






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
  from tokenizers import Tokenizer
  from tokenizers.models import WordPiece


class 					__lowercase					(		unittest.TestCase     ):







     '''simple docstring'''




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	Optional[Any] ):


         """simple docstring"""



         __A   			=		list(tokenizer_class.max_model_input_sizes.keys() )
         for model_name in sa_models[:1]:
             __A   			=		tokenizer_class.from_pretrained(__UpperCAmelCase )
             self.assertIsNotNone(__UpperCAmelCase )
             self.assertIsInstance(__UpperCAmelCase       ,		__UpperCAmelCase )
             self.assertIsInstance(__UpperCAmelCase       ,		__UpperCAmelCase )

             for special_tok in tokenizer.all_special_tokens:
                 self.assertIsInstance(__UpperCAmelCase       ,		__UpperCAmelCase )
                 __A   			=		tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
                 self.assertIsInstance(__UpperCAmelCase       ,		__UpperCAmelCase )




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	BatchEncoding       ,		UpperCamelCase_				:	Optional[Callable] = None ):


         """simple docstring"""



         __A   			=		pickle.dumps(__UpperCAmelCase )
         self.assertIsNotNone(__UpperCAmelCase )

         __A   			=		pickle.loads(__UpperCAmelCase )

         # Ensure is_fast is correctly restored
         self.assertEqual(be_restored.is_fast       ,		be_original.is_fast )

         # Ensure encodings are potentially correctly restored
         if be_original.is_fast:
             self.assertIsNotNone(be_restored.encodings )
         else:
             self.assertIsNone(be_restored.encodings )

         # Ensure the keys are the same
         for original_v, restored_v in zip(be_original.values()       ,		be_restored.values() ):
             if equal_op:
                 self.assertTrue(equal_op(__UpperCAmelCase       ,		__UpperCAmelCase ) )
             else:
                 self.assertEqual(__UpperCAmelCase       ,		__UpperCAmelCase )




     @slow
     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         self.check_tokenizer_from_pretrained(__UpperCAmelCase )




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """simple docstring"""



         self.assertEqual(TensorType("""tf""" )       ,		TensorType.TENSORFLOW )
         self.assertEqual(TensorType("""pt""" )       ,		TensorType.PYTORCH )
         self.assertEqual(TensorType("""np""" )       ,		TensorType.NUMPY )




     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	List[str] ):


         """simple docstring"""



         import numpy as np

         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )
         __A   			=		BertTokenizerFast.from_pretrained("""bert-base-cased""" )

         # Python no tensor
         with self.subTest("""BatchEncoding (Python, return_tensors=None)""" ):
             self.assert_dump_and_restore(tokenizer_p("""Small example to encode""" ) )

         with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)""" ):
             self.assert_dump_and_restore(
                 tokenizer_p("""Small example to encode"""       ,		return_tensors=TensorType.NUMPY )       ,		np.array_equal )

         with self.subTest("""BatchEncoding (Rust, return_tensors=None)""" ):
             self.assert_dump_and_restore(tokenizer_r("""Small example to encode""" ) )

         with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)""" ):
             self.assert_dump_and_restore(
                 tokenizer_r("""Small example to encode"""       ,		return_tensors=TensorType.NUMPY )       ,		np.array_equal )




     @require_tf
     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	List[str] ):


         """simple docstring"""



         import tensorflow as tf

         def tf_array_equals(UpperCamelCase_				:	Any       ,		UpperCamelCase_				:	List[Any] ):
             return tf.reduce_all(tf.equal(__UpperCAmelCase       ,		__UpperCAmelCase ) )

         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )
         __A   			=		BertTokenizerFast.from_pretrained("""bert-base-cased""" )

         with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)""" ):
             self.assert_dump_and_restore(
                 tokenizer_p("""Small example to encode"""       ,		return_tensors=TensorType.TENSORFLOW )       ,		__UpperCAmelCase )

         with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)""" ):
             self.assert_dump_and_restore(
                 tokenizer_r("""Small example to encode"""       ,		return_tensors=TensorType.TENSORFLOW )       ,		__UpperCAmelCase )




     @require_torch
     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



         import torch

         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )
         __A   			=		BertTokenizerFast.from_pretrained("""bert-base-cased""" )

         with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)""" ):
             self.assert_dump_and_restore(
                 tokenizer_p("""Small example to encode"""       ,		return_tensors=TensorType.PYTORCH )       ,		torch.equal )

         with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)""" ):
             self.assert_dump_and_restore(
                 tokenizer_r("""Small example to encode"""       ,		return_tensors=TensorType.PYTORCH )       ,		torch.equal )




     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )
         __A   			=		BertTokenizerFast.from_pretrained("""bert-base-cased""" )

         with self.subTest("""Python Tokenizer""" ):
             self.assertFalse(tokenizer_p("""Small example to_encode""" ).is_fast )

         with self.subTest("""Rust Tokenizer""" ):
             self.assertTrue(tokenizer_r("""Small example to_encode""" ).is_fast )




     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	Dict ):


         """simple docstring"""



         __A   			=		BertTokenizerFast.from_pretrained("""bert-base-cased""" )
         __A   			=		tokenizer_r(["""Test""", """\xad""", """test"""]       ,		is_split_into_words=__UpperCAmelCase )

         self.assertEqual(encoded.word_to_tokens(0 )       ,		TokenSpan(start=1       ,		end=2 ) )
         self.assertEqual(encoded.word_to_tokens(1 )       ,		__UpperCAmelCase )
         self.assertEqual(encoded.word_to_tokens(2 )       ,		TokenSpan(start=2       ,		end=3 ) )




     def 						lowerCAmelCase_   ( self				:	Dict ):


         """simple docstring"""



         __A   			=		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]} )
         __A   			=		batch.convert_to_tensors(tensor_type="""np""" )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(2, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(2,) )
         # test converting the converted
         with CaptureStderr() as cs:
             __A   			=		batch.convert_to_tensors(tensor_type="""np""" )
         self.assertFalse(len(cs.err )       ,		msg=F"should have no warning, but got {cs.err}" )

         __A   			=		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0} )
         __A   			=		batch.convert_to_tensors(tensor_type="""np"""       ,		prepend_batch_axis=__UpperCAmelCase )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(1, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(1,) )




     @require_torch
     def 						lowerCAmelCase_   ( self				:	Dict ):


         """simple docstring"""



         __A   			=		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]} )
         __A   			=		batch.convert_to_tensors(tensor_type="""pt""" )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(2, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(2,) )
         # test converting the converted
         with CaptureStderr() as cs:
             __A   			=		batch.convert_to_tensors(tensor_type="""pt""" )
         self.assertFalse(len(cs.err )       ,		msg=F"should have no warning, but got {cs.err}" )

         __A   			=		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0} )
         __A   			=		batch.convert_to_tensors(tensor_type="""pt"""       ,		prepend_batch_axis=__UpperCAmelCase )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(1, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(1,) )




     @require_tf
     def 						lowerCAmelCase_   ( self				:	Dict ):


         """simple docstring"""



         __A   			=		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]} )
         __A   			=		batch.convert_to_tensors(tensor_type="""tf""" )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(2, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(2,) )
         # test converting the converted
         with CaptureStderr() as cs:
             __A   			=		batch.convert_to_tensors(tensor_type="""tf""" )
         self.assertFalse(len(cs.err )       ,		msg=F"should have no warning, but got {cs.err}" )

         __A   			=		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0} )
         __A   			=		batch.convert_to_tensors(tensor_type="""tf"""       ,		prepend_batch_axis=__UpperCAmelCase )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(1, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(1,) )




     @require_flax
     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         __A   			=		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]} )
         __A   			=		batch.convert_to_tensors(tensor_type="""jax""" )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(2, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(2,) )
         # test converting the converted
         with CaptureStderr() as cs:
             __A   			=		batch.convert_to_tensors(tensor_type="""jax""" )
         self.assertFalse(len(cs.err )       ,		msg=F"should have no warning, but got {cs.err}" )

         __A   			=		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0} )
         __A   			=		batch.convert_to_tensors(tensor_type="""jax"""       ,		prepend_batch_axis=__UpperCAmelCase )
         self.assertEqual(tensor_batch["""inputs"""].shape       ,		(1, 3) )
         self.assertEqual(tensor_batch["""labels"""].shape       ,		(1,) )




     def 						lowerCAmelCase_   ( self				:	Union[str, Any] ):


         """simple docstring"""



         __A   			=		[{"""input_ids""": np.array([0, 1, 2] )}, {"""input_ids""": np.array([0, 1, 2, 3] )}]
         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )

         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		np.ndarray ) )
         self.assertEqual(batch["""input_ids"""].tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase       ,		return_tensors="""np""" )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		np.ndarray ) )
         self.assertEqual(batch["""input_ids"""].tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )




     @require_torch
     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         import torch

         __A   			=		[{"""input_ids""": torch.tensor([0, 1, 2] )}, {"""input_ids""": torch.tensor([0, 1, 2, 3] )}]
         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )

         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		torch.Tensor ) )
         self.assertEqual(batch["""input_ids"""].tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase       ,		return_tensors="""pt""" )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		torch.Tensor ) )
         self.assertEqual(batch["""input_ids"""].tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )




     @require_tf
     def 						lowerCAmelCase_   ( self				:	Optional[Any] ):


         """simple docstring"""



         import tensorflow as tf

         __A   			=		[{"""input_ids""": tf.constant([0, 1, 2] )}, {"""input_ids""": tf.constant([0, 1, 2, 3] )}]
         __A   			=		BertTokenizer.from_pretrained("""bert-base-cased""" )

