
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    lowerCamelCase      ="pt"
elif is_tf_available():
    lowerCamelCase      ="tf"
else:
    lowerCamelCase      ="jax"




class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  PerceiverTokenizer
     SCREAMING_SNAKE_CASE_		   =  False






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




           """simple docstring"""







           super().setUp()
           UpperCamelCase__    :   int					=				PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname  )






     @cached_property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   PerceiverTokenizer:




           """simple docstring"""







           return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=2_0						,						__SCREAMING_SNAKE_CASE=5  )  ->   Tuple[str, list]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				[]
           for i in range(len(__SCREAMING_SNAKE_CASE  )  ):
                 try:
                       UpperCamelCase__    :   Dict					=				tokenizer.decode([i]						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE  )
                 except UnicodeDecodeError:
                       pass
                 toks.append((i, tok)  )

           UpperCamelCase__    :   Union[str, Any]					=				list(filter(lambda __SCREAMING_SNAKE_CASE  : re.match(r'''^[ a-zA-Z]+$'''						,						t[1]  )						,						__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Union[str, Any]					=				list(filter(lambda __SCREAMING_SNAKE_CASE  : [t[0]] == tokenizer.encode(t[1]						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )						,						__SCREAMING_SNAKE_CASE  )  )
           if max_length is not None and len(__SCREAMING_SNAKE_CASE  ) > max_length:
                 UpperCamelCase__    :   Tuple					=				toks[:max_length]
           if min_length is not None and len(__SCREAMING_SNAKE_CASE  ) < min_length and len(__SCREAMING_SNAKE_CASE  ) > 0:
                 while len(__SCREAMING_SNAKE_CASE  ) < min_length:
                       UpperCamelCase__    :   Dict					=				toks + toks
        # toks_str = [t[1] for t in toks]
           UpperCamelCase__    :   List[Any]					=				[t[0] for t in toks]

           # Ensure consistency
           UpperCamelCase__    :   Any					=				tokenizer.decode(__SCREAMING_SNAKE_CASE						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE  )
           if " " not in output_txt and len(__SCREAMING_SNAKE_CASE  ) > 1:
                 UpperCamelCase__    :   Optional[Any]					=				(
                     tokenizer.decode([toks_ids[0]]						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE  )
                     + ''' '''
                     + tokenizer.decode(toks_ids[1:]						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE  )
                 )
           if with_prefix_space:
                 UpperCamelCase__    :   List[Any]					=				''' ''' + output_txt
           UpperCamelCase__    :   Optional[Any]					=				tokenizer.encode(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )
           return output_txt, output_ids






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.perceiver_tokenizer
           UpperCamelCase__    :   List[Any]					=				'''Unicode €.'''
           UpperCamelCase__    :   Tuple					=				tokenizer(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
           self.assertEqual(encoded['''input_ids''']						,						__SCREAMING_SNAKE_CASE  )

           # decoding
           UpperCamelCase__    :   Any					=				tokenizer.decode(__SCREAMING_SNAKE_CASE  )
           self.assertEqual(__SCREAMING_SNAKE_CASE						,						'''[CLS]Unicode €.[SEP]'''  )

           UpperCamelCase__    :   Dict					=				tokenizer('''e è é ê ë'''  )
           UpperCamelCase__    :   List[Any]					=				[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
           self.assertEqual(encoded['''input_ids''']						,						__SCREAMING_SNAKE_CASE  )
           # decoding
           UpperCamelCase__    :   Any					=				tokenizer.decode(__SCREAMING_SNAKE_CASE  )
           self.assertEqual(__SCREAMING_SNAKE_CASE						,						'''[CLS]e è é ê ë[SEP]'''  )

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''  )  )						,						'''[CLS]e è é ê ë[SEP]'''  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.perceiver_tokenizer
           UpperCamelCase__    :   Dict					=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCamelCase__    :   Union[str, Any]					=				[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
           # fmt: on
           UpperCamelCase__    :   Tuple					=				tokenizer(__SCREAMING_SNAKE_CASE						,						padding=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE  )
           self.assertIsInstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           if FRAMEWORK != "jax":
                 UpperCamelCase__    :   Tuple					=				list(batch.input_ids.numpy()[0]  )
           else:
                 UpperCamelCase__    :   Tuple					=				list(batch.input_ids.tolist()[0]  )

           self.assertListEqual(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           self.assertEqual((2, 3_8)						,						batch.input_ids.shape  )
           self.assertEqual((2, 3_8)						,						batch.attention_mask.shape  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.perceiver_tokenizer
           UpperCamelCase__    :   str					=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCamelCase__    :   Union[str, Any]					=				tokenizer(__SCREAMING_SNAKE_CASE						,						padding=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE  )
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('''input_ids'''						,						__SCREAMING_SNAKE_CASE  )
           self.assertIn('''attention_mask'''						,						__SCREAMING_SNAKE_CASE  )
           self.assertNotIn('''decoder_input_ids'''						,						__SCREAMING_SNAKE_CASE  )
           self.assertNotIn('''decoder_attention_mask'''						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.perceiver_tokenizer
           UpperCamelCase__    :   List[Any]					=				[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCamelCase__    :   Optional[int]					=				tokenizer(
               text_target=__SCREAMING_SNAKE_CASE						,						max_length=3_2						,						padding='''max_length'''						,						truncation=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE  )
           self.assertEqual(3_2						,						targets['''input_ids'''].shape[1]  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                       self.assertNotEqual(tokenizer.model_max_length						,						4_2  )

