





'''simple docstring'''





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
							from test_module.custom_tokenization_fast import CustomTokenizerFast

class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								# A mock response for an HTTP head request to emulate server down
								lowerCamelCase	 =mock.Mock()
								lowerCamelCase	 =500
								lowerCamelCase	 ={}
								lowerCamelCase	 =HTTPError
								lowerCamelCase	 ={}

								# Download this model to make sure it's in the cache.
								lowerCamelCase	 =BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch("""requests.Session.request"""   , return_value=UpperCAmelCase_							) as mock_head:
												lowerCamelCase	 =BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
												# This check we did call the fake head request
												mock_head.assert_called()


				@require_tokenizers
				def _snake_case       (		self							):
								# A mock response for an HTTP head request to emulate server down
								lowerCamelCase	 =mock.Mock()
								lowerCamelCase	 =500
								lowerCamelCase	 ={}
								lowerCamelCase	 =HTTPError
								lowerCamelCase	 ={}

								# Download this model to make sure it's in the cache.
								lowerCamelCase	 =GPTaTokenizerFast.from_pretrained("""gpt2"""							)

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch("""requests.Session.request"""   , return_value=UpperCAmelCase_							) as mock_head:
												lowerCamelCase	 =GPTaTokenizerFast.from_pretrained("""gpt2"""							)
												# This check we did call the fake head request
												mock_head.assert_called()


				def _snake_case       (		self							):
								# This test is for deprecated behavior and can be removed in v5
								try:
												lowerCamelCase	 =tempfile.mktemp()
												with open(UpperCAmelCase_   , """wb"""							) as f:
																http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""   , UpperCAmelCase_							)

												lowerCamelCase	 =AlbertTokenizer.from_pretrained(UpperCAmelCase_							)
								finally:
												os.remove(UpperCAmelCase_							)

								# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
								# the current folder and have the right name.
								if os.path.isfile("""tokenizer.json"""							):
												# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
												return
								try:
												with open("""tokenizer.json"""   , """wb"""							) as f:
																http_get("""https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"""   , UpperCAmelCase_							)
												lowerCamelCase	 =AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
												# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
												self.assertEqual(tokenizer.vocab_size   , 1000							)
												# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

								finally:
												os.remove("""tokenizer.json"""							)


				def _snake_case       (		self							):
								# This test is for deprecated behavior and can be removed in v5
								lowerCamelCase	 =AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""							)




@is_staging_test
class       __A     (						unittest.TestCase    ):
				__A      =				["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =TOKEN
								HfFolder.save_token(UpperCAmelCase_							)


				@classmethod
				def _snake_case       (		cls							):
								try:
												delete_repo(token=cls._token   , repo_id="""test-tokenizer"""							)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token   , repo_id="""valid_org/test-tokenizer-org"""							)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token   , repo_id="""test-dynamic-tokenizer"""							)
								except HTTPError:
												pass


				def _snake_case       (		self							):
								with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCamelCase	 =os.path.join(UpperCAmelCase_   , """vocab.txt"""							)
												with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as vocab_writer:
																vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
												lowerCamelCase	 =BertTokenizer(UpperCAmelCase_							)

								tokenizer.push_to_hub("""test-tokenizer"""   , use_auth_token=self._token							)
								lowerCamelCase	 =BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer"""							)
								self.assertDictEqual(new_tokenizer.vocab   , tokenizer.vocab							)

								# Reset repo
								delete_repo(token=self._token   , repo_id="""test-tokenizer"""							)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(UpperCAmelCase_   , repo_id="""test-tokenizer"""   , push_to_hub=UpperCAmelCase_   , use_auth_token=self._token							)

								lowerCamelCase	 =BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer"""							)
								self.assertDictEqual(new_tokenizer.vocab   , tokenizer.vocab							)


				def _snake_case       (		self							):
								with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCamelCase	 =os.path.join(UpperCAmelCase_   , """vocab.txt"""							)
												with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as vocab_writer:
																vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
												lowerCamelCase	 =BertTokenizer(UpperCAmelCase_							)

								tokenizer.push_to_hub("""valid_org/test-tokenizer-org"""   , use_auth_token=self._token							)
								lowerCamelCase	 =BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""							)
								self.assertDictEqual(new_tokenizer.vocab   , tokenizer.vocab							)

								# Reset repo
								delete_repo(token=self._token   , repo_id="""valid_org/test-tokenizer-org"""							)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(
												    UpperCAmelCase_   , repo_id="""valid_org/test-tokenizer-org"""   , push_to_hub=UpperCAmelCase_   , use_auth_token=self._token							)

								lowerCamelCase	 =BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""							)
								self.assertDictEqual(new_tokenizer.vocab   , tokenizer.vocab							)


