


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									=       logging.get_logger(__name__)

_A									=       {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

_A									=       {
    '''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									=       {
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}



def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Tuple )       ->							int:
   with open(snake_case						,      """r"""						,      encoding="""utf-8""" ) as f:
      __lowerCamelCase:				str     =	json.loads(f.read() )

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

   return vocab, raw_vocab, ids_to_tokens, emoji



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  int						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  Optional[int]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  Optional[int]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]="<|endoftext|>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<|endoftext|>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<|startoftext|>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<|endoftext|>"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=False    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
        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:				List[Any]     =	do_clean_text
        __lowerCamelCase:				int     =	load_vocab_and_emoji(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	SubWordJapaneseTokenizer(
            vocab=self.vocab    ,	ids_to_tokens=self.ids_to_tokens    ,	emoji=self.emoji							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return dict(self.raw_vocab    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        return self.subword_tokenizer.tokenize(SCREAMING_SNAKE_CASE_    ,	clean=self.do_clean_text							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        return self.vocab.get(SCREAMING_SNAKE_CASE_    ,	self.vocab.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.subword_tokenizer.convert_id_to_token(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        __lowerCamelCase:				str     =	"""""".join(SCREAMING_SNAKE_CASE_							).strip()
        return out_string





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				"Conversation"							):
        __lowerCamelCase:				Any     =	[]
        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:				Optional[Any]     =	input_ids[-self.model_max_length :]
        return input_ids







     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        __lowerCamelCase:				Optional[int]     =	0
        if os.path.isdir(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				List[Any]     =	os.path.join(
               SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
           __lowerCamelCase:				int     =	os.path.join(
               SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]							)
        else:
           __lowerCamelCase:				Dict     =	(
               (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
           )
           __lowerCamelCase:				Union[str, 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:				Optional[int]     =	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        a					(			_UpperCAmelCase   ):





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





     def __len__(    self					:				Dict							):
        return len(self.ids_to_tokens							)





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=False							):
        __lowerCamelCase:				int     =	text.replace(""" """    ,	"""<SP>"""							)
        __lowerCamelCase:				List[str]     =	text.replace("""　"""    ,	"""<SP>"""							)
        __lowerCamelCase:				Dict     =	text.replace("""\r\n"""    ,	"""<BR>"""							)
        __lowerCamelCase:				Tuple     =	text.replace("""\n"""    ,	"""<BR>"""							)
        __lowerCamelCase:				Optional[int]     =	text.replace("""\r"""    ,	"""<BR>"""							)
        __lowerCamelCase:				str     =	text.replace("""\t"""    ,	"""<TAB>"""							)
        __lowerCamelCase:				int     =	text.replace("""—"""    ,	"""ー"""							)
        __lowerCamelCase:				Any     =	text.replace("""−"""    ,	"""ー"""							)
        for k, v in self.emoji["emoji"].items():
           if k in text:
              __lowerCamelCase:				str     =	text.replace(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        if clean:
           __lowerCamelCase:				List[Any]     =	self.clean_text(SCREAMING_SNAKE_CASE_							)

        def check_simbol(SCREAMING_SNAKE_CASE_					:				str							):
           __lowerCamelCase:				Any     =	x.encode()
           if len(SCREAMING_SNAKE_CASE_							) == 1 and len(SCREAMING_SNAKE_CASE_							) == 2:
              __lowerCamelCase:				int     =	(int(e[0]							) << 8) + int(e[1]							)
              if (
                  (c >= 0xC_2_A_1 and c <= 0xC_2_B_F)
                  or (c >= 0xC_7_8_0 and c <= 0xC_7_8_3)
                  or (c >= 0xC_A_B_9 and c <= 0xC_B_B_F)
                  or (c >= 0xC_C_8_0 and c <= 0xC_D_A_2)
              ):
                 return True
           return False

        def checkuae(SCREAMING_SNAKE_CASE_					:				Tuple							):
           __lowerCamelCase:				int     =	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 >= 0xE_2_8_0_8_0 and c <= 0xE_2_B_0_7_F:
                 return True
           return False

        __lowerCamelCase:				Any     =	0
        __lowerCamelCase:				Union[str, Any]     =	[]
        while pos < len(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				Optional[Any]     =	min(len(SCREAMING_SNAKE_CASE_							)    ,	pos + self.maxlen + 1							) if text[pos] == """<""" else pos + 3
           __lowerCamelCase:				Union[str, Any]     =	[]  # (token_id, token, pos)
           for e in range(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	-1							):
              __lowerCamelCase:				int     =	text[pos:e]
              if wd in self.vocab:
                 if wd[0] == "<" and len(SCREAMING_SNAKE_CASE_							) > 2:
                    __lowerCamelCase:				Any     =	[(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:				Optional[Any]     =	sorted(SCREAMING_SNAKE_CASE_    ,	key=lambda SCREAMING_SNAKE_CASE_							: x[0]							)[0]
              result.append(SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Tuple     =	e
           else:
              __lowerCamelCase:				Dict     =	pos + 1
              __lowerCamelCase:				Dict     =	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:				List[str]     =	end
        return result







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="\n"							):
        __lowerCamelCase:				int     =	[]
        __lowerCamelCase:				Optional[Any]     =	[]
        __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:				Optional[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:				List[str]     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        return text






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor



class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple=13    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=3    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=224    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1000    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=[3, 3, 6, 4]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=[48, 56, 112, 220]    ,	):
        __lowerCamelCase:				Optional[Any]     =	parent
        __lowerCamelCase:				Any     =	batch_size
        __lowerCamelCase:				str     =	num_channels
        __lowerCamelCase:				str     =	is_training
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				List[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				int     =	attention_probs_dropout_prob
        __lowerCamelCase:				Union[str, Any]     =	num_labels
        __lowerCamelCase:				int     =	image_size
        __lowerCamelCase:				Optional[Any]     =	layer_depths
        __lowerCamelCase:				Dict     =	embed_dims





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        __lowerCamelCase:				Any     =	None
        if self.use_labels:
           __lowerCamelCase:				Tuple     =	ids_tensor([self.batch_size]    ,	self.num_labels							)

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        return SwiftFormerConfig(
            depths=self.layer_depths    ,	embed_dims=self.embed_dims    ,	mlp_ratio=4    ,	downsamples=[True, True, True, True]    ,	hidden_act="""gelu"""    ,	num_labels=self.num_labels    ,	down_patch_size=3    ,	down_stride=2    ,	down_pad=1    ,	drop_rate=0.0    ,	drop_path_rate=0.0    ,	use_layer_scale=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.num_labels
        __lowerCamelCase:				Union[str, Any]     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Tuple     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __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:				Optional[Any]     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,	nn.Linear							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





     @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				Any     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)  # TODO

           # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
           # with the width and height being successively divided by 2, after every 2 blocks
           for i in range(len(SCREAMING_SNAKE_CASE_							)							):
              self.assertEqual(
                  hidden_states[i].shape    ,	torch.Size(
                      [
                          self.model_tester.batch_size,
                          self.model_tester.embed_dims[i // 2],
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                      ]							)    ,	)

        __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				Optional[int]     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_SNAKE_CASE_							)
           for key in configs_no_init.__dict__.keys():
              if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_SNAKE_CASE_							)
           for name, param in model.named_parameters():
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9) / 1E9).round().item()    ,	[0.0, 1.0]    ,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,	)







     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]							).to(SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)


