"""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 				snake_case__     :


	def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=30  ,						lowerCamelCase=2  ,						lowerCamelCase=3  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=32  ,						lowerCamelCase=5  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=10  ,						lowerCamelCase=0.02  ,						lowerCamelCase=None  ,						):
								__a     				=  parent
								__a     				=  batch_size
								__a     				=  image_size
								__a     				=  patch_size
								__a     				=  num_channels
								__a     				=  is_training
								__a     				=  use_labels
								__a     				=  hidden_size
								__a     				=  num_hidden_layers
								__a     				=  num_attention_heads
								__a     				=  intermediate_size
								__a     				=  hidden_act
								__a     				=  hidden_dropout_prob
								__a     				=  attention_probs_dropout_prob
								__a     				=  type_sequence_label_size
								__a     				=  initializer_range
								__a     				=  scope

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


	def 		a__ (				self		):
								__a     				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

								__a     				=  self.get_config()

								return config, pixel_values, labels


	def 		a__ (				self		):
								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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								__a     				=  ViTMSNModel(config=lowercase_		)
								model.to(lowercase_		)
								model.eval()
								__a     				=  model(lowercase_		)
								self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								__a     				=  self.type_sequence_label_size
								__a     				=  ViTMSNForImageClassification(lowercase_		)
								model.to(lowercase_		)
								model.eval()
								__a     				=  model(lowercase_  ,						labels=lowercase_		)
								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
								__a     				=  1
								__a     				=  ViTMSNForImageClassification(lowercase_		)
								model.to(lowercase_		)
								model.eval()

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






	def 		a__ (				self		):
								__a     				=  self.prepare_config_and_inputs()
								__a				, __a				, __a     				=  config_and_inputs
								__a     				=  {"pixel_values": pixel_values}
								return config, inputs_dict






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
	_snake_case   :			int           =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	_snake_case   :			Optional[int]           =		(
	    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_snake_case   :			Optional[int]           =		False
	_snake_case   :			Optional[Any]           =		False
	_snake_case   :			List[Any]           =		False
	_snake_case   :			List[Any]           =		False


	def 		a__ (				self		):
								__a     				=  ViTMSNModelTester(self		)
								__a     				=  ConfigTester(self  ,						config_class=lowercase_  ,						has_text_modality=lowercase_  ,						hidden_size=37		)


	def 		a__ (				self		):
								self.config_tester.run_common_tests()


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


	def 		a__ (				self		):
								__a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

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


	def 		a__ (				self		):
								__a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

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

															__a     				=  ["pixel_values"]
															self.assertListEqual(arg_names[:1]  ,						lowercase_		)


	def 		a__ (				self		):
								__a     				=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase_		)


	def 		a__ (				self		):
								__a     				=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowercase_		)






	@slow
	def 		a__ (				self		):







								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a     				=  ViTMSNModel.from_pretrained(lowercase_		)
															self.assertIsNotNone(lowercase_		)



def 					_lowerCamelCase(					):
							__a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							return image






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


	@cached_property
	def 		a__ (				self		):
								return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None






	@slow
	def 		a__ (				self		):
								torch.manual_seed(2		)
								__a     				=  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(lowercase_		)

								__a     				=  self.default_image_processor
								__a     				=  prepare_img()
								__a     				=  image_processor(images=lowercase_  ,						return_tensors="pt"		).to(lowercase_		)

								# forward pass
								with torch.no_grad():
															__a     				=  model(**lowercase_		)

								# verify the logits
								__a     				=  torch.Size((1, 1000)		)
								self.assertEqual(outputs.logits.shape  ,						lowercase_		)

								__a     				=  torch.tensor([-0.0803, -0.4454, -0.2375]		).to(lowercase_		)

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

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
   from .tokenization_camembert import CamembertTokenizer
else:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            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  ,						additional_special_tokens=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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
        __a     				=  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,)