				@require_tokenizers
				def _snake_case       (		self							):
								CustomTokenizer.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCamelCase	 =os.path.join(UpperCAmelCase_   , """vocab.txt"""							)
												with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as vocab_writer:
																vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
												lowerCamelCase	 =CustomTokenizer(UpperCAmelCase_							)

								# No fast custom tokenizer
								tokenizer.push_to_hub("""test-dynamic-tokenizer"""   , use_auth_token=self._token							)

								lowerCamelCase	 =AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer"""   , trust_remote_code=UpperCAmelCase_							)
								# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__   , """CustomTokenizer"""							)

								# Fast and slow custom tokenizer
								CustomTokenizerFast.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCamelCase	 =os.path.join(UpperCAmelCase_   , """vocab.txt"""							)
												with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as vocab_writer:
																vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)

												lowerCamelCase	 =BertTokenizerFast.from_pretrained(UpperCAmelCase_							)
												bert_tokenizer.save_pretrained(UpperCAmelCase_							)
												lowerCamelCase	 =CustomTokenizerFast.from_pretrained(UpperCAmelCase_							)

								tokenizer.push_to_hub("""test-dynamic-tokenizer"""   , use_auth_token=self._token							)

								lowerCamelCase	 =AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer"""   , trust_remote_code=UpperCAmelCase_							)
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__   , """CustomTokenizerFast"""							)
								lowerCamelCase	 =AutoTokenizer.from_pretrained(
								    f"""{USER}/test-dynamic-tokenizer"""   , use_fast=UpperCAmelCase_   , trust_remote_code=UpperCAmelCase_							)
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__   , """CustomTokenizer"""							)




class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""Hello 友達"""							)
								self.assertEqual(trie.data   , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {""" """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}							)
								trie.add("""Hello"""							)
								trie.data
								self.assertEqual(trie.data   , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {"""""": 1, """ """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""							)   , ["""[CLS] This is a extra_id_100"""]							)
								trie.add("""[CLS]"""							)
								trie.add("""extra_id_1"""							)
								trie.add("""extra_id_100"""							)
								self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""							)   , ["""[CLS]""", """ This is a """, """extra_id_100"""]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""A"""							)
								self.assertEqual(trie.split("""ABC"""							)   , ["""A""", """BC"""]							)
								self.assertEqual(trie.split("""BCA"""							)   , ["""BC""", """A"""]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""TOKEN]"""							)
								trie.add("""[SPECIAL_TOKEN]"""							)
								self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""							)   , ["""This is something """, """[SPECIAL_TOKEN]"""]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""A"""							)
								trie.add("""P"""							)
								trie.add("""[SPECIAL_TOKEN]"""							)
								self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""							)   , ["""This is something """, """[SPECIAL_TOKEN]"""]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""AB"""							)
								trie.add("""B"""							)
								trie.add("""C"""							)
								self.assertEqual(trie.split("""ABC"""							)   , ["""AB""", """C"""]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =Trie()
								trie.add("""ABC"""							)
								trie.add("""B"""							)
								trie.add("""CD"""							)
								self.assertEqual(trie.split("""ABCD"""							)   , ["""ABC""", """D"""]							)


				def _snake_case       (		self							):
								# Even if the offsets are wrong, we necessarily output correct string
								# parts.
								lowerCamelCase	 =Trie()
								lowerCamelCase	 =trie.cut_text("""ABC"""   , [0, 0, 2, 1, 2, 3]							)
								self.assertEqual(UpperCAmelCase_   , ["""AB""", """C"""]							)












import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    FocalNetBackbone,
							    FocalNetForImageClassification,
							    FocalNetForMaskedImageModeling,
							    FocalNetModel,
							)
							from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=32   , UpperCAmelCase_=2   , UpperCAmelCase_=3   , UpperCAmelCase_=16   , UpperCAmelCase_=[32, 64, 128]   , UpperCAmelCase_=[1, 2, 1]   , UpperCAmelCase_=[2, 2, 4]   , UpperCAmelCase_=2   , UpperCAmelCase_=2.0   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=False   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , UpperCAmelCase_=True   , UpperCAmelCase_=10   , UpperCAmelCase_=8   , UpperCAmelCase_=["stage1", "stage2"]   , UpperCAmelCase_=[1, 2]   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =embed_dim
								lowerCamelCase	 =hidden_sizes
								lowerCamelCase	 =depths
								lowerCamelCase	 =num_heads
								lowerCamelCase	 =window_size
								lowerCamelCase	 =mlp_ratio
								lowerCamelCase	 =qkv_bias
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =drop_path_rate
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =use_absolute_embeddings
								lowerCamelCase	 =patch_norm
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =is_training
								lowerCamelCase	 =scope
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =encoder_stride
								lowerCamelCase	 =out_features
								lowerCamelCase	 =out_indices


