


'''simple docstring'''



import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


A__						:   Optional[int]				=    logging.get_logger(__name__)

A__						:   int				=    {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

A__						:   int				=    {
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

A__						:   List[Any]				=    {
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Tuple				)	->						int:
     with open(UpperCAmelCase_ ,       'r' ,       encoding='utf-8'				) as f:
          __lowerCamelCase  :      Optional[int]														=						json.loads(f.read()				)

     __lowerCamelCase  :      Union[str, Any]														=						collections.OrderedDict()
     __lowerCamelCase  :      Union[str, Any]														=						collections.OrderedDict()
     __lowerCamelCase  :      Optional[Any]														=						collections.OrderedDict()
     with open(UpperCAmelCase_ ,       'r' ,       encoding='utf-8'				) as f:
          __lowerCamelCase  :      Any														=						f.readlines()
     __lowerCamelCase  :      Tuple														=						[[t.rstrip('\n'				)] if (t == ',' or ',' not in t) else t.rstrip('\n'				).split(','				) for t in token]
     for idx, b in enumerate(UpperCAmelCase_				):
          __lowerCamelCase  :      int														=						b
          __lowerCamelCase  :      Any														=						idx
          for wd in b:
               __lowerCamelCase  :      Union[str, Any]														=						idx

     return vocab, raw_vocab, ids_to_tokens, emoji





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[str]					=     VOCAB_FILES_NAMES
    lowerCamelCase    :   Tuple					=     PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase    :   Union[str, Any]					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase    :   Union[str, Any]					=     ['input_ids', 'attention_mask']







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_="<|endoftext|>"     ,	SCREAMING_SNAKE_CASE_="<|endoftext|>"     ,	SCREAMING_SNAKE_CASE_="<|startoftext|>"     ,	SCREAMING_SNAKE_CASE_="<|endoftext|>"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Tuple:
         super().__init__(
             unk_token=SCREAMING_SNAKE_CASE_     ,	pad_token=SCREAMING_SNAKE_CASE_     ,	bos_token=SCREAMING_SNAKE_CASE_     ,	eos_token=SCREAMING_SNAKE_CASE_     ,	do_clean_text=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)
         if not os.path.isfile(SCREAMING_SNAKE_CASE_      ):
              raise ValueError(
                  f'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
                  ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'      )
         if not os.path.isfile(SCREAMING_SNAKE_CASE_      ):
              raise ValueError(
                  f'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
                  ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'      )
         __lowerCamelCase  :      int														=						do_clean_text
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						load_vocab_and_emoji(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						SubWordJapaneseTokenizer(
             vocab=self.vocab     ,	ids_to_tokens=self.ids_to_tokens     ,	emoji=self.emoji      )







    @property
    def  lowercase_							(				self      )							->	List[str]:
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab      )







    def  lowercase_							(				self      )							->	Any:
         return dict(self.raw_vocab     ,	**self.added_tokens_encoder      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         return self.subword_tokenizer.tokenize(SCREAMING_SNAKE_CASE_     ,	clean=self.do_clean_text      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         return self.vocab.get(SCREAMING_SNAKE_CASE_     ,	self.vocab.get(self.unk_token      )      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         return self.subword_tokenizer.convert_id_to_token(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Any														=						''.join(SCREAMING_SNAKE_CASE_      ).strip()
         return out_string







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[int]:
         __lowerCamelCase  :      int														=						[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_     ,	add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

         if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
              __lowerCamelCase  :      List[Any]														=						input_ids[-self.model_max_length :]
         return input_ids







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	Tuple[str]:
         __lowerCamelCase  :      List[Any]														=						0
         if os.path.isdir(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Tuple														=						os.path.join(
                  SCREAMING_SNAKE_CASE_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
              __lowerCamelCase  :      Union[str, Any]														=						os.path.join(
                  SCREAMING_SNAKE_CASE_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']      )
         else:
              __lowerCamelCase  :      List[Any]														=						(
                  (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
              )
              __lowerCamelCase  :      Any														=						(
                  (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
              )
         with open(SCREAMING_SNAKE_CASE_     ,	'w'     ,	encoding='utf-8'      ) as writer:
              for token_index, token in self.ids_to_tokens.items():
                   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!'      )
                        __lowerCamelCase  :      Dict														=						token_index
                   writer.write(','.join(SCREAMING_SNAKE_CASE_      ) + '\n'      )
                   index += 1
         with open(SCREAMING_SNAKE_CASE_     ,	'w'     ,	encoding='utf-8'      ) as writer:
              json.dump(self.emoji     ,	SCREAMING_SNAKE_CASE_      )
         return vocab_file, emoji_file







