


"""simple docstring"""






import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : int=13 ,       UpperCamelCase    : Tuple=30 ,       UpperCamelCase    : Union[str, Any]=2 ,       UpperCamelCase    : Optional[Any]=3 ,       UpperCamelCase    : Union[str, Any]=True ,       UpperCamelCase    : List[str]=True ,       UpperCamelCase    : Any=32 ,       UpperCamelCase    : List[Any]=5 ,       UpperCamelCase    : Any=4 ,       UpperCamelCase    : Dict=37 ,       UpperCamelCase    : Optional[Any]="gelu" ,       UpperCamelCase    : str=0.1 ,       UpperCamelCase    : List[str]=0.1 ,       UpperCamelCase    : Optional[int]=10 ,       UpperCamelCase    : List[str]=0.02 ,       UpperCamelCase    : Optional[int]=None ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					parent
												__UpperCAmelCase   :						List[str]      							=					batch_size
												__UpperCAmelCase   :						str      							=					image_size
												__UpperCAmelCase   :						str      							=					patch_size
												__UpperCAmelCase   :						Tuple      							=					num_channels
												__UpperCAmelCase   :						Any      							=					is_training
												__UpperCAmelCase   :						Tuple      							=					use_labels
												__UpperCAmelCase   :						Dict      							=					hidden_size
												__UpperCAmelCase   :						Optional[Any]      							=					num_hidden_layers
												__UpperCAmelCase   :						Optional[Any]      							=					num_attention_heads
												__UpperCAmelCase   :						Dict      							=					intermediate_size
												__UpperCAmelCase   :						Optional[int]      							=					hidden_act
												__UpperCAmelCase   :						Optional[Any]      							=					hidden_dropout_prob
												__UpperCAmelCase   :						Optional[int]      							=					attention_probs_dropout_prob
												__UpperCAmelCase   :						Tuple      							=					type_sequence_label_size
												__UpperCAmelCase   :						List[Any]      							=					initializer_range
												__UpperCAmelCase   :						int      							=					scope

												# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												__UpperCAmelCase   :						Any      							=					(image_size // patch_size) ** 2
												__UpperCAmelCase   :						int      							=					num_patches + 1






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






												__UpperCAmelCase   :						Dict      							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

												__UpperCAmelCase   :						str      							=					None
												if self.use_labels:
																			__UpperCAmelCase   :						List[str]      							=					ids_tensor([self.batch_size] ,       self.type_sequence_label_size  )

												__UpperCAmelCase   :						int      							=					self.get_config()

												return config, pixel_values, labels






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






												return ViTMSNConfig(
												    image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       hidden_size=self.hidden_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       initializer_range=self.initializer_range ,       )






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Dict ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					ViTMSNModel(config=lowerCAmelCase_  )
												model.to(lowerCAmelCase_  )
												model.eval()
												__UpperCAmelCase   :						Optional[Any]      							=					model(lowerCAmelCase_  )
												self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)  )






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.type_sequence_label_size
												__UpperCAmelCase   :						List[str]      							=					ViTMSNForImageClassification(lowerCAmelCase_  )
												model.to(lowerCAmelCase_  )
												model.eval()
												__UpperCAmelCase   :						Optional[int]      							=					model(lowerCAmelCase_ ,       labels=lowerCAmelCase_  )
												print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""  )
												print("""Labels: {labels}"""  )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)  )

												# test greyscale images
												__UpperCAmelCase   :						Any      							=					1
												__UpperCAmelCase   :						Any      							=					ViTMSNForImageClassification(lowerCAmelCase_  )
												model.to(lowerCAmelCase_  )
												model.eval()

												__UpperCAmelCase   :						Dict      							=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
												__UpperCAmelCase   :						Union[str, Any]      							=					model(lowerCAmelCase_  )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)  )






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






												__UpperCAmelCase   :						List[Any]      							=					self.prepare_config_and_inputs()
												__UpperCAmelCase   :						Any      							=					config_and_inputs
												__UpperCAmelCase   :						List[Any]      							=					{"pixel_values": pixel_values}
												return config, inputs_dict






@require_torch
class      lowerCamelCase__   (     __A     ,    __A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					__a	       =							(
					    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					ViTMSNModelTester(self  )
												__UpperCAmelCase   :						List[Any]      							=					ConfigTester(self ,       config_class=lowerCAmelCase_ ,       has_text_modality=lowerCAmelCase_ ,       hidden_size=37  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												self.config_tester.run_common_tests()






					@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""  )
					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												pass






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






												__UpperCAmelCase   :						Any      							=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase   :						str      							=					model_class(lowerCAmelCase_  )
																			self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)  )
																			__UpperCAmelCase   :						Tuple      							=					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(lowerCAmelCase_ ,       nn.Linear  )  )






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






												__UpperCAmelCase   :						Dict      							=					self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCAmelCase   :						Tuple      							=					["pixel_values"]
																			self.assertListEqual(arg_names[:1] ,       lowerCAmelCase_  )






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






												__UpperCAmelCase   :						Any      							=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowerCAmelCase_  )






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_  )






					@slow
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase   :						Union[str, Any]      							=					ViTMSNModel.from_pretrained(lowerCAmelCase_  )
																			self.assertIsNotNone(lowerCAmelCase_  )






def      lowerCamelCase       (     )		->       Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image





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







					"""simple docstring"""


					@cached_property
					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""  ) if is_vision_available() else None






					@slow
					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												torch.manual_seed(2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""  ).to(lowerCAmelCase_  )

												__UpperCAmelCase   :						List[str]      							=					self.default_image_processor
												__UpperCAmelCase   :						Union[str, Any]      							=					prepare_img()
												__UpperCAmelCase   :						List[str]      							=					image_processor(images=lowerCAmelCase_ ,       return_tensors="""pt"""  ).to(lowerCAmelCase_  )

												# forward pass
												with torch.no_grad():
																			__UpperCAmelCase   :						int      							=					model(**lowerCAmelCase_  )

												# verify the logits
												__UpperCAmelCase   :						List[Any]      							=					torch.Size((1, 1_000)  )
												self.assertEqual(outputs.logits.shape ,       lowerCAmelCase_  )

												__UpperCAmelCase   :						Any      							=					torch.tensor([-0.0803, -0.4454, -0.2375]  ).to(lowerCAmelCase_  )

												self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       lowerCAmelCase_ ,       atol=1e-4  )  )








"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			lowerCamelCase_					=      None


lowerCamelCase_					=      logging.get_logger(__name__)


lowerCamelCase_					=      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					=      {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_					=      {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
lowerCamelCase_					=      ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				MBartTokenizer

	__magic_name__	      =				[]
	__magic_name__	      =				[]





	def __init__(      self					: List[str]	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: str=None	,	lowerCAmelCase_					: List[Any]="<s>"	,	lowerCAmelCase_					: Optional[Any]="</s>"	,	lowerCAmelCase_					: str="</s>"	,	lowerCAmelCase_					: str="<s>"	,	lowerCAmelCase_					: List[Any]="<unk>"	,	lowerCAmelCase_					: Tuple="<pad>"	,	lowerCAmelCase_					: Union[str, Any]="<mask>"	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: str=None	,	lowerCAmelCase_					: Optional[Any]=None	,	**lowerCAmelCase_					: Any	,	)						->			Tuple:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_     : Union[str, Any]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else mask_token

				super().__init__(
				    vocab_file=lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	bos_token=lowerCAmelCase_	,	eos_token=lowerCAmelCase_	,	sep_token=lowerCAmelCase_	,	cls_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	mask_token=lowerCAmelCase_	,	src_lang=lowerCAmelCase_	,	tgt_lang=lowerCAmelCase_	,	additional_special_tokens=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				UpperCAmelCase_     : Tuple			     =					vocab_file
				UpperCAmelCase_     : str			     =					False if not self.vocab_file else True

				UpperCAmelCase_     : List[Any]			     =					FAIRSEQ_LANGUAGE_CODES.copy()

				if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							_additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

				self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
				UpperCAmelCase_     : Tuple			     =					{
				    lang_code: self.convert_tokens_to_ids(lowerCAmelCase_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				UpperCAmelCase_     : int			     =					src_lang if src_lang is not None else "en_XX"
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(self._src_lang						)
				UpperCAmelCase_     : Any			     =					tgt_lang
				self.set_src_lang_special_tokens(self._src_lang						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			str:
				return self._src_lang





	@src_lang.setter
	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : List[Any]			     =					new_src_lang
				self.set_src_lang_special_tokens(self._src_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : List[str]			     =					[self.sep_token_id]
				UpperCAmelCase_     : Dict			     =					[self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str]	,	lowerCAmelCase_					: Optional[str]	,	**lowerCAmelCase_					: Union[str, Any]						)						->			Dict:
				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
				UpperCAmelCase_     : str			     =					src_lang
				UpperCAmelCase_     : str			     =					self(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_	,	return_tensors=lowerCAmelCase_	,	**lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					tgt_lang_id
				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: str = "en_XX"	,	lowerCAmelCase_					: Optional[List[str]] = None	,	lowerCAmelCase_					: str = "ro_RO"	,	**lowerCAmelCase_					: Dict	,	)						->			BatchEncoding:
				UpperCAmelCase_     : List[Any]			     =					src_lang
				UpperCAmelCase_     : Tuple			     =					tgt_lang
				return super().prepare_seqaseq_batch(lowerCAmelCase_	,	lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Any:
				return self.set_src_lang_special_tokens(self.src_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Dict:
				return self.set_tgt_lang_special_tokens(self.tgt_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[Any]						)						->			None:
				UpperCAmelCase_     : int			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					[]
				UpperCAmelCase_     : str			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : List[Any]			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[Any]			     =					processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,	self.prefix_tokens + self.suffix_tokens						)						)	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : Optional[int]			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[Any]			     =					processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,	self.prefix_tokens + self.suffix_tokens						)						)	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."						)

				if not os.path.isdir(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
							return
				UpperCAmelCase_     : List[str]			     =					os.path.join(
				    lowerCAmelCase_	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCAmelCase_						):
							copyfile(self.vocab_file	,	lowerCAmelCase_						)

				return (out_vocab_file,)