         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		tf.Tensor ) )
         self.assertEqual(batch["""input_ids"""].numpy().tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
         __A   			=		tokenizer.pad(__UpperCAmelCase       ,		padding=__UpperCAmelCase       ,		return_tensors="""tf""" )
         self.assertTrue(isinstance(batch["""input_ids"""]       ,		tf.Tensor ) )
         self.assertEqual(batch["""input_ids"""].numpy().tolist()       ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )




     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	List[str] ):


         """simple docstring"""



         __A   			=		Tokenizer(WordPiece(unk_token="""[UNK]""" ) )
         PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase )





     @require_tokenizers
     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         __A   			=		Tokenizer(WordPiece(unk_token="""[UNK]""" ) )
         with tempfile.TemporaryDirectory() as tmpdirname:
             bert_tokenizer.save(os.path.join(__UpperCAmelCase       ,		"""tokenizer.json""" ) )
             PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase       ,		"""tokenizer.json""" ) )









import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						str						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Dict=13						,  __UpperCAmelCase	:						Optional[int]=32						,  __UpperCAmelCase	:						Optional[Any]=2						,  __UpperCAmelCase	:						Union[str, Any]=3						,  __UpperCAmelCase	:						Dict=16						,  __UpperCAmelCase	:						List[str]=[1, 2, 1]						,  __UpperCAmelCase	:						str=[2, 2, 4]						,  __UpperCAmelCase	:						Optional[Any]=2						,  __UpperCAmelCase	:						Optional[Any]=2.0						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						Tuple=0.0						,  __UpperCAmelCase	:						Dict=0.0						,  __UpperCAmelCase	:						int=0.1						,  __UpperCAmelCase	:						List[str]="gelu"						,  __UpperCAmelCase	:						Dict=False						,  __UpperCAmelCase	:						Optional[Any]=True						,  __UpperCAmelCase	:						str=0.02						,  __UpperCAmelCase	:						List[str]=1E-5						,  __UpperCAmelCase	:						int=True						,  __UpperCAmelCase	:						List[str]=None						,  __UpperCAmelCase	:						Union[str, Any]=True						,  __UpperCAmelCase	:						List[str]=10						,  __UpperCAmelCase	:						Dict=8						,  __UpperCAmelCase	:						Optional[Any]=["stage1", "stage2"]						,  __UpperCAmelCase	:						Optional[int]=[1, 2]						,  )			-> Optional[int]:
							UpperCAmelCase_=   parent
							UpperCAmelCase_=   batch_size
							UpperCAmelCase_=   image_size
							UpperCAmelCase_=   patch_size
							UpperCAmelCase_=   num_channels
							UpperCAmelCase_=   embed_dim
							UpperCAmelCase_=   depths
							UpperCAmelCase_=   num_heads
							UpperCAmelCase_=   window_size
							UpperCAmelCase_=   mlp_ratio
							UpperCAmelCase_=   qkv_bias
							UpperCAmelCase_=   hidden_dropout_prob
							UpperCAmelCase_=   attention_probs_dropout_prob
							UpperCAmelCase_=   drop_path_rate
							UpperCAmelCase_=   hidden_act
							UpperCAmelCase_=   use_absolute_embeddings
							UpperCAmelCase_=   patch_norm
							UpperCAmelCase_=   layer_norm_eps
							UpperCAmelCase_=   initializer_range
							UpperCAmelCase_=   is_training
							UpperCAmelCase_=   scope
							UpperCAmelCase_=   use_labels
							UpperCAmelCase_=   type_sequence_label_size
							UpperCAmelCase_=   encoder_stride
							UpperCAmelCase_=   out_features
							UpperCAmelCase_=   out_indices




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> List[str]:
							UpperCAmelCase_=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

							UpperCAmelCase_=   self.get_config()

							return config, pixel_values, labels




	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Optional[int]:
							return SwinConfig(
							    image_size=self.image_size						,  patch_size=self.patch_size						,  num_channels=self.num_channels						,  embed_dim=self.embed_dim						,  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   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Dict     )			-> Optional[Any]:
							UpperCAmelCase_=   SwinModel(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )

							UpperCAmelCase_=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						Optional[Any]     )			-> Optional[int]:
							UpperCAmelCase_=   SwinBackbone(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps     )						,  len(config.out_features     )     )
							self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,  [self.batch_size, model.channels[0], 16, 16]     )

							# verify channels
							self.parent.assertEqual(len(model.channels     )						,  len(config.out_features     )     )

							# verify backbone works with out_features=None
							UpperCAmelCase_=   None
							UpperCAmelCase_=   SwinBackbone(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   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, model.channels[-1], 4, 4]     )

