



import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    CpmAntConfig,
	    CpmAntForCausalLM,
	    CpmAntModel,
	    CpmAntTokenizer,
	)






@require_torch
class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						List[str]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						int=2						,  __UpperCAmelCase	:						int=8						,  __UpperCAmelCase	:						List[str]=True						,  __UpperCAmelCase	:						Any=False						,  __UpperCAmelCase	:						str=False						,  __UpperCAmelCase	:						Optional[Any]=False						,  __UpperCAmelCase	:						Optional[Any]=False						,  __UpperCAmelCase	:						int=99						,  __UpperCAmelCase	:						Dict=32						,  __UpperCAmelCase	:						str=3						,  __UpperCAmelCase	:						Optional[Any]=4						,  __UpperCAmelCase	:						Dict=37						,  __UpperCAmelCase	:						List[str]=32						,  __UpperCAmelCase	:						List[str]=128						,  __UpperCAmelCase	:						Tuple=8						,  __UpperCAmelCase	:						int=8						,  __UpperCAmelCase	:						Optional[Any]=8						,  __UpperCAmelCase	:						List[Any]=1.0						,  __UpperCAmelCase	:						Tuple=True						,  )			-> Dict:
							UpperCAmelCase_=   parent
							UpperCAmelCase_=   batch_size
							UpperCAmelCase_=   seq_length
							UpperCAmelCase_=   is_training
							UpperCAmelCase_=   use_token_type_ids
							UpperCAmelCase_=   use_input_mask
							UpperCAmelCase_=   use_labels
							UpperCAmelCase_=   use_mc_token_ids
							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   hidden_size
							UpperCAmelCase_=   num_hidden_layers
							UpperCAmelCase_=   num_attention_heads
							UpperCAmelCase_=   intermediate_size
							UpperCAmelCase_=   num_buckets
							UpperCAmelCase_=   max_distance
							UpperCAmelCase_=   prompt_length
							UpperCAmelCase_=   prompt_types
							UpperCAmelCase_=   segment_types
							UpperCAmelCase_=   init_std
							UpperCAmelCase_=   return_dict




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Dict:
							UpperCAmelCase_=   {}
							UpperCAmelCase_=   ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     ).type(torch.intaa     )
							UpperCAmelCase_=   False

							UpperCAmelCase_=   self.get_config()

							return (config, input_ids)




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Dict:
							return CpmAntConfig(
							    vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  num_attention_heads=self.num_attention_heads						,  dim_ff=self.intermediate_size						,  position_bias_num_buckets=self.num_buckets						,  position_bias_max_distance=self.max_distance						,  prompt_types=self.prompt_types						,  prompt_length=self.prompt_length						,  segment_types=self.segment_types						,  use_cache=__UpperCAmelCase						,  init_std=self.init_std						,  return_dict=self.return_dict						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						int						,  *__UpperCAmelCase	:						int     )			-> Tuple:
							UpperCAmelCase_=   CpmAntModel(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							UpperCAmelCase_=   model(**__UpperCAmelCase     ).last_hidden_state

							self.parent.assertEqual(hidden_states.shape						,  (self.batch_size, self.seq_length, config.hidden_size)     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						Optional[Any]						,  *__UpperCAmelCase	:						Optional[int]     )			-> List[str]:
							UpperCAmelCase_=   CpmAntForCausalLM(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							UpperCAmelCase_=   input_ids["""input_ids"""].to(__UpperCAmelCase     )
							model.eval()

							UpperCAmelCase_=   model(**__UpperCAmelCase     )
							self.parent.assertEqual(
							    model_output.logits.shape						,  (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> str:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_config_and_inputs()
							return config, inputs_dict






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






	"""simple docstring"""


	a__      :					List[Any]		   =							(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
	a__      :					Optional[int]		   =							(
	    {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
	)

	a__      :					int		   =							False
	a__      :					Dict		   =							False
	a__      :					Any		   =							False
	a__      :					Dict		   =							False
	a__      :					List[str]		   =							False




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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Any:
							self.config_tester.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.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Any:
							unittest.skip("""CPMAnt doesn't support input_embeds."""     )(self.test_inputs_embeds     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Tuple:
							unittest.skip(
							    """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""     )(self.test_retain_grad_hidden_states_attentions     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Optional[int]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_cpmant_model(__UpperCAmelCase						,  __UpperCAmelCase     )




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






@require_torch
class 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	@tooslow
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Optional[int]:
							UpperCAmelCase_=   """今天天气真好！"""
							UpperCAmelCase_=   """openbmb/cpm-ant-10b"""
							UpperCAmelCase_=   CpmAntModel.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   CpmAntTokenizer.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   tokenizer(__UpperCAmelCase						,  return_tensors="""pt"""     )
							UpperCAmelCase_=   model(**__UpperCAmelCase     ).last_hidden_state

							UpperCAmelCase_=   torch.tensor(
							    [[[6.1_708, 5.9_244, 1.0_835], [6.5_207, 6.2_893, -11.3_324], [-1.0_107, -0.0_576, -5.9_577]]]						,  )
							self.assertTrue(torch.allclose(hidden_states[:, :3, :3]						,  __UpperCAmelCase						,  atol=1E-2     )     )






@require_torch
class 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	@tooslow
	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> Union[str, Any]:
							UpperCAmelCase_=   """今天天气真好！"""
							UpperCAmelCase_=   """openbmb/cpm-ant-10b"""
							UpperCAmelCase_=   CpmAntForCausalLM.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   CpmAntTokenizer.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   tokenizer(__UpperCAmelCase						,  return_tensors="""pt"""     )
							UpperCAmelCase_=   model(**__UpperCAmelCase     ).logits

							UpperCAmelCase_=   torch.tensor(
							    [[[-6.4_267, -6.4_083, -6.3_958], [-5.8_802, -5.9_447, -5.7_811], [-5.3_896, -5.4_820, -5.4_295]]]						,  )
							self.assertTrue(torch.allclose(hidden_states[:, :3, :3]						,  __UpperCAmelCase						,  atol=1E-2     )     )




	@tooslow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> List[Any]:
							UpperCAmelCase_=   """openbmb/cpm-ant-10b"""
							UpperCAmelCase_=   CpmAntForCausalLM.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   CpmAntTokenizer.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   """今天天气不错，"""
							UpperCAmelCase_=   """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
							UpperCAmelCase_=   tokenizer(__UpperCAmelCase						,  return_tensors="""pt"""     )
							UpperCAmelCase_=   model.generate(**__UpperCAmelCase     )
							UpperCAmelCase_=   tokenizer.batch_decode(__UpperCAmelCase     )
							self.assertEqual(__UpperCAmelCase						,  __UpperCAmelCase     )




	@tooslow
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Any:
							UpperCAmelCase_=   """openbmb/cpm-ant-10b"""
							UpperCAmelCase_=   CpmAntForCausalLM.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   CpmAntTokenizer.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   ["""今天天气不错，""", """新年快乐，万事如意！"""]
							UpperCAmelCase_=   [
							    """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
							    """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
							]
							UpperCAmelCase_=   tokenizer(__UpperCAmelCase						,  return_tensors="""pt"""						,  padding=__UpperCAmelCase     )
							UpperCAmelCase_=   model.generate(**__UpperCAmelCase     )
							UpperCAmelCase_=   tokenizer.batch_decode(__UpperCAmelCase     )
							self.assertEqual(__UpperCAmelCase						,  __UpperCAmelCase     )









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     )