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      Optional[int]														=						vocab  # same as swe
         __lowerCamelCase  :      Dict														=						ids_to_tokens  # same as bpe
         __lowerCamelCase  :      str														=						emoji
         __lowerCamelCase  :      str														=						np.max([len(SCREAMING_SNAKE_CASE_      ) for w in self.vocab.keys()]      )
         __lowerCamelCase  :      Union[str, Any]														=						re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'      )
         __lowerCamelCase  :      Optional[Any]														=						re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'      )
         __lowerCamelCase  :      List[Any]														=						re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'      )
         __lowerCamelCase  :      Dict														=						re.compile(
             r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'      )
         __lowerCamelCase  :      int														=						re.compile(
             r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'      )
         __lowerCamelCase  :      List[str]														=						re.compile(
             r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'      )
         __lowerCamelCase  :      Optional[int]														=						'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
         __lowerCamelCase  :      Any														=						'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
         __lowerCamelCase  :      Optional[Any]														=						str.maketrans({k: '<BLOCK>' for k in keisen + blocks}      )







    def __len__(				self      )							->	Any:
         return len(self.ids_to_tokens      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         __lowerCamelCase  :      Any														=						self.content_repattera.sub('<URL>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Any														=						self.content_repattera.sub('<EMAIL>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						self.content_repattera.sub('<TEL>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						self.content_repattera.sub('<DATE>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						self.content_repattera.sub('<DATE>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						self.content_repattera.sub('<PRICE>'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Any														=						content.translate(self.content_transa      )
         while "<BLOCK><BLOCK>" in content:
              __lowerCamelCase  :      Optional[int]														=						content.replace('<BLOCK><BLOCK>'     ,	'<BLOCK>'      )
         return content







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						text.replace(' '     ,	'<SP>'      )
         __lowerCamelCase  :      Optional[Any]														=						text.replace('　'     ,	'<SP>'      )
         __lowerCamelCase  :      str														=						text.replace('\r\n'     ,	'<BR>'      )
         __lowerCamelCase  :      List[Any]														=						text.replace('\n'     ,	'<BR>'      )
         __lowerCamelCase  :      Tuple														=						text.replace('\r'     ,	'<BR>'      )
         __lowerCamelCase  :      int														=						text.replace('\t'     ,	'<TAB>'      )
         __lowerCamelCase  :      Optional[int]														=						text.replace('—'     ,	'ー'      )
         __lowerCamelCase  :      List[Any]														=						text.replace('−'     ,	'ー'      )
         for k, v in self.emoji["emoji"].items():
              if k in text:
                   __lowerCamelCase  :      Tuple														=						text.replace(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         if clean:
              __lowerCamelCase  :      Union[str, Any]														=						self.clean_text(SCREAMING_SNAKE_CASE_      )

         def check_simbol(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      List[Any]														=						x.encode()
              if len(SCREAMING_SNAKE_CASE_      ) == 1 and len(SCREAMING_SNAKE_CASE_      ) == 2:
                   __lowerCamelCase  :      Dict														=						(int(e[0]      ) << 8) + int(e[1]      )
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      str														=						x.encode()
              if len(SCREAMING_SNAKE_CASE_      ) == 1 and len(SCREAMING_SNAKE_CASE_      ) == 3:
                   __lowerCamelCase  :      List[Any]														=						(int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        return True
              return False

         __lowerCamelCase  :      Any														=						0
         __lowerCamelCase  :      Union[str, Any]														=						[]
         while pos < len(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Dict														=						min(len(SCREAMING_SNAKE_CASE_      )     ,	pos + self.maxlen + 1      ) if text[pos] == '<' else pos + 3
              __lowerCamelCase  :      Dict														=						[]  # (token_id, token, pos)
              for e in range(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	-1      ):
                   __lowerCamelCase  :      List[str]														=						text[pos:e]
                   if wd in self.vocab:
                        if wd[0] == "<" and len(SCREAMING_SNAKE_CASE_      ) > 2:
                             __lowerCamelCase  :      str														=						[(self.vocab[wd], wd, e)]
                             break
                        else:
                             candidates.append((self.vocab[wd], wd, e)      )
              if len(SCREAMING_SNAKE_CASE_      ) > 0:
                   # the smallest token_id is adopted
                   __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						sorted(SCREAMING_SNAKE_CASE_     ,	key=lambda SCREAMING_SNAKE_CASE_      : x[0]      )[0]
                   result.append(SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      int														=						e
              else:
                   __lowerCamelCase  :      List[Any]														=						pos + 1
                   __lowerCamelCase  :      int														=						text[pos:end]
                   if check_simbol(SCREAMING_SNAKE_CASE_      ):
                        result.append('<KIGOU>'      )
                   elif checkuae(SCREAMING_SNAKE_CASE_      ):
                        result.append('<U2000U2BFF>'      )
                   else:
                        for i in wd.encode('utf-8'      ):
                             result.append('<|byte%d|>' % i      )
                   __lowerCamelCase  :      str														=						end
         return result