        # Now let's start the test
           UpperCamelCase__    :   Dict					=				self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                       # Isolate this from the other tests because we save additional tokens/etc
                       UpperCamelCase__    :   Optional[Any]					=				tempfile.mkdtemp()

                       UpperCamelCase__    :   Optional[int]					=				''' He is very happy, UNwant\u00E9d,running'''
                       UpperCamelCase__    :   str					=				tokenizer.encode(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )
                       tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

                       UpperCamelCase__    :   Optional[Any]					=				tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   int					=				after_tokenizer.encode(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )
                       self.assertListEqual(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

                       shutil.rmtree(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[Any]					=				self.get_tokenizers(model_max_length=4_2  )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                       # Isolate this from the other tests because we save additional tokens/etc
                       UpperCamelCase__    :   int					=				tempfile.mkdtemp()

                       UpperCamelCase__    :   int					=				''' He is very happy, UNwant\u00E9d,running'''
                       tokenizer.add_tokens(['''bim''', '''bambam''']  )
                       UpperCamelCase__    :   Tuple					=				tokenizer.additional_special_tokens
                       additional_special_tokens.append('''new_additional_special_token'''  )
                       tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}  )
                       UpperCamelCase__    :   Any					=				tokenizer.encode(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )
                       tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

                       UpperCamelCase__    :   Optional[Any]					=				tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Optional[Any]					=				after_tokenizer.encode(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE  )
                       self.assertListEqual(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                       self.assertIn('''new_additional_special_token'''						,						after_tokenizer.additional_special_tokens  )
                       self.assertEqual(after_tokenizer.model_max_length						,						4_2  )

                       UpperCamelCase__    :   int					=				tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE						,						model_max_length=4_3  )
                       self.assertEqual(tokenizer.model_max_length						,						4_3  )

                       shutil.rmtree(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				[]
           if self.test_slow_tokenizer:
                 tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

           if self.test_rust_tokenizer:
                 tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE  )

                       with open(os.path.join(__SCREAMING_SNAKE_CASE						,						'''special_tokens_map.json'''  )						,						encoding='''utf-8'''  ) as json_file:
                             UpperCamelCase__    :   Dict					=				json.load(__SCREAMING_SNAKE_CASE  )

                       with open(os.path.join(__SCREAMING_SNAKE_CASE						,						'''tokenizer_config.json'''  )						,						encoding='''utf-8'''  ) as json_file:
                             UpperCamelCase__    :   List[str]					=				json.load(__SCREAMING_SNAKE_CASE  )

                       UpperCamelCase__    :   Tuple					=				[F'''<extra_id_{i}>''' for i in range(1_2_5  )]

                       UpperCamelCase__    :   List[Any]					=				added_tokens_extra_ids + [
                           '''an_additional_special_token'''
                       ]
                       UpperCamelCase__    :   Union[str, Any]					=				added_tokens_extra_ids + [
                           '''an_additional_special_token'''
                       ]

                       with open(os.path.join(__SCREAMING_SNAKE_CASE						,						'''special_tokens_map.json'''  )						,						'''w'''						,						encoding='''utf-8'''  ) as outfile:
                             json.dump(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                       with open(os.path.join(__SCREAMING_SNAKE_CASE						,						'''tokenizer_config.json'''  )						,						'''w'''						,						encoding='''utf-8'''  ) as outfile:
                             json.dump(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

                       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                       # "special_tokens_map.json" files
                       UpperCamelCase__    :   Tuple					=				tokenizer_class.from_pretrained(
                           __SCREAMING_SNAKE_CASE						,						)
                       self.assertIn(
                           '''an_additional_special_token'''						,						tokenizer_without_change_in_init.additional_special_tokens  )
                       self.assertEqual(
                           ['''an_additional_special_token''']						,						tokenizer_without_change_in_init.convert_ids_to_tokens(
                               tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']  )  )						,						)

