
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    BioGptForCausalLM,
					    BioGptForSequenceClassification,
					    BioGptForTokenClassification,
					    BioGptModel,
					    BioGptTokenizer,
					)
					from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: Tuple							,A					: str=13							,A					: List[str]=7							,A					: Dict=True							,A					: Optional[Any]=True							,A					: Union[str, Any]=False							,A					: Optional[int]=True							,A					: int=99							,A					: Any=32							,A					: int=5							,A					: Tuple=4							,A					: Optional[Any]=37							,A					: Dict="gelu"							,A					: int=0.1							,A					: Optional[Any]=0.1							,A					: int=5_12							,A					: Tuple=16							,A					: Any=2							,A					: int=0.02							,A					: Optional[int]=3							,A					: str=4							,A					: Tuple=None							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= seq_length
					__A   			= is_training
					__A   			= use_input_mask
					__A   			= use_token_type_ids
					__A   			= use_labels
					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= type_sequence_label_size
					__A   			= initializer_range
					__A   			= num_labels
					__A   			= num_choices
					__A   			= scope






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

					__A   			= None
					if self.use_input_mask:
									__A   			= random_attention_mask([self.batch_size, self.seq_length] )

					__A   			= None
					if self.use_token_type_ids:
									__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.type_vocab_size )

					__A   			= None
					__A   			= None
					__A   			= None
					if self.use_labels:
									__A   			= ids_tensor([self.batch_size]							,self.type_sequence_label_size )
									__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.num_labels )
									__A   			= ids_tensor([self.batch_size]							,self.num_choices )

					__A   			= self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					return BioGptConfig(
					    vocab_size=self.vocab_size							,hidden_size=self.hidden_size							,num_hidden_layers=self.num_hidden_layers							,num_attention_heads=self.num_attention_heads							,intermediate_size=self.intermediate_size							,hidden_act=self.hidden_act							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,max_position_embeddings=self.max_position_embeddings							,type_vocab_size=self.type_vocab_size							,is_decoder=A							,initializer_range=self.initializer_range							,)






	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any]							,A					: Any							,A					: Any							,A					: Any							,A					: int							,A					: List[str]							,A					: List[Any] ):
					__A   			= BioGptModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A )
					__A   			= model(A )
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size) )






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple							,A					: str							,A					: Optional[Any]							,A					: str							,A					: Dict							,A					: Dict							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple							,):
					__A   			= BioGptForCausalLM(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,token_type_ids=A							,labels=A )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size) )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: int							,A					: Dict							,A					: Optional[Any]							,A					: List[Any]							,A					: Any							,*A					: Optional[Any] ):
					__A   			= BioGptModel(config=A )
					model.to(A )
					model.eval()

					# create attention mask
					__A   			= torch.ones(input_ids.shape							,dtype=torch.long							,device=A )
					__A   			= self.seq_length // 2
					__A   			= 0

					# first forward pass
					__A			,							__A   			= model(A							,attention_mask=A ).to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__A   			= ids_tensor((self.batch_size, 1)							,config.vocab_size )

					# change a random masked slice from input_ids
					__A   			= ids_tensor((1,)							,A ).item() + 1
					__A   			= ids_tensor((self.batch_size, 1)							,config.vocab_size ).squeeze(-1 )
					__A   			= random_other_next_tokens

					# append to next input_ids and attn_mask
					__A   			= torch.cat([input_ids, next_tokens]							,dim=-1 )
					__A   			= torch.cat(
					    [attn_mask, torch.ones((attn_mask.shape[0], 1)							,dtype=torch.long							,device=A )]							,dim=1							,)

					# get two different outputs
					__A   			= model(A							,attention_mask=A )["last_hidden_state"]
					__A   			= model(A							,past_key_values=A							,attention_mask=A )["last_hidden_state"]

					# select random slice
					__A   			= ids_tensor((1,)							,output_from_past.shape[-1] ).item()
					__A   			= output_from_no_past[:, -1, random_slice_idx].detach()
					__A   			= output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(A							,A							,atol=1E-3 ) )






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[str]							,A					: Dict							,A					: Dict							,A					: Tuple							,A					: Optional[int]							,*A					: int ):
					__A   			= BioGptModel(config=A ).to(A ).eval()

