


import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       OpenAIGPTConfig,
       OpenAIGPTDoubleHeadsModel,
       OpenAIGPTForSequenceClassification,
       OpenAIGPTLMHeadModel,
       OpenAIGPTModel,
   )



class        a					:





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				str=13    ,	SCREAMING_SNAKE_CASE_					:				List[str]=7    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				List[str]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=99    ,	SCREAMING_SNAKE_CASE_					:				Any=32    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=5    ,	SCREAMING_SNAKE_CASE_					:				Tuple=4    ,	SCREAMING_SNAKE_CASE_					:				Any=37    ,	SCREAMING_SNAKE_CASE_					:				List[Any]="gelu"    ,	SCREAMING_SNAKE_CASE_					:				str=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=512    ,	SCREAMING_SNAKE_CASE_					:				Dict=16    ,	SCREAMING_SNAKE_CASE_					:				List[str]=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.02    ,	SCREAMING_SNAKE_CASE_					:				List[str]=3    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	):
        __lowerCamelCase:				Optional[int]     =	parent
        __lowerCamelCase:				Tuple     =	batch_size
        __lowerCamelCase:				int     =	seq_length
        __lowerCamelCase:				Optional[int]     =	is_training
        __lowerCamelCase:				List[str]     =	use_token_type_ids
        __lowerCamelCase:				Optional[int]     =	use_labels
        __lowerCamelCase:				Dict     =	vocab_size
        __lowerCamelCase:				str     =	hidden_size
        __lowerCamelCase:				Any     =	num_hidden_layers
        __lowerCamelCase:				Optional[Any]     =	num_attention_heads
        __lowerCamelCase:				Any     =	intermediate_size
        __lowerCamelCase:				Optional[int]     =	hidden_act
        __lowerCamelCase:				Any     =	hidden_dropout_prob
        __lowerCamelCase:				List[str]     =	attention_probs_dropout_prob
        __lowerCamelCase:				Dict     =	max_position_embeddings
        __lowerCamelCase:				Union[str, Any]     =	type_vocab_size
        __lowerCamelCase:				List[str]     =	type_sequence_label_size
        __lowerCamelCase:				List[Any]     =	initializer_range
        __lowerCamelCase:				List[Any]     =	num_labels
        __lowerCamelCase:				List[str]     =	num_choices
        __lowerCamelCase:				Tuple     =	scope
        __lowerCamelCase:				Union[str, Any]     =	self.vocab_size - 1





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Dict     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				str     =	None
        if self.use_token_type_ids:
           __lowerCamelCase:				Optional[Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.type_vocab_size							)

        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Dict     =	None
        __lowerCamelCase:				Any     =	None
        if self.use_labels:
           __lowerCamelCase:				Any     =	ids_tensor([self.batch_size]    ,	self.type_sequence_label_size							)
           __lowerCamelCase:				Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels							)
           __lowerCamelCase:				Dict     =	ids_tensor([self.batch_size]    ,	self.num_choices							)

        __lowerCamelCase:				Optional[Any]     =	OpenAIGPTConfig(
            vocab_size=self.vocab_size    ,	n_embd=self.hidden_size    ,	n_layer=self.num_hidden_layers    ,	n_head=self.num_attention_heads    ,	n_positions=self.max_position_embeddings    ,	pad_token_id=self.pad_token_id    ,	)

        __lowerCamelCase:				Optional[int]     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,	2							)

        return (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str    ,	*SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				int     =	OpenAIGPTModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				List[str]     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	head_mask=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)

        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	*SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				List[Any]     =	OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.loss.shape    ,	()							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	*SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Union[str, Any]     =	OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.loss.shape    ,	()							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	*SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Tuple     =	self.num_labels
        __lowerCamelCase:				int     =	OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Dict     =	ids_tensor([self.batch_size]    ,	self.type_sequence_label_size							)
        __lowerCamelCase:				Tuple     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Optional[Any]     =	self.prepare_config_and_inputs()
        (
            (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     
        ):				str     =	config_and_inputs
        __lowerCamelCase:				Dict     =	{
            """input_ids""": input_ids,
            """token_type_ids""": token_type_ids,
            """head_mask""": head_mask,
        }

        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(
         (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__ :  Union[str, Any]						      =	(
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
     UpperCAmelCase__ :  int						      =	(
         {
             "feature-extraction": OpenAIGPTModel,
             "text-classification": OpenAIGPTForSequenceClassification,
             "text-generation": OpenAIGPTLMHeadModel,
             "zero-shot": OpenAIGPTForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
           # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
           # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
           # tiny config could not be created.
           return True

        return False





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str=False							):
        __lowerCamelCase:				Optional[int]     =	super()._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	return_labels=SCREAMING_SNAKE_CASE_							)

        if return_labels:
           if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
              __lowerCamelCase:				List[str]     =	torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_    ,	)
              __lowerCamelCase:				List[str]     =	inputs_dict["""labels"""]
              __lowerCamelCase:				Optional[Any]     =	inputs_dict["""labels"""]
              __lowerCamelCase:				Any     =	torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.num_choices)    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_    ,	)
              __lowerCamelCase:				Dict     =	torch.zeros(
                  self.model_tester.batch_size    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_							)
        return inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[str]     =	OpenAIGPTModelTester(self							)
        __lowerCamelCase:				Dict     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	n_embd=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Optional[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[int]     =	OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



@require_torch
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Dict     =	OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""							)
        model.to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	torch.tensor([[481, 4735, 544]]    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_							)  # the president is
        __lowerCamelCase:				Tuple     =	[
            481,
            4735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            40477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  # the president is a very good man. " \n " i\'m sure he is, " said the

        __lowerCamelCase:				Optional[int]     =	model.generate(SCREAMING_SNAKE_CASE_    ,	do_sample=SCREAMING_SNAKE_CASE_							)
        self.assertListEqual(output_ids[0].tolist()    ,	SCREAMING_SNAKE_CASE_							)






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        a					:





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





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

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

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        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=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.num_labels
        __lowerCamelCase:				Union[str, Any]     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Tuple     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				Any     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)  # 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(SCREAMING_SNAKE_CASE_							)							):
              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),
                      ]							)    ,	)

        __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				Optional[int]     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_SNAKE_CASE_							)
           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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

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

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_SNAKE_CASE_							)
           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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





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







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]							).to(SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)


