'''simple docstring'''





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 			__A :



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



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size					)

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

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

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

         UpperCAmelCase_			       =  self.get_config()

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



    def    _lowercase		(self       :       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    _lowercase		(self       :       Tuple  ,     __a       :       Dict  ,     __a       :       Any  ,     __a       :       List[str]  ,     __a       :       Dict  ,     __a       :       Any  ,     __a       :       Any  ,     __a       :       List[Any]					):
         UpperCAmelCase_			       =  BioGptModel(config=__a					)
         model.to(__a					)
         model.eval()
         UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a					)
         UpperCAmelCase_			       =  model(__a					)
         self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)					)



    def    _lowercase		(self       :       List[Any]  ,     __a       :       Any  ,     __a       :       Union[str, Any]  ,     __a       :       Optional[Any]  ,     __a       :       int  ,     __a       :       Optional[Any]  ,     __a       :       Any  ,     __a       :       List[str]  ,     __a       :       Optional[Any]  ,     __a       :       List[Any]  ,     ):
         UpperCAmelCase_			       =  BioGptForCausalLM(config=__a					)
         model.to(__a					)
         model.eval()
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Optional[int]  ,     __a       :       Tuple  ,     __a       :       List[Any]  ,     __a       :       str  ,     __a       :       Dict  ,     __a       :       Tuple  ,     *__a       :       List[Any]					):
         UpperCAmelCase_			       =  BioGptModel(config=__a					)
         model.to(__a					)
         model.eval()

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

         # first forward pass
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a					).to_tuple()

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

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

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

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

         # select random slice
         UpperCAmelCase_			       =  ids_tensor((1,)  ,     output_from_past.shape[-1]					).item()
         UpperCAmelCase_			       =  output_from_no_past[:, -1, random_slice_idx].detach()
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Dict  ,     __a       :       Tuple  ,     __a       :       List[str]  ,     __a       :       Optional[Any]  ,     __a       :       str  ,     __a       :       Any  ,     *__a       :       Optional[int]					):
         UpperCAmelCase_			       =  BioGptModel(config=__a					).to(__a					).eval()

         UpperCAmelCase_			       =  torch.ones(input_ids.shape  ,     dtype=torch.long  ,     device=__a					)

         # first forward pass
         UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a  ,     use_cache=__a					)

         UpperCAmelCase_       ,					UpperCAmelCase_			       =  outputs.to_tuple()

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

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

         UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a					)["last_hidden_state"]
         UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a  ,     past_key_values=__a					)[
             "last_hidden_state"
         ]

         # select random slice
         UpperCAmelCase_			       =  ids_tensor((1,)  ,     output_from_past.shape[-1]					).item()
         UpperCAmelCase_			       =  output_from_no_past[:, -3:, random_slice_idx].detach()
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Optional[Any]  ,     __a       :       Optional[Any]  ,     __a       :       Optional[int]  ,     __a       :       Optional[int]  ,     __a       :       Tuple  ,     __a       :       Tuple  ,     *__a       :       List[Any]  ,     __a       :       Optional[int]=False					):
         UpperCAmelCase_			       =  BioGptForCausalLM(__a					)
         model.to(__a					)
         if gradient_checkpointing:
              model.gradient_checkpointing_enable()

         UpperCAmelCase_			       =  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    _lowercase		(self       :       Tuple  ,     __a       :       int  ,     *__a       :       int					):
         UpperCAmelCase_			       =  BioGptModel(__a					)
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Any  ,     __a       :       str  ,     __a       :       List[str]  ,     __a       :       Tuple  ,     __a       :       Any  ,     __a       :       List[Any]  ,     *__a       :       Tuple					):
         UpperCAmelCase_			       =  self.num_labels
         UpperCAmelCase_			       =  BioGptForTokenClassification(__a					)
         model.to(__a					)
         model.eval()
         UpperCAmelCase_			       =  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    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					
         )			       =  config_and_inputs
         UpperCAmelCase_			       =  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		List[str]			  =    (
        (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
        if is_torch_available()
        else ()
    )
    a__			:		Optional[int]			  =    (BioGptForCausalLM,) if is_torch_available() else ()
    a__			:		int			  =    (
        {
            """feature-extraction""": BioGptModel,
            """text-classification""": BioGptForSequenceClassification,
            """text-generation""": BioGptForCausalLM,
            """token-classification""": BioGptForTokenClassification,
            """zero-shot""": BioGptForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__			:		Union[str, Any]			  =    False



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  BioGptModelTester(self					)
         UpperCAmelCase_			       =  ConfigTester(self  ,     config_class=__a  ,     hidden_size=37					)



    def    _lowercase		(self       :       Tuple					):
         self.config_tester.run_common_tests()



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_			       =  type
              self.model_tester.create_and_check_model(*__a					)



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a					)