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_="\n"      )							->	Union[str, Any]:
         __lowerCamelCase  :      Dict														=						[]
         __lowerCamelCase  :      int														=						[]
         __lowerCamelCase  :      Optional[Any]														=						self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
              byte_tokens.append(int(word[6:-2]      )      )
         else:
              if len(SCREAMING_SNAKE_CASE_      ) > 0:
                   words.append(bytearray(SCREAMING_SNAKE_CASE_      ).decode('utf-8'     ,	errors='replace'      )      )
                   __lowerCamelCase  :      Union[str, Any]														=						[]
              if word[:7] == "<|emoji" and word[-2:] == "|>":
                   words.append(self.emoji['emoji_inv'][word]      )
              elif word == "<SP>":
                   words.append(' '      )
              elif word == "<BR>":
                   words.append(SCREAMING_SNAKE_CASE_      )
              elif word == "<TAB>":
                   words.append('\t'      )
              elif word == "<BLOCK>":
                   words.append('▀'      )
              elif word == "<KIGOU>":
                   words.append('ǀ'      )
              elif word == "<U2000U2BFF>":
                   words.append('‖'      )
              else:
                   words.append(SCREAMING_SNAKE_CASE_      )
         if len(SCREAMING_SNAKE_CASE_      ) > 0:
              words.append(bytearray(SCREAMING_SNAKE_CASE_      ).decode('utf-8'     ,	errors='replace'      )      )
         __lowerCamelCase  :      Dict														=						''.join(SCREAMING_SNAKE_CASE_      )
         return text










'''simple docstring'''



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      Dict														=						batch_size
         __lowerCamelCase  :      int														=						image_size
         __lowerCamelCase  :      List[str]														=						patch_size
         __lowerCamelCase  :      Optional[int]														=						num_channels
         __lowerCamelCase  :      Any														=						is_training
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      Optional[Any]														=						num_attention_heads
         __lowerCamelCase  :      Dict														=						intermediate_size
         __lowerCamelCase  :      Union[str, Any]														=						hidden_act
         __lowerCamelCase  :      Optional[int]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      str														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(image_size // patch_size) ** 2
         __lowerCamelCase  :      Optional[int]														=						num_patches + 1







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      int														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __lowerCamelCase  :      Optional[int]														=						ViTConfig(
             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     ,	)

         return config, pixel_values







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						FlaxViTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(self.image_size, self.image_size)
         __lowerCamelCase  :      str														=						(self.patch_size, self.patch_size)
         __lowerCamelCase  :      Any														=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, num_patches + 1, self.hidden_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Tuple														=						self.type_sequence_label_size
         __lowerCamelCase  :      Any														=						FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      int														=						config_and_inputs
         __lowerCamelCase  :      Union[str, Any]														=						{'pixel_values': pixel_values}
         return config, inputs_dict







@require_flax
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   str					=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







    def  lowercase_							(				self      )							->	None:
         __lowerCamelCase  :      str														=						FlaxViTModelTester(self      )
         __lowerCamelCase  :      Union[str, Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[Any]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase  :      Optional[Any]														=						model_class(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						inspect.signature(model.__call__      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowerCamelCase  :      List[str]														=						[*signature.parameters.keys()]

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__      ):
                   __lowerCamelCase  :      List[Any]														=						self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

                   @jax.jit
                   def model_jitted(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      ):
                        return model(pixel_values=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

                   with self.subTest('JIT Enabled'      ):
                        __lowerCamelCase  :      Optional[int]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   with self.subTest('JIT Disabled'      ):
                        with jax.disable_jit():
                             __lowerCamelCase  :      Union[str, Any]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	len(SCREAMING_SNAKE_CASE_      )      )
                   for jitted_output, output in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                        self.assertEqual(jitted_output.shape     ,	output.shape      )







    @slow
    def  lowercase_							(				self      )							->	List[Any]:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase  :      Union[str, Any]														=						model_class_name.from_pretrained('google/vit-base-patch16-224'      )
              __lowerCamelCase  :      Union[str, Any]														=						model(np.ones((1, 3, 2_24, 2_24)      )      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )