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								return FocalNetConfig(
								    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , embed_dim=self.embed_dim   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , num_heads=self.num_heads   , window_size=self.window_size   , mlp_ratio=self.mlp_ratio   , qkv_bias=self.qkv_bias   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , drop_path_rate=self.drop_path_rate   , hidden_act=self.hidden_act   , use_absolute_embeddings=self.use_absolute_embeddings   , path_norm=self.patch_norm   , layer_norm_eps=self.layer_norm_eps   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , out_features=self.out_features   , out_indices=self.out_indices   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								lowerCamelCase	 =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
								lowerCamelCase	 =int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, expected_seq_len, expected_dim)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							)   , len(config.out_features							)							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							)   , [self.batch_size, self.image_size, 8, 8]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							)   , len(config.out_features							)							)
								self.parent.assertListEqual(model.channels   , config.hidden_sizes[:-1]							)

								# verify backbone works with out_features=None
								lowerCamelCase	 =None
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							)   , 1							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							)   , [self.batch_size, self.image_size * 2, 4, 4]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							)   , 1							)
								self.parent.assertListEqual(model.channels   , [config.hidden_sizes[-1]]							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetForMaskedImageModeling(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(
								    result.reconstruction.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForMaskedImageModeling(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.reconstruction.shape   , (self.batch_size, 1, self.image_size, self.image_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =self.type_sequence_label_size
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(
				    (
				        FocalNetModel,
				        FocalNetForImageClassification,
				        FocalNetForMaskedImageModeling,
				        FocalNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				__A      =				(
				    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
				    if is_torch_available()
				    else {}
				)
				__A      =				False

				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , embed_dim=37   , has_text_modality=UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


				def _snake_case       (		self							):
								return


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_							)


				@unittest.skip(reason="""FocalNet does not use inputs_embeds"""							)
				def _snake_case       (		self							):
								pass


				@unittest.skip(reason="""FocalNet does not use feedforward chunking"""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)							)
												lowerCamelCase	 =model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCAmelCase_   , nn.Linear							)							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												lowerCamelCase	 =inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowerCamelCase	 =[*signature.parameters.keys()]

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =model_class(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								with torch.no_grad():
												lowerCamelCase	 =model(**self._prepare_for_class(UpperCAmelCase_   , UpperCAmelCase_							)							)

								lowerCamelCase	 =outputs.hidden_states

								lowerCamelCase	 =getattr(
								    self.model_tester   , """expected_num_hidden_layers"""   , len(self.model_tester.depths							) + 1							)
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								# FocalNet has a different seq_length
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )

								lowerCamelCase	 =outputs.reshaped_hidden_states
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =reshaped_hidden_states[0].shape
								lowerCamelCase	 =(
								    reshaped_hidden_states[0].view(UpperCAmelCase_   , UpperCAmelCase_   , height * width							).permute(0   , 2   , 1							)
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCamelCase	 =True

												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase	 =3

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCamelCase	 =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)


				@slow
				def _snake_case       (		self							):
								for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase	 =FocalNetModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =_config_zero_init(UpperCAmelCase_							)
								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(config=UpperCAmelCase_							)
												for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																				self.assertIn(
																				    ((param.data.mean() * 1E9).round() / 1E9).item()   , [0.0, 1.0]   , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   , )




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


				@cached_property
				def _snake_case       (		self							):
								# TODO update organization
								return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""							) if is_vision_available() else None


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""							).to(UpperCAmelCase_							)
								lowerCamelCase	 =self.default_image_processor

								lowerCamelCase	 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								lowerCamelCase	 =image_processor(images=UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

								# verify the logits
								lowerCamelCase	 =torch.Size((1, 1000)							)
								self.assertEqual(outputs.logits.shape   , UpperCAmelCase_							)
								lowerCamelCase	 =torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]							).to(UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]   , UpperCAmelCase_   , atol=1E-4							)							)
								self.assertTrue(outputs.logits.argmax(dim=-1							).item()   , 281							)




@require_torch
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				(FocalNetBackbone,) if is_torch_available() else ()
				__A      =				FocalNetConfig

				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)





