


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase    =					logging.get_logger(__name__)

lowerCamelCase    =					"""▁"""

lowerCamelCase    =					{"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

lowerCamelCase    =					{
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

lowerCamelCase    =					{
    """vocab_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
    },
    """sentencepiece_model_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
    },
}

lowerCamelCase    =					{
    """ernie-m-base""": 514,
    """ernie-m-large""": 514,
}

lowerCamelCase    =					{
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						["input_ids"]

 __snake_case				=						VOCAB_FILES_NAMES
 __snake_case				=						PRETRAINED_INIT_CONFIGURATION
 __snake_case				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case				=						PRETRAINED_VOCAB_FILES_MAP
 __snake_case				=						RESOURCE_FILES_NAMES



 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=False	,   _UpperCAmelCase="utf8"	,   _UpperCAmelCase="[UNK]"	,   _UpperCAmelCase="[SEP]"	,   _UpperCAmelCase="[PAD]"	,   _UpperCAmelCase="[CLS]"	,   _UpperCAmelCase="[MASK]"	,   _UpperCAmelCase = None	,   **_UpperCAmelCase	,   ):
       snake_case_ =   {} if sp_model_kwargs is None else sp_model_kwargs
       super().__init__(
           do_lower_case=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   sep_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   cls_token=__lowerCAmelCase	,   mask_token=__lowerCAmelCase	,   vocab_file=__lowerCAmelCase	,   encoding=__lowerCAmelCase	,   sp_model_kwargs=self.sp_model_kwargs	,   **__lowerCAmelCase	,   )
       snake_case_ =   do_lower_case
       snake_case_ =   sentencepiece_model_ckpt
       snake_case_ =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(__lowerCAmelCase      )

       # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
       if vocab_file is not None:
             snake_case_ =   self.load_vocab(filepath=__lowerCAmelCase      )
       else:
             snake_case_ =   {self.sp_model.id_to_piece(__lowerCAmelCase      ): id for id in range(self.sp_model.get_piece_size()      )}
       snake_case_ =   {v: k for k, v in self.vocab.items()}



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):







       if text is None:
             return None

       snake_case_ =   self.tokenize(__lowerCAmelCase      )
       snake_case_ ,					snake_case_ =   '''''', []

       for i, ch in enumerate(__lowerCAmelCase      ):
             if ch in self.SP_CHAR_MAPPING:
                   snake_case_ =   self.SP_CHAR_MAPPING.get(__lowerCAmelCase      )
             else:
                   snake_case_ =   unicodedata.normalize('''NFKC'''	,   __lowerCAmelCase      )
             if self.is_whitespace(__lowerCAmelCase      ):
                   continue
             normalized_text += ch
             char_mapping.extend([i] * len(__lowerCAmelCase      )      )

       snake_case_ ,					snake_case_ ,					snake_case_ =   normalized_text, [], 0

       if self.do_lower_case:
             snake_case_ =   text.lower()

       for token in split_tokens:
             if token[:1] == "▁":
                   snake_case_ =   token[1:]
             snake_case_ =   text[offset:].index(__lowerCAmelCase      ) + offset
             snake_case_ =   start + len(__lowerCAmelCase      )

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)      )
             snake_case_ =   end
       return token_mapping



 @property
 def 		UpperCamelCase__  (			self      ):
       return len(self.vocab      )



 def 		UpperCamelCase__  (			self      ):
       return dict(self.vocab	,   **self.added_tokens_encoder      )



 def __getstate__(			self      ):
       snake_case_ =   self.__dict__.copy()
       snake_case_ =   None
       return state



 def __setstate__(			self	,   _UpperCAmelCase      ):
       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.sentencepiece_model_ckpt      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return "".join((self.SP_CHAR_MAPPING.get(__lowerCAmelCase	,   __lowerCAmelCase      ) for c in text)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=False	,   _UpperCAmelCase=64	,   _UpperCAmelCase=0.1      ):







       if self.sp_model_kwargs.get('''enable_sampling'''      ) is True:
             snake_case_ =   True
       if self.sp_model_kwargs.get('''alpha'''      ) is not None:
             snake_case_ =   self.sp_model_kwargs.get('''alpha'''      )
       if self.sp_model_kwargs.get('''nbest_size'''      ) is not None:
             snake_case_ =   self.sp_model_kwargs.get('''nbest_size'''      )

       if not enable_sampling:
             snake_case_ =   self.sp_model.EncodeAsPieces(__lowerCAmelCase      )
       else:
             snake_case_ =   self.sp_model.SampleEncodeAsPieces(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase      )
       snake_case_ =   []
       for pi, piece in enumerate(__lowerCAmelCase      ):
             if piece == SPIECE_UNDERLINE:
                   if not pieces[pi + 1].startswith(__lowerCAmelCase      ) and pi != 0:
                         new_pieces.append(__lowerCAmelCase      )
                         continue
                   else:
                         continue
             snake_case_ =   0
             for i, chunk in enumerate(__lowerCAmelCase      ):
                   if chunk == SPIECE_UNDERLINE:
                         continue
                   if self.is_ch_char(__lowerCAmelCase      ) or self.is_punct(__lowerCAmelCase      ):
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]      )
                         new_pieces.append(__lowerCAmelCase      )
                         snake_case_ =   i + 1
                   elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]      )
                         snake_case_ =   i
                   elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]      )
                         snake_case_ =   i
             if len(__lowerCAmelCase      ) > lst_i:
                   new_pieces.append(piece[lst_i:]      )
       return new_pieces



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase	,   ''' '''      ).strip()
       return out_string



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.convert_ids_to_tokens(__lowerCAmelCase      )
       snake_case_ =   ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase	,   ''' '''      ).strip()
       return out_string



