


import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

__lowerCAmelCase			=							{
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

__lowerCAmelCase			=							{
    '''jukebox''': 5_12,
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Dict       					=			VOCAB_FILES_NAMES
 __lowercase			:							Tuple       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							Tuple       					=			PRETRAINED_LYRIC_TOKENS_SIZES
 __lowercase			:							Dict       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=["v3", "v2", "v2"]    ,			lowerCAmelCase__=512    ,			lowerCAmelCase__=5    ,			lowerCAmelCase__="<|endoftext|>"    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else unk_token
        super().__init__(
            unk_token=lowerCAmelCase__    ,			n_genres=lowerCAmelCase__    ,			version=lowerCAmelCase__    ,			max_n_lyric_tokens=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
        lowercase__:       List[str]        =       version
        lowercase__:       Any        =       max_n_lyric_tokens
        lowercase__:       Optional[Any]        =       n_genres

        with open(lowerCAmelCase__    ,			encoding='utf-8'			) as vocab_handle:
               lowercase__:       str        =       json.load(lowerCAmelCase__			)

        with open(lowerCAmelCase__    ,			encoding='utf-8'			) as vocab_handle:
               lowercase__:       int        =       json.load(lowerCAmelCase__			)

        with open(lowerCAmelCase__    ,			encoding='utf-8'			) as vocab_handle:
               lowercase__:       List[Any]        =       json.load(lowerCAmelCase__			)

        lowercase__:       List[str]        =       R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
        # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
        if len(self.lyrics_encoder			) == 79:
               lowercase__:       Tuple        =       oov.replace(R'\-\''    ,			R'\-+\''			)

        lowercase__:       Optional[int]        =       regex.compile(lowerCAmelCase__			)
        lowercase__:       int        =       {v: k for k, v in self.artists_encoder.items()}
        lowercase__:       Any        =       {v: k for k, v in self.genres_encoder.items()}
        lowercase__:       Union[str, Any]        =       {v: k for k, v in self.lyrics_encoder.items()}
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return len(self.artists_encoder			) + len(self.genres_encoder			) + len(self.lyrics_encoder			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return dict(self.artists_encoder    ,			self.genres_encoder    ,			self.lyrics_encoder			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       [self.artists_encoder.get(lowerCAmelCase__    ,			0			) for artist in list_artists]
        for genres in range(len(lowerCAmelCase__			)			):
               lowercase__:       str        =       [self.genres_encoder.get(lowerCAmelCase__    ,			0			) for genre in list_genres[genres]]
               lowercase__:       Tuple        =       list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]			))

        lowercase__:       Tuple        =       [[self.lyrics_encoder.get(lowerCAmelCase__    ,			0			) for character in list_lyrics[0]], [], []]
        return artists_id, list_genres, lyric_ids
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        return list(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__      ,						lowercase__      ,						lowercase__:       List[Any]        =       self.prepare_for_tokenization(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       self._tokenize(lowerCAmelCase__			)
        return artist, genre, lyrics
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = False			) ->    Tuple[str, str, str, Dict[str, Any]]:




        '''simple docstring'''
        for idx in range(len(self.version			)			):
               if self.version[idx] == "v3":
                      lowercase__:       Optional[Any]        =       artists[idx].lower()
                      lowercase__:       List[Any]        =       [genres[idx].lower()]
               else:
                      lowercase__:       int        =       self._normalize(artists[idx]			) + '.v2'
                      lowercase__:       Tuple        =       [
                          self._normalize(lowerCAmelCase__			) + '.v2' for genre in genres[idx].split('_'			)
                      ]  # split is for the full dictionary with combined genres

        if self.version[0] == "v2":
               lowercase__:       int        =       regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'			)
               lowercase__:       str        =       'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
               lowercase__:       Dict        =       {vocab[index]: index + 1 for index in range(len(lowerCAmelCase__			)			)}
               lowercase__:       int        =       0
               lowercase__:       Optional[Any]        =       len(lowerCAmelCase__			) + 1
               lowercase__:       List[Any]        =       self.vocab
               lowercase__:       Optional[int]        =       {v: k for k, v in self.vocab.items()}
               lowercase__:       Tuple        =       ''
        else:
               lowercase__:       Optional[int]        =       regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'			)

        lowercase__:       str        =       self._run_strip_accents(lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       lyrics.replace('\\'    ,			'\n'			)
        lowercase__:       Optional[Any]        =       self.out_of_vocab.sub(''    ,			lowerCAmelCase__			), [], []
        return artists, genres, lyrics
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        lowercase__:       List[str]        =       unicodedata.normalize('NFD'    ,			lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       []
        for char in text:
               lowercase__:       Optional[Any]        =       unicodedata.category(lowerCAmelCase__			)
               if cat == "Mn":
                      continue
               output.append(lowerCAmelCase__			)
        return "".join(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       (
            [chr(lowerCAmelCase__			) for i in range(ord('a'			)    ,			ord('z'			) + 1			)]
            + [chr(lowerCAmelCase__			) for i in range(ord('A'			)    ,			ord('Z'			) + 1			)]
            + [chr(lowerCAmelCase__			) for i in range(ord('0'			)    ,			ord('9'			) + 1			)]
            + ['.']
        )
        lowercase__:       Union[str, Any]        =       frozenset(lowerCAmelCase__			)
        lowercase__:       int        =       re.compile(R'_+'			)
        lowercase__:       Any        =       ''.join([c if c in accepted else '_' for c in text.lower()]			)
        lowercase__:       Optional[int]        =       pattern.sub('_'    ,			lowerCAmelCase__			).strip('_'			)
        return text
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        return " ".join(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False			) ->    Tuple:




        '''simple docstring'''
        # Convert to TensorType
        if not isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			):
               lowercase__:       Dict        =       TensorType(lowerCAmelCase__			)

        # Get a function reference for the correct framework
        if tensor_type == TensorType.TENSORFLOW:
               if not is_tf_available():
                      raise ImportError(
                          'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'			)
               import tensorflow as tf

               lowercase__:       str        =       tf.constant
               lowercase__:       List[str]        =       tf.is_tensor
        elif tensor_type == TensorType.PYTORCH:
               if not is_torch_available():
                      raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'			)
               import torch

               lowercase__:       Tuple        =       torch.tensor
               lowercase__:       int        =       torch.is_tensor
        elif tensor_type == TensorType.JAX:
               if not is_flax_available():
                      raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'			)
               import jax.numpy as jnp  # noqa: F811

               lowercase__:       Any        =       jnp.array
               lowercase__:       Tuple        =       _is_jax
        else:
               lowercase__:       Optional[Any]        =       np.asarray
               lowercase__:       List[Any]        =       _is_numpy

        # Do the tensor conversion in batch

        try:
               if prepend_batch_axis:
                      lowercase__:       Optional[Any]        =       [inputs]

               if not is_tensor(lowerCAmelCase__			):
                      lowercase__:       Dict        =       as_tensor(lowerCAmelCase__			)
        except:  # noqa E722
               raise ValueError(
                   'Unable to create tensor, you should probably activate truncation and/or padding '
                   'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'			)

        return inputs
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=""    ,			lowerCAmelCase__="pt"			) ->    BatchEncoding:




        '''simple docstring'''
        lowercase__:       List[str]        =       [0, 0, 0]
        lowercase__:       List[Any]        =       [artist] * len(self.version			)
        lowercase__:       str        =       [genres] * len(self.version			)

        lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       self.tokenize(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       self._convert_token_to_id(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       str        =       [-INFINITY] * len(full_tokens[-1]			)
        lowercase__:       int        =       [
            self.convert_to_tensors(
                [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,			tensor_type=lowerCAmelCase__			)
            for i in range(len(self.version			)			)
        ]
        return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return

        lowercase__:       Tuple        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']			)
        with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as f:
               f.write(json.dumps(self.artists_encoder    ,			ensure_ascii=lowerCAmelCase__			)			)

        lowercase__:       List[Any]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']			)
        with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as f:
               f.write(json.dumps(self.genres_encoder    ,			ensure_ascii=lowerCAmelCase__			)			)

        lowercase__:       Tuple        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']			)
        with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as f:
               f.write(json.dumps(self.lyrics_encoder    ,			ensure_ascii=lowerCAmelCase__			)			)

        return (artists_file, genres_file, lyrics_file)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.artists_decoder.get(lowerCAmelCase__			)
        lowercase__:       Dict        =       [self.genres_decoder.get(lowerCAmelCase__			) for genre in genres_index]
        lowercase__:       Optional[int]        =       [self.lyrics_decoder.get(lowerCAmelCase__			) for character in lyric_index]
        return artist, genres, lyrics






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    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=2    ,			) ->    List[str]:




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

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        lowercase__:       List[str]        =       (image_size // patch_size) ** 2
        lowercase__:       Tuple        =       num_patches + 2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__:       int        =       None
        if self.use_labels:
               lowercase__:       str        =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size			)

        lowercase__:       List[str]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[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=lowerCAmelCase__    ,			initializer_range=self.initializer_range    ,			encoder_stride=self.encoder_stride    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       TFDeiTModel(config=lowerCAmelCase__			)
        lowercase__:       Any        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
        lowercase__:       int        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(
            result.reconstruction.shape    ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase__:       Tuple        =       1
        lowercase__:       Optional[int]        =       TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

        lowercase__:       int        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.reconstruction.shape    ,			(self.batch_size, 1, self.image_size, self.image_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.type_sequence_label_size
        lowercase__:       Optional[Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase__:       Optional[Any]        =       1
        lowercase__:       Union[str, Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)

        lowercase__:       List[str]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Dict        =       config_and_inputs
        lowercase__:       Optional[Any]        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 __lowercase			:							Union[str, Any]       					=			(
     {
         'feature-extraction': TFDeiTModel,
         'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 __lowercase			:							Union[str, Any]       					=			False
 __lowercase			:							Tuple       					=			False
 __lowercase			:							int       					=			False
 __lowercase			:							Optional[int]       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       TFDeiTModelTester(self			)
        lowercase__:       int        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='DeiT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       int        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       Tuple        =       model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
               lowercase__:       Union[str, Any]        =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Dense			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__:       Any        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       super()._prepare_for_class(lowerCAmelCase__    ,			lowerCAmelCase__    ,			return_labels=lowerCAmelCase__			)

        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			) ->    Tuple:




        '''simple docstring'''
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:       Union[str, Any]        =       TFDeiTModel.from_pretrained(lowerCAmelCase__			)
               self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    Tuple:
       lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''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'''
        lowercase__:       str        =       TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'			)

        lowercase__:       str        =       self.default_image_processor
        lowercase__:       Dict        =       prepare_img()
        lowercase__:       Optional[int]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       int        =       model(**lowerCAmelCase__			)

        # verify the logits
        lowercase__:       Tuple        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

        lowercase__:       List[Any]        =       tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

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