					__A   			= torch.ones(input_ids.shape							,dtype=torch.long							,device=A )

					# first forward pass
					__A   			= model(A							,attention_mask=A							,use_cache=A )

					__A			,							__A   			= outputs.to_tuple()

					# create hypothetical multiple next token and extent to next_input_ids
					__A   			= ids_tensor((self.batch_size, 3)							,config.vocab_size )
					__A   			= ids_tensor((self.batch_size, 3)							,2 )

					# append to next input_ids and
					__A   			= torch.cat([input_ids, next_tokens]							,dim=-1 )
					__A   			= torch.cat([attention_mask, next_attn_mask]							,dim=-1 )

					__A   			= model(A							,attention_mask=A )["last_hidden_state"]
					__A   			= model(A							,attention_mask=A							,past_key_values=A )[
					    "last_hidden_state"
					]

					# select random slice
					__A   			= ids_tensor((1,)							,output_from_past.shape[-1] ).item()
					__A   			= output_from_no_past[:, -3:, random_slice_idx].detach()
					__A   			= output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(A							,A							,atol=1E-3 ) )






	def 				UpperCamelCase_  (       self					: Any							,A					: Union[str, Any]							,A					: List[Any]							,A					: str							,A					: List[Any]							,A					: Optional[int]							,*A					: str							,A					: List[Any]=False ):
					__A   			= BioGptForCausalLM(A )
					model.to(A )
					if gradient_checkpointing:
									model.gradient_checkpointing_enable()

					__A   			= model(A							,labels=A )
					self.parent.assertEqual(result.loss.shape							,() )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size) )
					result.loss.backward()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: str							,*A					: List[Any] ):
					__A   			= BioGptModel(A )
					__A   			= model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
					for key in model.state_dict().keys():
									if "c_proj" in key and "weight" in key:
													self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std )							,0.0_01 )
													self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )							,0.01 )






	def 				UpperCamelCase_  (       self					: Dict							,A					: str							,A					: int							,A					: str							,A					: List[Any]							,A					: str							,*A					: Optional[Any] ):
					__A   			= self.num_labels
					__A   			= BioGptForTokenClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,token_type_ids=A )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.num_labels) )







	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.prepare_config_and_inputs()
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= config_and_inputs
					__A   			= {"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	snake_case_					    =     (BioGptForCausalLM,) if is_torch_available() else ()
	snake_case_					    =     (
	    {
	        "feature-extraction": BioGptModel,
	        "text-classification": BioGptForSequenceClassification,
	        "text-generation": BioGptForCausalLM,
	        "token-classification": BioGptForTokenClassification,
	        "zero-shot": BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= BioGptModelTester(self )
					__A   			= ConfigTester(self							,config_class=A							,hidden_size=37 )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					self.config_tester.run_common_tests()






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									__A   			= type
									self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_forward_and_backwards(*A							,gradient_checkpointing=A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_weight_initialization(*A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_for_token_classification(*A )






	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					model.to(A )
					__A   			= BioGptTokenizer.from_pretrained("microsoft/biogpt" )

					__A   			= "left"

					# Define PAD Token = EOS Token = 50256
					__A   			= tokenizer.eos_token
					__A   			= model.config.eos_token_id

					# use different length sentences to test batching
					__A   			= [
					    "Hello, my dog is a little",
					    "Today, I",
					]

					__A   			= tokenizer(A							,return_tensors="pt"							,padding=A )
					__A   			= inputs["input_ids"].to(A )

					__A   			= model.generate(
					    input_ids=A							,attention_mask=inputs["attention_mask"].to(A )							,)

					__A   			= tokenizer(sentences[0]							,return_tensors="pt" ).input_ids.to(A )
					__A   			= model.generate(input_ids=A )

					__A   			= inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
					__A   			= tokenizer(sentences[1]							,return_tensors="pt" ).input_ids.to(A )
					__A   			= model.generate(input_ids=A							,max_length=model.config.max_length - num_paddings )

					__A   			= tokenizer.batch_decode(A							,skip_special_tokens=A )
					__A   			= tokenizer.decode(output_non_padded[0]							,skip_special_tokens=A )
					__A   			= tokenizer.decode(output_padded[0]							,skip_special_tokens=A )

					__A   			= [
					    "Hello, my dog is a little bit bigger than a little bit.",
					    "Today, I have a good idea of how to use the information",
					]
					self.assertListEqual(A							,A )
					self.assertListEqual(A							,[non_padded_sentence, padded_sentence] )