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return self.vocab.get(__lowerCAmelCase	,   self.vocab.get(self.unk_token      )      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return self.reverse_vocab.get(__lowerCAmelCase	,   self.unk_token      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None      ):







       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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None      ):







       if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

       return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=False      ):







       if already_has_special_tokens:
             if token_ids_a is not None:
                   raise ValueError(
                       '''You should not supply a second sequence if the provided sequence of '''
                       '''ids is already formatted with special tokens for the model.'''      )
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
             return [1] + ([0] * len(__lowerCAmelCase      )) + [1, 1] + ([0] * len(__lowerCAmelCase      )) + [1]
       return [1] + ([0] * len(__lowerCAmelCase      )) + [1]



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):







       if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(__lowerCAmelCase      ) + 2) * [0]

       # [CLS] A [SEP] [SEP] B [SEP]
       return [0] * (len(__lowerCAmelCase      ) + 1) + [1] * (len(__lowerCAmelCase      ) + 3)



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):







       if "\u4e00" <= char <= "\u9fff":
             return True
       return False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):







       if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
       return False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):







       if char in ",;:.?!~，；：。？！《》【】":
             return True
       return False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):







       if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
       if len(__lowerCAmelCase      ) == 1:
             snake_case_ =   unicodedata.category(__lowerCAmelCase      )
             if cat == "Zs":
                   return True
       return False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   {}
       with io.open(__lowerCAmelCase	,   '''r'''	,   encoding='''utf-8'''      ) as f:
             for index, line in enumerate(__lowerCAmelCase      ):
                   snake_case_ =   line.rstrip('''\n'''      )
                   snake_case_ =   int(__lowerCAmelCase      )

       return token_to_idx





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   0
       if os.path.isdir(__lowerCAmelCase      ):
             snake_case_ =   os.path.join(
                 __lowerCAmelCase	,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
       else:
             snake_case_ =   (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
       with open(__lowerCAmelCase	,   '''w'''	,   encoding='''utf-8'''      ) as writer:
             for token, token_index in sorted(self.vocab.items()	,   key=lambda _UpperCAmelCase      : kv[1]      ):
                   if index != token_index:
                         logger.warning(
                             F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                             ''' Please check that the vocabulary is not corrupted!'''      )
                         snake_case_ =   token_index
                   writer.write(token + '''\n'''      )
                   index += 1

       snake_case_ =   os.path.join(__lowerCAmelCase	,   '''sentencepiece.bpe.model'''      )
       with open(__lowerCAmelCase	,   '''wb'''      ) as fi:
             snake_case_ =   self.sp_model.serialized_model_proto()
             fi.write(__lowerCAmelCase      )

       return (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 lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=13	,   _UpperCAmelCase=30	,   _UpperCAmelCase=2	,   _UpperCAmelCase=3	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=10	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=3	,   _UpperCAmelCase=None	,   _UpperCAmelCase=2	,   ):
       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
       snake_case_ =   encoder_stride

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



 def 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       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=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   encoder_stride=self.encoder_stride	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTForMaskedImageModeling(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(
           result.reconstruction.shape	,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForMaskedImageModeling(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.type_sequence_label_size
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

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





 def 		UpperCamelCase__  (			self      ):
       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_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __snake_case				=						(
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __snake_case				=						False
 __snake_case				=						False
 __snake_case				=						False



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   has_text_modality=_UpperCAmelCase	,   hidden_size=37      )



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



 @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
 def 		UpperCamelCase__  (			self      ):
       pass



 def 		UpperCamelCase__  (			self      ):
       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(_UpperCAmelCase      )
             self.assertIsInstance(model.get_input_embeddings()	,   (nn.Module)      )
             snake_case_ =   model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase	,   nn.Linear      )      )



 def 		UpperCamelCase__  (			self      ):
       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(_UpperCAmelCase      )
             snake_case_ =   inspect.signature(model.forward      )
             # 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]	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=False      ):
       snake_case_ =   super()._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

       if return_labels:
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict



 def 		UpperCamelCase__  (			self      ):
       if not self.model_tester.is_training:
             return

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

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       snake_case_ =   False
       snake_case_ =   True

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_ =   [
           {'''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(_UpperCAmelCase      ),
                     *get_values(_UpperCAmelCase      ),
                 ]
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue

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

                         snake_case_ =   model_class(_UpperCAmelCase      )
                         model.to(_UpperCAmelCase      )
                         model.train()

                         snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

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

                         snake_case_ =   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=_UpperCAmelCase      ) as warning_list:
                               snake_case_ =   model(**_UpperCAmelCase      ).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 		UpperCamelCase__  (			self      ):
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_ =   DeiTModel.from_pretrained(_UpperCAmelCase      )
             self.assertIsNotNone(_UpperCAmelCase      )






def  __lowerCAmelCase					()->						Optional[int]:




      """simple docstring"""

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







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

 '''simple docstring'''

 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
           if is_vision_available()
           else None
       )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      ).to(
           _UpperCAmelCase      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

       # forward pass
       with torch.no_grad():
             snake_case_ =   model(**_UpperCAmelCase      )

       # verify the logits
       snake_case_ =   torch.Size((1, 10_00)      )
       self.assertEqual(outputs.logits.shape	,   _UpperCAmelCase      )

       snake_case_ =   torch.tensor([-1.0_266, 0.1_912, -1.2_861]      ).to(_UpperCAmelCase      )

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





 @slow
 @require_accelerate
 @require_torch_gpu
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTModel.from_pretrained(
           '''facebook/deit-base-distilled-patch16-224'''	,   torch_dtype=torch.floataa	,   device_map='''auto'''      )
       snake_case_ =   self.default_image_processor

       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      )
       snake_case_ =   inputs.pixel_values.to(_UpperCAmelCase      )

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