





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__			:



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]=13						,lowerCamelCase__						:   Optional[int]=3						,lowerCamelCase__						:   Tuple=True						,lowerCamelCase__						:   Optional[Any]=True						,lowerCamelCase__						:   List[Any]=0.1						,lowerCamelCase__						:   int=0.1						,lowerCamelCase__						:   int=224						,lowerCamelCase__						:   str=1000						,lowerCamelCase__						:   int=[3, 3, 6, 4]						,lowerCamelCase__						:   List[str]=[48, 56, 112, 220]						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						layer_depths
           SCREAMING_SNAKE_CASE		=						embed_dims






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           SCREAMING_SNAKE_CASE		=						None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size]						,self.num_labels	)

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, pixel_values, labels






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[Any]:




           '''simple docstring'''


           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=lowerCamelCase__						,layer_scale_init_value=1e-5						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						SwiftFormerModel(config=lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.embed_dims[-1], 7, 7)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						SwiftFormerForImageClassification(lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,labels=lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)

           SCREAMING_SNAKE_CASE		=						SwiftFormerForImageClassification(lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()

           SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


           ((SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE))		=						self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_torch
class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     str		     =      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     __snake_case   :     Union[str, Any]		     =      (
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     __snake_case   :     int		     =      False
     __snake_case   :     int		     =      False
     __snake_case   :     str		     =      False
     __snake_case   :     Optional[int]		     =      False
     __snake_case   :     Optional[int]		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						SwiftFormerModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(
               self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__						,hidden_size=37						,num_attention_heads=12						,num_hidden_layers=12						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           self.config_tester.run_common_tests()






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




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCamelCase__						,nn.Linear	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Union[str, Any]:




           '''simple docstring'''


           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE		=						SwiftFormerModel.from_pretrained(lowerCamelCase__	)
                 self.assertIsNotNone(lowerCamelCase__	)






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




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Tuple	):
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)
                 model.to(lowerCamelCase__	)
                 model.eval()

                 with torch.no_grad():
                       SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

                 SCREAMING_SNAKE_CASE		=						outputs.hidden_states

                 SCREAMING_SNAKE_CASE		=						8
                 self.assertEqual(len(lowerCamelCase__	)						,lowerCamelCase__	)  # 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(lowerCamelCase__	)	):
                       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),
                               ]	)						,)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						True
                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE		=						True

                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






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




           '''simple docstring'''


           def _config_zero_init(lowerCamelCase__						:   int	):
                 SCREAMING_SNAKE_CASE		=						copy.deepcopy(lowerCamelCase__	)
                 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(lowerCamelCase__						,lowerCamelCase__						,1e-1_0	)
                       if isinstance(getattr(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)						,lowerCamelCase__	):
                             SCREAMING_SNAKE_CASE		=						_config_zero_init(getattr(lowerCamelCase__						,lowerCamelCase__	)	)
                             setattr(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                 return configs_no_init

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE		=						_config_zero_init(lowerCamelCase__	)
           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						model_class(config=lowerCamelCase__	)
                 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						:   Tuple	)					->		Any:




           '''simple docstring'''


           pass



def        __lowercase       (				)			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image



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



     '''simple docstring'''

     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Any:




           '''simple docstring'''


           return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""	) if is_vision_available() else None






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""	).to(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.default_image_processor
           SCREAMING_SNAKE_CASE		=						prepare_img()
           SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""pt"""	).to(lowerCamelCase__	)

           # forward pass
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

           # verify the logits
           SCREAMING_SNAKE_CASE		=						torch.Size((1, 1000)	)
           self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]	).to(lowerCamelCase__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]						,lowerCamelCase__						,atol=1e-4	)	)









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						       =	TypeVar("""DatasetType""", Dataset, IterableDataset)



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "first_exhausted"			,						)			->       DatasetType:


      '''simple docstring'''


      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError("""Unable to interleave an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )
      else:
            return _interleave_iterable_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 0			,						)			->       DatasetType:


      '''simple docstring'''


      if not dsets:
            raise ValueError("""Unable to concatenate an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )
      else:
            return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )


