size							)
								self.assertEqual(vocab["""<unk>"""]   , 3							)
								self.assertIn(self.tokenizer.get_lang_token("""en"""							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 =self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.assertIn(UpperCAmelCase_   , self.tokenizer.all_special_ids							)
								# fmt: off
								lowerCamelCase	 =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
								# fmt: on
								lowerCamelCase	 =self.tokenizer.decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
								lowerCamelCase	 =self.tokenizer.decode(generated_ids[1:]   , skip_special_tokens=UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)
								self.assertNotIn(self.tokenizer.eos_token   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =tempfile.mkdtemp()
								lowerCamelCase	 =self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.lang_token_to_id   , UpperCAmelCase_							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 ="""fr"""

								lowerCamelCase	 =self.tokenizer(self.src_text   , text_target=self.tgt_text   , padding=UpperCAmelCase_   , return_tensors="""pt"""							)

								lowerCamelCase	 =shift_tokens_right(
								    batch["""labels"""]   , self.tokenizer.pad_token_id   , self.tokenizer.eos_token_id							)

								for k in batch:
												lowerCamelCase	 =batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)

								lowerCamelCase	 ="""zh"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

								lowerCamelCase	 ="""zh"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer._build_translation_inputs("""A test"""   , return_tensors="""pt"""   , src_lang="""en"""   , tgt_lang="""ar"""							)

								self.assertEqual(
								    nested_simplify(UpperCAmelCase_							)   , {
								        # en_XX, A, test, EOS
								        """input_ids""": [[128022, 58, 4183, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 128006,
								    }   , )