							# verify channels
							self.parent.assertEqual(len(model.channels     )						,  1     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						List[str]     )			-> int:
							UpperCAmelCase_=   SwinForMaskedImageModeling(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )
							self.parent.assertEqual(
							    result.logits.shape						,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							UpperCAmelCase_=   1
							UpperCAmelCase_=   SwinForMaskedImageModeling(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						int     )			-> List[Any]:
							UpperCAmelCase_=   self.type_sequence_label_size
							UpperCAmelCase_=   SwinForImageClassification(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase						,  labels=__UpperCAmelCase     )
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							UpperCAmelCase_=   1
							UpperCAmelCase_=   SwinForImageClassification(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Any:
							UpperCAmelCase_=   self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   
							)=   config_and_inputs
							UpperCAmelCase_=   {"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 		lowercase						( snake_case__ ,    snake_case__ ,    unittest.TestCase):






	"""simple docstring"""


	a__      :					int		   =							(
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	a__      :					str		   =							(
	    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	a__      :					int		   =							True

	a__      :					Tuple		   =							False
	a__      :					Union[str, Any]		   =							False
	a__      :					Optional[int]		   =							False




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Optional[int]:
							UpperCAmelCase_=   SwinModelTester(self     )
							UpperCAmelCase_=   ConfigTester(self						,  config_class=__UpperCAmelCase						,  embed_dim=37     )




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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> List[Any]:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase     )




	@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Tuple:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> List[str]:
							super().test_training_gradient_checkpointing()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> int:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Tuple:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Optional[Any]:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )




	@unittest.skip(reason="""Swin does not use inputs_embeds"""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Dict:
							pass




	@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Union[str, Any]:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Any:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

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

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Tuple:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=   True

							for model_class in self.all_model_classes:
													UpperCAmelCase_=   True
													UpperCAmelCase_=   False
													UpperCAmelCase_=   True
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )
													UpperCAmelCase_=   outputs.attentions
													UpperCAmelCase_=   len(self.model_tester.depths     )
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													UpperCAmelCase_=   True
													UpperCAmelCase_=   config.window_size**2
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )
													UpperCAmelCase_=   outputs.attentions
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )
													UpperCAmelCase_=   len(__UpperCAmelCase     )

													# Check attention is always last and order is fine
													UpperCAmelCase_=   True
													UpperCAmelCase_=   True
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )

													# also another +1 for reshaped_hidden_states
													UpperCAmelCase_=   1 if model_class.__name__ == """SwinBackbone""" else 2
													self.assertEqual(out_len + added_hidden_states						,  len(__UpperCAmelCase     )     )

													UpperCAmelCase_=   outputs.attentions

													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Dict     )			-> str:
							UpperCAmelCase_=   model_class(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

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

							UpperCAmelCase_=   outputs.hidden_states

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

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

							UpperCAmelCase_=   (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]						,  )

							if not model_class.__name__ == "SwinBackbone":
													UpperCAmelCase_=   outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   reshaped_hidden_states[0].shape
													UpperCAmelCase_=   (
													    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   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Tuple:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_=   (
							    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:
													UpperCAmelCase_=   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"]
													UpperCAmelCase_=   True

													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=   3

							UpperCAmelCase_=   (
							    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)
							)
							UpperCAmelCase_=   (
							    config.patch_size
							    if isinstance(config.patch_size						,  collections.abc.Iterable     )
							    else (config.patch_size, config.patch_size)
							)

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

							for model_class in self.all_model_classes:
													UpperCAmelCase_=   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"]
													UpperCAmelCase_=   True
													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  (padded_height, padded_width)     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> List[Any]:
							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase_=   SwinModel.from_pretrained(__UpperCAmelCase     )
													self.assertIsNotNone(__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Optional[int]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_=   _config_zero_init(__UpperCAmelCase     )
							for model_class in self.all_model_classes:
													UpperCAmelCase_=   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 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	@cached_property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> str:
							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""     )
							    if is_vision_available()
							    else None
							)




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> int:
							UpperCAmelCase_=   SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""     ).to(__UpperCAmelCase     )
							UpperCAmelCase_=   self.default_image_processor

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

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

							# verify the logits
							UpperCAmelCase_=   torch.Size((1, 1_000)     )
							self.assertEqual(outputs.logits.shape						,  __UpperCAmelCase     )
							UpperCAmelCase_=   torch.tensor([-0.0_948, -0.6_454, -0.0_921]     ).to(__UpperCAmelCase     )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]						,  __UpperCAmelCase						,  atol=1E-4     )     )






@require_torch
class 		lowercase						( unittest.TestCase ,    snake_case__):






	"""simple docstring"""


	a__      :					Any		   =							(SwinBackbone,) if is_torch_available() else ()
	a__      :					int		   =							SwinConfig




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Optional[Any]:
							UpperCAmelCase_=   SwinModelTester(self     )




