





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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		_lowerCamelCase :    Union[str, Any]  							= '''pt'''
elif is_tf_available():
		_lowerCamelCase :    Any  							= '''tf'''
else:
		_lowerCamelCase :    Union[str, Any]  							= '''jax'''


class 						lowerCamelCase       (__lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ByTaTokenizer
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					str						)				->			Any:





														"""simple docstring"""
														super().setUp()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						ByTaTokenizer()
														tokenizer.save_pretrained(self.tmpdirname						)






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





														"""simple docstring"""
														return ByTaTokenizer.from_pretrained("google/byt5-small"						)






							def  A_     ( self     :					Optional[Any],			**_UpperCAmelCase     :					Union[str, Any]						)				->			ByTaTokenizer:





														"""simple docstring"""
														return self.tokenizer_class.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Union[str, Any]=2_0,			_UpperCAmelCase     :					int=5						)				->			Tuple[str, list]:





														"""simple docstring"""
														# XXX The default common tokenizer tests assume that every ID is decodable on its own.
														# This assumption is invalid for ByT5 because single bytes might not be
														# valid utf-8 (byte 128 for instance).
														# Here we're overriding the smallest possible method to provide
														# a clean sequence without making the same assumption.

														SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
														for i in range(len(_UpperCAmelCase						)						):
																					try:
																												SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.decode([i],			clean_up_tokenization_spaces=_UpperCAmelCase						)
																					except UnicodeDecodeError:
																												pass
																					toks.append((i, tok)						)

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

														# Ensure consistency
														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.decode(_UpperCAmelCase,			clean_up_tokenization_spaces=_UpperCAmelCase						)
														if " " not in output_txt and len(_UpperCAmelCase						) > 1:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(
																					    tokenizer.decode([toks_ids[0]],			clean_up_tokenization_spaces=_UpperCAmelCase						)
																					    + " "
																					    + tokenizer.decode(toks_ids[1:],			clean_up_tokenization_spaces=_UpperCAmelCase						)
																					)
														if with_prefix_space:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						" " + output_txt
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
														return output_txt, output_ids






							def  A_     ( self     :					Optional[Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer(["hi", "I went to the gym", ""]						)
														self.assertListEqual(batch_with_eos_added["input_ids"],			batch_without_eos_added["input_ids"]						)






							def  A_     ( self     :					List[str]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"Unicode €."
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
														self.assertEqual(encoded["input_ids"],			_UpperCAmelCase						)

														# decoding
														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.decode(_UpperCAmelCase						)
														self.assertEqual(_UpperCAmelCase,			"Unicode €.</s>"						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer("e è é ê ë"						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
														self.assertEqual(encoded["input_ids"],			_UpperCAmelCase						)
														# decoding
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.decode(_UpperCAmelCase						)
														self.assertEqual(_UpperCAmelCase,			"e è é ê ë</s>"						)

														# encode/decode, but with `encode` instead of `__call__`
														self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"						)						),			"e è é ê ë</s>"						)






							def  A_     ( self     :					Dict						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						["A long paragraph for summarization.", "Another paragraph for summarization."]
														# fmt: off
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
														# fmt: on
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			return_tensors=_UpperCAmelCase						)
														self.assertIsInstance(_UpperCAmelCase,			_UpperCAmelCase						)

														if FRAMEWORK != "jax":
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						list(batch.input_ids.numpy()[0]						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						list(batch.input_ids.tolist()[0]						)

														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														self.assertEqual((2, 3_7),			batch.input_ids.shape						)
														self.assertEqual((2, 3_7),			batch.attention_mask.shape						)






							def  A_     ( self     :					Any						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						["A long paragraph for summarization.", "Another paragraph for summarization."]
														SCREAMING_SNAKE_CASE__       :      int     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			return_tensors=_UpperCAmelCase						)
														# check if input_ids are returned and no decoder_input_ids
														self.assertIn("input_ids",			_UpperCAmelCase						)
														self.assertIn("attention_mask",			_UpperCAmelCase						)
														self.assertNotIn("decoder_input_ids",			_UpperCAmelCase						)
														self.assertNotIn("decoder_attention_mask",			_UpperCAmelCase						)






							def  A_     ( self     :					List[str]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      Dict     =						[
														    "Summary of the text.",
														    "Another summary.",
														]
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer(
														    text_target=_UpperCAmelCase,			max_length=3_2,			padding="max_length",			truncation=_UpperCAmelCase,			return_tensors=_UpperCAmelCase						)
														self.assertEqual(3_2,			targets["input_ids"].shape[1]						)






							def  A_     ( self     :					int						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.ta_base_tokenizer
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						["A long paragraph for summarization. </s>"]
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						["Summary of the text. </s>"]
														# fmt: off
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
														# fmt: on

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer(_UpperCAmelCase,			text_target=_UpperCAmelCase						)

														self.assertEqual(_UpperCAmelCase,			batch["input_ids"][0]						)
														self.assertEqual(_UpperCAmelCase,			batch["labels"][0]						)






							def  A_     ( self     :					Optional[int]						)				->			List[Any]:





														"""simple docstring"""
														# safety check on max_len default value so we are sure the test works
														SCREAMING_SNAKE_CASE__       :      Union[str, 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
														SCREAMING_SNAKE_CASE__       :      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
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tempfile.mkdtemp()

																												SCREAMING_SNAKE_CASE__       :      Dict     =						" He is very happy, UNwant\u00E9d,running"
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												tokenizer.save_pretrained(_UpperCAmelCase						)

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

																												shutil.rmtree(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						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
																												SCREAMING_SNAKE_CASE__       :      int     =						tempfile.mkdtemp()

																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						" He is very happy, UNwant\u00E9d,running"
																												tokenizer.add_tokens(["bim", "bambam"]						)
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.additional_special_tokens
																												additional_special_tokens.append("new_additional_special_token"						)
																												tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												tokenizer.save_pretrained(_UpperCAmelCase						)

																												SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.__class__.from_pretrained(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						after_tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)
																												self.assertIn("new_additional_special_token",			after_tokenizer.additional_special_tokens						)
																												self.assertEqual(after_tokenizer.model_max_length,			4_2						)

																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer.__class__.from_pretrained(_UpperCAmelCase,			model_max_length=4_3						)
																												self.assertEqual(tokenizer.model_max_length,			4_3						)

																												shutil.rmtree(_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[]
														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(_UpperCAmelCase						)

																												with open(os.path.join(_UpperCAmelCase,			"special_tokens_map.json"						),			encoding="utf-8"						) as json_file:
																																			SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						json.load(_UpperCAmelCase						)

																												with open(os.path.join(_UpperCAmelCase,			"tokenizer_config.json"						),			encoding="utf-8"						) as json_file:
																																			SCREAMING_SNAKE_CASE__       :      str     =						json.load(_UpperCAmelCase						)

																												SCREAMING_SNAKE_CASE__       :      List[str]     =						[F'''<extra_id_{i}>''' for i in range(1_2_5						)]

																												SCREAMING_SNAKE_CASE__       :      Any     =						added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]

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

																												# 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
																												SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer_class.from_pretrained(
																												    _UpperCAmelCase,			)
																												self.assertIn(
																												    "an_additional_special_token",			tokenizer_without_change_in_init.additional_special_tokens						)
																												# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																												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
																												SCREAMING_SNAKE_CASE__       :      Dict     =						added_tokens_extra_ids + [AddedToken("a_new_additional_special_token",			lstrip=_UpperCAmelCase						)]
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer_class.from_pretrained(
																												    _UpperCAmelCase,			additional_special_tokens=_UpperCAmelCase,			)

																												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  A_     ( self     :					Tuple						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      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(_UpperCAmelCase						)

																												SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer_class.from_pretrained(_UpperCAmelCase						)

																												self.assertTrue(tokenizer.decode([2_5_5]						) == ""						)






							def  A_     ( self     :					Optional[int]						)				->			Optional[Any]:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[Any]						)				->			List[Any]:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														pass






							def  A_     ( self     :					List[str]						)				->			Optional[int]:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
														# and special added tokens as tokens
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_tokenizers(fast=_UpperCAmelCase,			do_lower_case=_UpperCAmelCase						)
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer.convert_tokens_to_string(_UpperCAmelCase						)

																												self.assertIsInstance(_UpperCAmelCase,			_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
																												    "bos_token",
																												    "eos_token",
																												    "unk_token",
																												    "sep_token",
																												    "pad_token",
																												    "cls_token",
																												    "mask_token",
																												]

																												SCREAMING_SNAKE_CASE__       :      Dict     =						0
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer.convert_ids_to_tokens(
																												    _UpperCAmelCase,			skip_special_tokens=_UpperCAmelCase						)

																												for attr in attributes_list:
																																			setattr(_UpperCAmelCase,			attr + "_id",			_UpperCAmelCase						)
																																			self.assertEqual(getattr(_UpperCAmelCase,			_UpperCAmelCase						),			_UpperCAmelCase						)
																																			self.assertEqual(getattr(_UpperCAmelCase,			attr + "_id"						),			_UpperCAmelCase						)

																																			setattr(_UpperCAmelCase,			attr + "_id",			_UpperCAmelCase						)
																																			self.assertEqual(getattr(_UpperCAmelCase,			_UpperCAmelCase						),			_UpperCAmelCase						)
																																			self.assertEqual(getattr(_UpperCAmelCase,			attr + "_id"						),			_UpperCAmelCase						)

																												setattr(_UpperCAmelCase,			"additional_special_tokens_ids",			[]						)
																												self.assertListEqual(getattr(_UpperCAmelCase,			"additional_special_tokens"						),			[]						)
																												self.assertListEqual(getattr(_UpperCAmelCase,			"additional_special_tokens_ids"						),			[]						)

																												setattr(_UpperCAmelCase,			"additional_special_tokens_ids",			[token_id_to_test_setters]						)
																												self.assertListEqual(getattr(_UpperCAmelCase,			"additional_special_tokens"						),			[token_to_test_setters]						)
																												self.assertListEqual(getattr(_UpperCAmelCase,			"additional_special_tokens_ids"						),			[token_id_to_test_setters]						)













import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      str     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
														SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						scope

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






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

														SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

														return config, pixel_values, labels






							def  A_     ( self     :					int						)				->			Tuple:





														"""simple docstring"""
														return ViTMSNConfig(
														    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,			initializer_range=self.initializer_range,			)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase,			labels=_UpperCAmelCase						)
														print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"						)
														print("Labels: {labels}"						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)

														# test greyscale images
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
														return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							UpperCAmelCase_      						=   (
							    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model_class(_UpperCAmelCase						)
																					self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_UpperCAmelCase,			nn.Linear						)						)






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      int     =						model_class(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      int     =						inspect.signature(model.forward						)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					SCREAMING_SNAKE_CASE__       :      int     =						[*signature.parameters.keys()]

																					SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Optional[int]						)				->			List[Any]:





														"""simple docstring"""
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"						) if is_vision_available() else None






							@slow
							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														torch.manual_seed(2						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

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






