




'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor




class 			_snake_case		:




   def __init__( self    ,							a__    ,							a__=13    ,							a__=30    ,							a__=2    ,							a__=3    ,							a__=True    ,							a__=True    ,							a__=32    ,							a__=2    ,							a__=4    ,							a__=37    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=10    ,							a__=0.0_2    ,							a__=3    ,							a__=None    ,							)    ->    Optional[Any]:

          '''simple docstring'''




          snake_case_      =				parent
          snake_case_      =				batch_size
          snake_case_      =				image_size
          snake_case_      =				patch_size
          snake_case_      =				num_channels
          snake_case_      =				is_training
          snake_case_      =				use_labels
          snake_case_      =				hidden_size
          snake_case_      =				num_hidden_layers
          snake_case_      =				num_attention_heads
          snake_case_      =				intermediate_size
          snake_case_      =				hidden_act
          snake_case_      =				hidden_dropout_prob
          snake_case_      =				attention_probs_dropout_prob
          snake_case_      =				type_sequence_label_size
          snake_case_      =				initializer_range
          snake_case_      =				scope

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




   def        lowerCAmelCase__    ( self							)    ->    List[Any]:

          '''simple docstring'''




          snake_case_      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

          snake_case_      =				self.get_config()

          return config, pixel_values, labels




   def        lowerCAmelCase__    ( self							)    ->    str:

          '''simple docstring'''




          return ViTConfig(
              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    ,							is_decoder=_snake_case    ,							initializer_range=self.initializer_range    ,							)




   def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__							)    ->    Optional[int]:

          '''simple docstring'''




          snake_case_      =				TFViTModel(config=_snake_case							)
          snake_case_      =				model(_snake_case    ,							training=_snake_case							)
          self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)							)

          # Test with an image with different size than the one specified in config.
          snake_case_      =				self.image_size // 2
          snake_case_      =				pixel_values[:, :, :image_size, :image_size]
          snake_case_      =				model(_snake_case    ,							interpolate_pos_encoding=_snake_case    ,							training=_snake_case							)
          snake_case_      =				(image_size // self.patch_size) ** 2 + 1
          self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, seq_length, self.hidden_size)							)




   def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__							)    ->    List[Any]:

          '''simple docstring'''




          snake_case_      =				self.type_sequence_label_size
          snake_case_      =				TFViTForImageClassification(_snake_case							)
          snake_case_      =				model(_snake_case    ,							labels=_snake_case    ,							training=_snake_case							)
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)							)

          # Test with an image with different size than the one specified in config.
          snake_case_      =				self.image_size // 2
          snake_case_      =				pixel_values[:, :, :image_size, :image_size]
          snake_case_      =				model(_snake_case    ,							interpolate_pos_encoding=_snake_case    ,							training=_snake_case							)
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)							)

          # test greyscale images
          snake_case_      =				1
          snake_case_      =				TFViTForImageClassification(_snake_case							)
          snake_case_      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          snake_case_      =				model(_snake_case							)
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)							)




   def        lowerCAmelCase__    ( self							)    ->    int:

          '''simple docstring'''




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




@require_tf
class 			_snake_case		(     lowercase_						,						lowercase_						,						unittest.TestCase							):
   lowerCAmelCase_					:				str							=   (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
   lowerCAmelCase_					:				str							=   (
       {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
       if is_tf_available()
       else {}
   )

   lowerCAmelCase_					:				str							=   False
   lowerCAmelCase_					:				List[str]							=   False
   lowerCAmelCase_					:				Optional[int]							=   False




   def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

          '''simple docstring'''




          snake_case_      =				TFViTModelTester(self							)
          snake_case_      =				ConfigTester(self    ,							config_class=_snake_case    ,							has_text_modality=_snake_case    ,							hidden_size=37							)




   def        lowerCAmelCase__    ( self							)    ->    int:

          '''simple docstring'''




          self.config_tester.run_common_tests()




   @unittest.skip(reason="ViT does not use inputs_embeds"							)
   def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

          '''simple docstring'''




          pass




   @unittest.skip(reason="ViT does not use inputs_embeds"							)
   def        lowerCAmelCase__    ( self							)    ->    Any:

          '''simple docstring'''




          pass




   def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

          '''simple docstring'''




          snake_case_ ,				snake_case_      =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case_      =				model_class(_snake_case							)
                 self.assertIsInstance(model.get_input_embeddings()    ,							(tf.keras.layers.Layer)							)
                 snake_case_      =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_snake_case    ,							tf.keras.layers.Layer							)							)




   def        lowerCAmelCase__    ( self							)    ->    str:

          '''simple docstring'''




          snake_case_ ,				snake_case_      =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case_      =				["pixel_values"]
                 self.assertListEqual(arg_names[:1]    ,							_snake_case							)




   def        lowerCAmelCase__    ( self							)    ->    List[str]:

          '''simple docstring'''




          snake_case_      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_snake_case							)




   def        lowerCAmelCase__    ( self							)    ->    Dict:

          '''simple docstring'''




          snake_case_      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_snake_case							)




   @slow
   def        lowerCAmelCase__    ( self							)    ->    List[str]:

          '''simple docstring'''




          snake_case_      =				TFViTModel.from_pretrained("google/vit-base-patch16-224"							)
          self.assertIsNotNone(_snake_case							)


def 					UpperCamelCase_(     ):

       '''simple docstring'''






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




@require_tf
@require_vision
class 			_snake_case		(     unittest.TestCase							):




   @cached_property
   def        lowerCAmelCase__    ( self							)    ->    Any:

          '''simple docstring'''




          return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"							) if is_vision_available() else None




   @slow
   def        lowerCAmelCase__    ( self							)    ->    Tuple:

          '''simple docstring'''




          snake_case_      =				TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"							)

          snake_case_      =				self.default_image_processor
          snake_case_      =				prepare_img()
          snake_case_      =				image_processor(images=_snake_case    ,							return_tensors="tf"							)

          # forward pass
          snake_case_      =				model(**_snake_case							)

          # verify the logits
          snake_case_      =				tf.TensorShape((1, 1_000)							)
          self.assertEqual(outputs.logits.shape    ,							_snake_case							)

          snake_case_      =				tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]							)

          tf.debugging.assert_near(outputs.logits[0, :3]    ,							_snake_case    ,							atol=1e-4							)










'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_SCREAMING_SNAKE_CASE							:       Any						=			"▁"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Tuple							=   ["input_ids", "attention_mask"]




			def __init__( self    ,							a__    ,							a__="<s>"    ,							a__="</s>"    ,							a__="</s>"    ,							a__="<s>"    ,							a__="<unk>"    ,							a__="<pad>"    ,							a__="<mask>"    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							cls_token=a__    ,							pad_token=a__    ,							mask_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(a__							)							)

										snake_case_      =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_      =				len(self.sp_model							) - 1
										snake_case_      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=a__    ,							token_ids_a=a__    ,							already_has_special_tokens=a__							)

										if token_ids_a is None:
																	return [1] + ([0] * len(a__							)) + [1]
										return [1] + ([0] * len(a__							)) + [1, 1] + ([0] * len(a__							)) + [1]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[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]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				{self.convert_ids_to_tokens(a__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										return self.sp_model.encode(a__    ,							out_type=a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										snake_case_      =				self.sp_model.PieceToId(a__							)

										return spm_id if spm_id else self.unk_token_id




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										snake_case_      =				False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(a__							) + token
																								snake_case_      =				True
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
																								snake_case_      =				False
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def __getstate__( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

										# for backward compatibility
										if not hasattr(self    ,							"sp_model_kwargs"							):
																	snake_case_      =				{}

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_      =				os.path.join(
										    a__    ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(a__							) and os.path.isfile(self.vocab_file							):
																	copyfile(self.vocab_file    ,							a__							)
										elif not os.path.isfile(self.vocab_file							):
																	with open(a__    ,							"wb"							) as fi:
																								snake_case_      =				self.sp_model.serialized_model_proto()
																								fi.write(a__							)

										return (out_vocab_file,)




