




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""": {
        """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"""
        ),
    },
}

UpperCamelCase__    			=			{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

UpperCamelCase__    			=			"""▁"""
class 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    VOCAB_FILES_NAMES
						_a							:    Tuple					=    PRETRAINED_VOCAB_FILES_MAP
						_a							:    List[Any]					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    Any					=    ["""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		,			):




											"""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		,			sp_model_kwargs=self.sp_model_kwargs		,			**_A		,			)

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

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

											__lowerCAmelCase				   =       len(self.sp_model			) - 1
											__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.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]
											__lowerCAmelCase				   =       self.sp_model.PieceToId(_A			)

											return spm_id if spm_id else self.unk_token_id






						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(_A			)






						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,)










import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import 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=5		,			_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				   =       DeiTModel(config=_A			)
											model.to(_A			)
											model.eval()
											__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				   =       DeiTForMaskedImageModeling(config=_A			)
											model.to(_A			)
											model.eval()
											__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				   =       DeiTForMaskedImageModeling(_A			)
											model.to(_A			)
											model.eval()

											__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				   =       DeiTForImageClassification(_A			)
											model.to(_A			)
											model.eval()
											__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				   =       DeiTForImageClassification(_A			)
											model.to(_A			)
											model.eval()

											__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_torch
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    Optional[Any]					=    (
						    (
						        DeiTModel,
						        DeiTForImageClassification,
						        DeiTForImageClassificationWithTeacher,
						        DeiTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						_a							:    int					=    (
						    {
						        """feature-extraction""": DeiTModel,
						        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						_a							:    Optional[Any]					=    False
						_a							:    Tuple					=    False
						_a							:    Tuple					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTModelTester(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()		,			(nn.Module)			)
																__lowerCAmelCase				   =       model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_A		,			nn.Linear			)			)






						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.forward			)
																# 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 model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																					del inputs_dict["labels"]

											return inputs_dict






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.model_tester.is_training:
																return

											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase				   =       True

											for model_class in self.all_model_classes:
																# DeiTForImageClassificationWithTeacher supports inference-only
																if (
																    model_class in get_values(_A			)
																    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																):
																					continue
																__lowerCAmelCase				   =       model_class(_A			)
																model.to(_A			)
																model.train()
																__lowerCAmelCase				   =       self._prepare_for_class(_A		,			_A		,			return_labels=_A			)
																__lowerCAmelCase				   =       model(**_A			).loss
																loss.backward()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																return

											__lowerCAmelCase				   =       False
											__lowerCAmelCase				   =       True

											for model_class in self.all_model_classes:
																if model_class in get_values(_A			) or not model_class.supports_gradient_checkpointing:
																					continue
																# DeiTForImageClassificationWithTeacher supports inference-only
																if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																					continue
																__lowerCAmelCase				   =       model_class(_A			)
																model.gradient_checkpointing_enable()
																model.to(_A			)
																model.train()
																__lowerCAmelCase				   =       self._prepare_for_class(_A		,			_A		,			return_labels=_A			)
																__lowerCAmelCase				   =       model(**_A			).loss
																loss.backward()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											__lowerCAmelCase				   =       [
											    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
											    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
											    {"title": "regression", "num_labels": 1, "dtype": torch.float},
											]

											for model_class in self.all_model_classes:
																if (
																    model_class
																    not in [
																        *get_values(_A			),
																        *get_values(_A			),
																    ]
																    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																):
																					continue

																for problem_type in problem_types:
																					with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""			):
																										__lowerCAmelCase				   =       problem_type["title"]
																										__lowerCAmelCase				   =       problem_type["num_labels"]

																										__lowerCAmelCase				   =       model_class(_A			)
																										model.to(_A			)
																										model.train()

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

																										if problem_type["num_labels"] > 1:
																															__lowerCAmelCase				   =       inputs["labels"].unsqueeze(1			).repeat(1		,			problem_type["num_labels"]			)

																										__lowerCAmelCase				   =       inputs["labels"].to(problem_type["dtype"]			)

																										# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																										# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																										# they have the same size." which is a symptom something in wrong for the regression problem.
																										# See https://github.com/huggingface/transformers/issues/11780
																										with warnings.catch_warnings(record=_A			) as warning_list:
																															__lowerCAmelCase				   =       model(**_A			).loss
																										for w in warning_list:
																															if "Using a target size that is different to the input size" in str(w.message			):
																																				raise ValueError(
																																				    f"""Something is going wrong in the regression problem: intercepted {w.message}"""			)

																										loss.backward()






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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







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


@require_torch
@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				   =       DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"			).to(
											    _A			)

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

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase				   =       model(**_A			)

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

											__lowerCAmelCase				   =       torch.tensor([-1.02_66, 0.19_12, -1.28_61]			).to(_A			)

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






						@slow
						@require_accelerate
						@require_torch_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTModel.from_pretrained(
											    "facebook/deit-base-distilled-patch16-224"		,			torch_dtype=torch.floataa		,			device_map="auto"			)
											__lowerCAmelCase				   =       self.default_image_processor

											__lowerCAmelCase				   =       prepare_img()
											__lowerCAmelCase				   =       image_processor(images=_A		,			return_tensors="pt"			)
											__lowerCAmelCase				   =       inputs.pixel_values.to(_A			)

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																__lowerCAmelCase				   =       model(_A			)