                       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                       UpperCamelCase__    :   List[Any]					=				added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''						,						lstrip=__SCREAMING_SNAKE_CASE  )]
                       UpperCamelCase__    :   Tuple					=				tokenizer_class.from_pretrained(
                           __SCREAMING_SNAKE_CASE						,						additional_special_tokens=__SCREAMING_SNAKE_CASE						,						)

                       self.assertIn('''a_new_additional_special_token'''						,						tokenizer.additional_special_tokens  )
                       self.assertEqual(
                           ['''a_new_additional_special_token''']						,						tokenizer.convert_ids_to_tokens(
                               tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']  )  )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([1_7_8]  )						,						'''�'''  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE						,						do_lower_case=__SCREAMING_SNAKE_CASE  )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                       UpperCamelCase__    :   int					=				['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                       UpperCamelCase__    :   str					=				tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE  )

                       self.assertIsInstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )




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 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=3_0						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=3_7						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=2						,						)  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				parent
           UpperCamelCase__    :   Optional[int]					=				batch_size
           UpperCamelCase__    :   Optional[Any]					=				image_size
           UpperCamelCase__    :   Optional[int]					=				patch_size
           UpperCamelCase__    :   Any					=				num_channels
           UpperCamelCase__    :   Optional[Any]					=				is_training
           UpperCamelCase__    :   Dict					=				use_labels
           UpperCamelCase__    :   Optional[int]					=				hidden_size
           UpperCamelCase__    :   str					=				num_hidden_layers
           UpperCamelCase__    :   int					=				num_attention_heads
           UpperCamelCase__    :   List[Any]					=				intermediate_size
           UpperCamelCase__    :   int					=				hidden_act
           UpperCamelCase__    :   int					=				hidden_dropout_prob
           UpperCamelCase__    :   Optional[Any]					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Tuple					=				type_sequence_label_size
           UpperCamelCase__    :   Optional[int]					=				initializer_range
           UpperCamelCase__    :   List[str]					=				scope
           UpperCamelCase__    :   str					=				encoder_stride

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

           UpperCamelCase__    :   List[Any]					=				self.get_config()

           return config, pixel_values, labels






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """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=__SCREAMING_SNAKE_CASE						,						initializer_range=self.initializer_range						,						encoder_stride=self.encoder_stride						,						)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				TFDeiTModel(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)  )






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




           """simple docstring"""







           UpperCamelCase__    :   str					=				TFDeiTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(
               result.reconstruction.shape						,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

           # test greyscale images
           UpperCamelCase__    :   Optional[int]					=				1
           UpperCamelCase__    :   Any					=				TFDeiTForMaskedImageModeling(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[Any]					=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
           UpperCamelCase__    :   Any					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.reconstruction.shape						,						(self.batch_size, 1, self.image_size, self.image_size)  )






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




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.type_sequence_label_size
           UpperCamelCase__    :   Optional[Any]					=				TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				model(__SCREAMING_SNAKE_CASE						,						labels=__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)  )

           # test greyscale images
           UpperCamelCase__    :   int					=				1
           UpperCamelCase__    :   int					=				TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
           UpperCamelCase__    :   Optional[Any]					=				model(__SCREAMING_SNAKE_CASE						,						labels=__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.prepare_config_and_inputs()
           UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Any					=				config_and_inputs
           UpperCamelCase__    :   Optional[int]					=				{'''pixel_values''': pixel_values}
           return config, inputs_dict




@require_tf
class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  (
         {
             '''feature-extraction''': TFDeiTModel,
             '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






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




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				TFDeiTModelTester(self  )
           UpperCamelCase__    :   str					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						has_text_modality=__SCREAMING_SNAKE_CASE						,						hidden_size=3_7  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           self.config_tester.run_common_tests()






     @unittest.skip(reason='''DeiT does not use inputs_embeds'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 UpperCamelCase__    :   List[Any]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 self.assertIsInstance(model.get_input_embeddings()						,						(tf.keras.layers.Layer)  )
                 UpperCamelCase__    :   Dict					=				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE						,						tf.keras.layers.Dense  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				self.model_tester.prepare_config_and_inputs_for_common()

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

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=False  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				super()._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						return_labels=__SCREAMING_SNAKE_CASE  )

           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  )  ->   Optional[int]:




           """simple docstring"""







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


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Tuple					=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




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





     """simple docstring"""







     @cached_property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







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






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''  )

           UpperCamelCase__    :   str					=				self.default_image_processor
           UpperCamelCase__    :   List[str]					=				prepare_img()
           UpperCamelCase__    :   int					=				image_processor(images=__SCREAMING_SNAKE_CASE						,						return_tensors='''tf'''  )

           # forward pass
           UpperCamelCase__    :   List[Any]					=				model(**__SCREAMING_SNAKE_CASE  )

           # verify the logits
           UpperCamelCase__    :   Union[str, Any]					=				tf.TensorShape((1, 1_0_0_0)  )
           self.assertEqual(outputs.logits.shape						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				tf.constant([-1.0266, 0.1912, -1.2861]  )

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