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*__a  ,     gradient_checkpointing=__a					)



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a					)



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_weight_initialization(*__a					)



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_for_token_classification(*__a					)



    @slow
    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  BioGptForCausalLM.from_pretrained("microsoft/biogpt"					)
         model.to(__a					)
         UpperCAmelCase_			       =  BioGptTokenizer.from_pretrained("microsoft/biogpt"					)

         UpperCAmelCase_			       =  "left"

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

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

         UpperCAmelCase_			       =  tokenizer(__a  ,     return_tensors="pt"  ,     padding=__a					)
         UpperCAmelCase_			       =  inputs["input_ids"].to(__a					)

         UpperCAmelCase_			       =  model.generate(
             input_ids=__a  ,     attention_mask=inputs["attention_mask"].to(__a					)  ,     )

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

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

         UpperCAmelCase_			       =  tokenizer.batch_decode(__a  ,     skip_special_tokens=__a					)
         UpperCAmelCase_			       =  tokenizer.decode(output_non_padded[0]  ,     skip_special_tokens=__a					)
         UpperCAmelCase_			       =  tokenizer.decode(output_padded[0]  ,     skip_special_tokens=__a					)

         UpperCAmelCase_			       =  [
             "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    _lowercase		(self       :       Dict					):
         for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_			       =  BioGptModel.from_pretrained(__a					)
              self.assertIsNotNone(__a					)



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






    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_			       =  3
         UpperCAmelCase_			       =  "multi_label_classification"
         UpperCAmelCase_			       =  input_dict["input_ids"]
         UpperCAmelCase_			       =  input_ids.ne(1					).to(__a					)
         UpperCAmelCase_			       =  ids_tensor(
             [self.model_tester.batch_size, config.num_labels]  ,     self.model_tester.type_sequence_label_size					).to(torch.float					)
         UpperCAmelCase_			       =  BioGptForSequenceClassification(__a					)
         model.to(__a					)
         model.eval()
         UpperCAmelCase_			       =  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 			__A (					unittest.TestCase      ):



    @slow
    def    _lowercase		(self       :       List[Any]					):
         UpperCAmelCase_			       =  BioGptForCausalLM.from_pretrained("microsoft/biogpt"					)
         UpperCAmelCase_			       =  torch.tensor([[2, 4805, 9, 656, 21]]					)
         UpperCAmelCase_			       =  model(__a					)[0]

         UpperCAmelCase_			       =  42384

         UpperCAmelCase_			       =  torch.Size((1, 5, vocab_size)					)
         self.assertEqual(output.shape  ,     __a					)

         UpperCAmelCase_			       =  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    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  BioGptTokenizer.from_pretrained("microsoft/biogpt"					)
         UpperCAmelCase_			       =  BioGptForCausalLM.from_pretrained("microsoft/biogpt"					)
         model.to(__a					)

         torch.manual_seed(0					)
         UpperCAmelCase_			       =  tokenizer("COVID-19 is"  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  model.generate(
             **__a  ,     min_length=100  ,     max_length=1024  ,     num_beams=5  ,     early_stopping=__a  ,     )
         UpperCAmelCase_			       =  tokenizer.decode(output_ids[0]  ,     skip_special_tokens=__a					)

         UpperCAmelCase_			       =  (
             "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      UpperCamelCase		:






						def __init__(		self  :Union[str, Any]			, __magic_name__  :Any			, __magic_name__  :List[str]=13			, __magic_name__  :Any=3			, __magic_name__  :str=True			, __magic_name__  :int=True			, __magic_name__  :Dict=0.1			, __magic_name__  :Optional[int]=0.1			, __magic_name__  :List[str]=224			, __magic_name__  :Any=1_000			, __magic_name__  :int=[3, 3, 6, 4]			, __magic_name__  :int=[48, 56, 112, 220]			, )      ->int:
										lowercase    :      Dict       = parent
										lowercase    :      List[str]       = batch_size
										lowercase    :      Union[str, Any]       = num_channels
										lowercase    :      Dict       = is_training
										lowercase    :      Any       = use_labels
										lowercase    :      Dict       = hidden_dropout_prob
										lowercase    :      Union[str, Any]       = attention_probs_dropout_prob
										lowercase    :      Optional[Any]       = num_labels
										lowercase    :      Optional[Any]       = image_size
										lowercase    :      Optional[int]       = layer_depths
										lowercase    :      Optional[int]       = embed_dims






						def      __snake_case      (		self  :Tuple				)      ->str:
										lowercase    :      Dict       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										lowercase    :      Optional[Any]       = None
										if self.use_labels:
														lowercase    :      str       = ids_tensor([self.batch_size]			, self.num_labels				)

										lowercase    :      List[Any]       = self.get_config()

										return config, pixel_values, labels






						def      __snake_case      (		self  :Dict				)      ->Union[str, Any]:
										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=__magic_name__			, layer_scale_init_value=1E-5			, )






