



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							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=13					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=30					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=2					,		__SCREAMING_SNAKE_CASE							:					str=3					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=True					,		__SCREAMING_SNAKE_CASE							:					Any=True					,		__SCREAMING_SNAKE_CASE							:					List[str]=32					,		__SCREAMING_SNAKE_CASE							:					Dict=2					,		__SCREAMING_SNAKE_CASE							:					List[Any]=4					,		__SCREAMING_SNAKE_CASE							:					str=37					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="gelu"					,		__SCREAMING_SNAKE_CASE							:					Any=0.1					,		__SCREAMING_SNAKE_CASE							:					Dict=0.1					,		__SCREAMING_SNAKE_CASE							:					List[str]=10					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0.02					,		__SCREAMING_SNAKE_CASE							:					str=3					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=2					,		):


           '''simple docstring'''

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

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

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

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

           __a							=    self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase      (		self							:					Any):


           '''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=UpperCamelCase__					,		initializer_range=self.initializer_range					,		encoder_stride=self.encoder_stride					,		)



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

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

           # test greyscale images
           __a							=    1
           __a							=    TFDeiTForMaskedImageModeling(UpperCamelCase__)

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

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

           # test greyscale images
           __a							=    1
           __a							=    TFDeiTForImageClassification(UpperCamelCase__)

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





       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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


@require_tf
class    _A	(     __a				,__a				,unittest.TestCase   ):
       UpperCamelCase__  :   Optional[Any]						 =						(
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase__  :   int						 =						(
           {
               '''feature-extraction''': TFDeiTModel,
               '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       UpperCamelCase__  :   Optional[int]						 =						False
       UpperCamelCase__  :   Union[str, Any]						 =						False
       UpperCamelCase__  :   Any						 =						False
       UpperCamelCase__  :   int						 =						False



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    TFDeiTModelTester(self)
           __a							=    ConfigTester(self					,		config_class=UpperCamelCase__					,		has_text_modality=UpperCamelCase__					,		hidden_size=37)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           self.config_tester.run_common_tests()



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


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a							=    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]					,		UpperCamelCase__)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__)



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					int=False):


           '''simple docstring'''

           __a							=    super()._prepare_for_class(UpperCamelCase__					,		UpperCamelCase__					,		return_labels=UpperCamelCase__)

           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 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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





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

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



       @cached_property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

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





       @slow
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''')

           __a							=    self.default_image_processor
           __a							=    prepare_img()
           __a							=    image_processor(images=UpperCamelCase__					,		return_tensors='''tf''')

           # forward pass
           __a							=    model(**UpperCamelCase__)

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

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

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







import os
import unicodedata
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 SPIECE_UNDERLINE, logging


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[str]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Dict						=					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=False					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<s>"					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<sep>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<pad>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<cls>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<mask>"					,		__SCREAMING_SNAKE_CASE							:					Any=["<eop>", "<eod>"]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           __a							=    AddedToken(__SCREAMING_SNAKE_CASE					,		lstrip=__SCREAMING_SNAKE_CASE					,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else mask_token

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__SCREAMING_SNAKE_CASE					,		remove_space=__SCREAMING_SNAKE_CASE					,		keep_accents=__SCREAMING_SNAKE_CASE					,		bos_token=__SCREAMING_SNAKE_CASE					,		eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		sep_token=__SCREAMING_SNAKE_CASE					,		pad_token=__SCREAMING_SNAKE_CASE					,		cls_token=__SCREAMING_SNAKE_CASE					,		mask_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    3

           __a							=    do_lower_case
           __a							=    remove_space
           __a							=    keep_accents
           __a							=    vocab_file

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)

           try:
               import jieba
           except ModuleNotFoundError as error:
               raise error.__class__(
                   '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                   '''See https://pypi.org/project/jieba/ for installation.''')
           __a							=    jieba
           __a							=    str.maketrans(''' \n'''					,		'''\u2582\u2583''')



       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return len(self.sp_model)



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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



       def __getstate__(		self							:					Any):


           '''simple docstring'''

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



       def __setstate__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    d

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

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           if self.remove_space:
               __a							=    ''' '''.join(inputs.strip().split())
           else:
               __a							=    inputs
           __a							=    outputs.replace('''``'''					,		'''"''').replace('''\'\''''					,		'''"''')

           if not self.keep_accents:
               __a							=    unicodedata.normalize('''NFKD'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    ''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
           if self.do_lower_case:
               __a							=    outputs.lower()

           return outputs



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    self.preprocess_text(__SCREAMING_SNAKE_CASE)
           __a							=    self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)
           __a							=    []
           for piece in pieces:
               if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                   __a							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE					,		''''''))
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                       if len(cur_pieces[0]) == 1:
                           __a							=    cur_pieces[1:]
                       else:
                           __a							=    cur_pieces[0][1:]
                   cur_pieces.append(piece[-1])
                   new_pieces.extend(__SCREAMING_SNAKE_CASE)
               else:
                   new_pieces.append(__SCREAMING_SNAKE_CASE)

           return new_pieces



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE					,		''' ''').strip()
           return out_string



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

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

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [2]

           if token_ids_a is None:
               return len(token_ids_a + sep) * [0] + cls_segment_id
           return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

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

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

           return (out_vocab_file,)





       def 		_lowerCamelCase      (		self							:					Any					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    super()._decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    text.replace(''' '''					,		'''''').replace('''\u2582'''					,		''' ''').replace('''\u2583'''					,		'''\n''')
           return text





