




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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase__         =      {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCamelCase__         =      {
    """camembert-base""": 512,
}

UpperCamelCase__         =      """▁"""
class       a__   ( snake_case__				):






						"""simple docstring"""




						_a							:   List[Any] =   VOCAB_FILES_NAMES
						_a							:   Optional[int] =   PRETRAINED_VOCAB_FILES_MAP
						_a							:   int =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:   Union[str, Any] =   ["""input_ids""", """attention_mask"""]






						def __init__(       self		,      _A		,      _A="<s>"		,      _A="</s>"		,      _A="</s>"		,      _A="<s>"		,      _A="<unk>"		,      _A="<pad>"		,      _A="<mask>"		,      _A=["<s>NOTUSED", "</s>NOTUSED"]		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	AddedToken(_A		,      lstrip=_A		,      rstrip=_A      ) if isinstance(_A		,      _A      ) else mask_token

											__lowerCAmelCase           =	{} 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		,      additional_special_tokens=_A		,      sp_model_kwargs=self.sp_model_kwargs		,      **_A		,      )
											__lowerCAmelCase           =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
											self.sp_model.Load(str(_A      )      )
											__lowerCAmelCase           =	vocab_file
											# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
											# sentencepiece vocabulary (this is the case for <s> and </s>
											__lowerCAmelCase           =	{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
											__lowerCAmelCase           =	len(self.fairseq_tokens_to_ids      )
											__lowerCAmelCase           =	len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
											__lowerCAmelCase           =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None		,      _A = False      ):




											"""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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[self.sep_token_id]
											__lowerCAmelCase           =	[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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
											elif self.sp_model.PieceToId(_A      ) == 0:
																# Convert sentence piece unk token to fairseq unk token index
																return self.unk_token_id
											return self.fairseq_offset + self.sp_model.PieceToId(_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[]
											__lowerCAmelCase           =	""
											__lowerCAmelCase           =	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
																					__lowerCAmelCase           =	True
																					__lowerCAmelCase           =	[]
																else:
																					current_sub_tokens.append(_A      )
																					__lowerCAmelCase           =	False
											out_string += self.sp_model.decode(_A      )
											return out_string.strip()






						def __getstate__(       self      ):




											"""simple docstring"""



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






						def __setstate__(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	d

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

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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											if not os.path.isdir(_A      ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																return
											__lowerCAmelCase           =	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:
																					__lowerCAmelCase           =	self.sp_model.serialized_model_proto()
																					fi.write(_A      )

											return (out_vocab_file,)










from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor
class       a__   :






						def __init__(       self		,      _A		,      _A=1_3		,      _A=3_0		,      _A=2		,      _A=3		,      _A=True		,      _A=True		,      _A=3_2		,      _A=2		,      _A=4		,      _A=3_7		,      _A="gelu"		,      _A=0.1		,      _A=0.1		,      _A=1_0		,      _A=0.02		,      _A=3		,      _A=None		,      _A=2		,      ):




											"""simple docstring"""



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

											# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
											__lowerCAmelCase           =	(image_size // patch_size) ** 2
											__lowerCAmelCase           =	num_patches + 2






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return DeiTConfig(
											    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=_A		,      initializer_range=self.initializer_range		,      encoder_stride=self.encoder_stride		,      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModel(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(
											    result.reconstruction.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(_A      )

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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.type_sequence_label_size
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )

											__lowerCAmelCase           =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






@require_tf
class       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   Optional[Any] =   (
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:   Optional[Any] =   (
						    {
						        """feature-extraction""": TFDeiTModel,
						        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModelTester(self      )
											__lowerCAmelCase           =	ConfigTester(self		,      config_class=_A		,      has_text_modality=_A		,      hidden_size=3_7      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="DeiT does not use inputs_embeds"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase           =	["pixel_values"]
																self.assertListEqual(arg_names[:1]		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=False      ):




											"""simple docstring"""



											__lowerCAmelCase           =	super()._prepare_for_class(_A		,      _A		,      return_labels=_A      )

											if return_labels:
																if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
																					del inputs_dict["labels"]

											return inputs_dict






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	TFDeiTModel.from_pretrained(_A      )
																self.assertIsNotNone(_A      )







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


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






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return (
											    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
											    if is_vision_available()
											    else None
											)






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

											__lowerCAmelCase           =	self.default_image_processor
											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="tf"      )

											# forward pass
											__lowerCAmelCase           =	model(**_A      )

											# verify the logits
											__lowerCAmelCase           =	tf.TensorShape((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape		,      _A      )

											__lowerCAmelCase           =	tf.constant([-1.02_66, 0.19_12, -1.28_61]      )

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