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Union[str, Any]			, __magic_name__  :Dict			, __magic_name__  :Dict				)      ->Optional[Any]:
										lowercase    :      Optional[int]       = SwiftFormerModel(config=__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.embed_dims[-1], 7, 7)				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any			, __magic_name__  :Tuple			, __magic_name__  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = self.num_labels
										lowercase    :      List[str]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      List[Any]       = model(__magic_name__			, labels=__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)

										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()

										lowercase    :      Tuple       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										((lowercase)      ,      (lowercase)      ,      (lowercase))    :      Optional[Any]       = self.prepare_config_and_inputs()
										lowercase    :      Any       = {"""pixel_values""": pixel_values}
										return config, inputs_dict


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

						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : List[str]												=		False
						_SCREAMING_SNAKE_CASE  : Tuple												=		False
						_SCREAMING_SNAKE_CASE  : List[Any]												=		False






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = SwiftFormerModelTester(self				)
										lowercase    :      List[str]       = ConfigTester(
										    self			, config_class=__magic_name__			, has_text_modality=__magic_name__			, hidden_size=37			, num_attention_heads=12			, num_hidden_layers=12			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Union[str, Any]:
										self.config_tester.run_common_tests()






						@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""				)
						def      __snake_case      (		self  :Any				)      ->Any:
										pass






						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Dict       = model_class(__magic_name__				)
														lowercase    :      int       = model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__magic_name__			, nn.Linear				)				)






						def      __snake_case      (		self  :Any				)      ->List[str]:
										lowercase      ,      lowercase    :      int       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Union[str, Any]       = model_class(__magic_name__				)
														lowercase    :      Tuple       = inspect.signature(model.forward				)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowercase    :      str       = [*signature.parameters.keys()]

														lowercase    :      Any       = ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]			, __magic_name__				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->List[str]:
										lowercase    :      Any       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__magic_name__				)






						@slow
						def      __snake_case      (		self  :Any				)      ->Dict:
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase    :      List[Any]       = SwiftFormerModel.from_pretrained(__magic_name__				)
														self.assertIsNotNone(__magic_name__				)






						@unittest.skip(reason="""SwiftFormer does not output attentions"""				)
						def      __snake_case      (		self  :List[str]				)      ->Optional[Any]:
										pass






						def      __snake_case      (		self  :List[str]				)      ->List[str]:
										def check_hidden_states_output(__magic_name__  :str			, __magic_name__  :Optional[Any]			, __magic_name__  :Tuple				):
														lowercase    :      Optional[Any]       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()

														with torch.no_grad():
																		lowercase    :      Dict       = model(**self._prepare_for_class(__magic_name__			, __magic_name__				)				)

														lowercase    :      Tuple       = outputs.hidden_states

														lowercase    :      List[str]       = 8
														self.assertEqual(len(__magic_name__				)			, __magic_name__				)  # 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(__magic_name__				)				):
																		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),
																		        ]				)			, )

										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Optional[int]       = True
														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)

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

														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										def _config_zero_init(__magic_name__  :List[Any]				):
														lowercase    :      Tuple       = copy.deepcopy(__magic_name__				)
														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(__magic_name__			, __magic_name__			, 1E-10				)
																		if isinstance(getattr(__magic_name__			, __magic_name__			, __magic_name__				)			, __magic_name__				):
																						lowercase    :      Any       = _config_zero_init(getattr(__magic_name__			, __magic_name__				)				)
																						setattr(__magic_name__			, __magic_name__			, __magic_name__				)
														return configs_no_init

										lowercase      ,      lowercase    :      Dict       = self.model_tester.prepare_config_and_inputs_for_common()

										lowercase    :      int       = _config_zero_init(__magic_name__				)
										for model_class in self.all_model_classes:
														lowercase    :      Union[str, Any]       = model_class(config=__magic_name__				)
														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      __snake_case      (		self  :Any				)      ->Any:
										pass


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      int       = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image


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






						@cached_property
						def      __snake_case      (		self  :Optional[Any]				)      ->List[str]:
										return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""				) if is_vision_available() else None






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""				).to(__magic_name__				)

										lowercase    :      Any       = self.default_image_processor
										lowercase    :      str       = prepare_img()
										lowercase    :      Tuple       = image_processor(images=__magic_name__			, return_tensors="""pt"""				).to(__magic_name__				)

										# forward pass
										with torch.no_grad():
														lowercase    :      Union[str, Any]       = model(**__magic_name__				)

										# verify the logits
										lowercase    :      Optional[int]       = torch.Size((1, 1_000)				)
										self.assertEqual(outputs.logits.shape			, __magic_name__				)

										lowercase    :      Optional[Any]       = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]				).to(__magic_name__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			, __magic_name__			, atol=1E-4				)				)