	@slow
	def 				UpperCamelCase_  (       self					: str ):
					for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= BioGptModel.from_pretrained(A )
									self.assertIsNotNone(A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					__A   			= 3
					__A   			= input_dict["input_ids"]
					__A   			= input_ids.ne(1 ).to(A )
					__A   			= ids_tensor([self.model_tester.batch_size]							,self.model_tester.type_sequence_label_size )
					__A   			= BioGptForSequenceClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,labels=A )
					self.assertEqual(result.logits.shape							,(self.model_tester.batch_size, self.model_tester.num_labels) )







	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					__A   			= 3
					__A   			= "multi_label_classification"
					__A   			= input_dict["input_ids"]
					__A   			= input_ids.ne(1 ).to(A )
					__A   			= ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]							,self.model_tester.type_sequence_label_size ).to(torch.float )
					__A   			= BioGptForSequenceClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,labels=A )
					self.assertEqual(result.logits.shape							,(self.model_tester.batch_size, self.model_tester.num_labels) )





@require_torch
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					__A   			= torch.tensor([[2, 48_05, 9, 6_56, 21]] )
					__A   			= model(A )[0]

					__A   			= 4_23_84

					__A   			= torch.Size((1, 5, vocab_size) )
					self.assertEqual(output.shape							,A )

					__A   			= torch.tensor(
					    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]] )

					self.assertTrue(torch.allclose(output[:, :3, :3]							,A							,atol=1E-4 ) )







	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= BioGptTokenizer.from_pretrained("microsoft/biogpt" )
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					model.to(A )

					torch.manual_seed(0 )
					__A   			= tokenizer("COVID-19 is"							,return_tensors="pt" ).to(A )
					__A   			= model.generate(
					    **A							,min_length=1_00							,max_length=10_24							,num_beams=5							,early_stopping=A							,)
					__A   			= tokenizer.decode(output_ids[0]							,skip_special_tokens=A )

					__A   			= (
					    "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
					    " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
					    " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
					    " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
					    " more than 800,000 deaths."
					)
					self.assertEqual(A							,A )





'''simple docstring'''




import copy
import inspect
import unittest

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

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


if is_torch_available():
						import torch
						from torch import nn

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


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 	_lowerCAmelCase							:


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


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

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

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels


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


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     SwiftFormerModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.embed_dims[-1], 7, 7)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						Any	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[int]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

				A_	:						int	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_	:						int	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
				A_	:						Dict	=     model(lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				((A_),    (A_),    (A_))	:						int	=     self.prepare_config_and_inputs()
				A_	:						Any	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Optional[int]	=     SwiftFormerModelTester(self							)
				A_	:						Any	=     ConfigTester(
				    self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37       ,      num_attention_heads=12       ,      num_hidden_layers=12       ,      )


	def     _a	(self							):
				self.config_tester.run_common_tests()


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


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						Optional[int]	=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase       ,      nn.Linear							)							)


	def     _a	(self							):
				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

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

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						str	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						Optional[Any]	=     SwiftFormerModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


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


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						str	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Optional[int]	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						Any	=     outputs.hidden_states

							A_	:						Any	=     8
							self.assertEqual(len(lowercase							)       ,      lowercase							)  # TODO

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

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A_	:						str	=     True

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				def _config_zero_init(lowercase							):
							A_	:						Optional[Any]	=     copy.deepcopy(lowercase							)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowercase       ,      lowercase       ,      1E-10							)
										if isinstance(getattr(lowercase       ,      lowercase       ,      lowercase							)       ,      lowercase							):
													A_	:						Any	=     _config_zero_init(getattr(lowercase       ,      lowercase							)							)
													setattr(lowercase       ,      lowercase       ,      lowercase							)
							return configs_no_init

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

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


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






def 	a			( ):
			'''simple docstring'''





			A_	:						List[str]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







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


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


	@slow
	def     _a	(self							):
				A_	:						Any	=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowercase							)

				A_	:						Dict	=     self.default_image_processor
				A_	:						Dict	=     prepare_img()
				A_	:						int	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						int	=     model(**lowercase							)

				# verify the logits
				A_	:						int	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]							).to(lowercase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowercase       ,      atol=1E-4							)							)