



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		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1_3		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=2_2_4		,				SCREAMING_SNAKE_CASE=1_0_0_0		,				SCREAMING_SNAKE_CASE=[3, 3, 6, 4]		,				SCREAMING_SNAKE_CASE=[4_8, 5_6, 1_1_2, 2_2_0]		,				) ->			Dict:
  a__												= parent
  a__												= batch_size
  a__												= num_channels
  a__												= is_training
  a__												= use_labels
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= num_labels
  a__												= image_size
  a__												= layer_depths
  a__												= embed_dims

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

  a__												= self.get_config()

  return config, pixel_values, labels

 def 						_UpperCAmelCase       (	self      ) ->			Optional[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=SCREAMING_SNAKE_CASE		,				layer_scale_init_value=1e-5		,				)

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  a__												= SwiftFormerModel(config=SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.embed_dims[-1], 7, 7)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  a__												= self.num_labels
  a__												= SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)      )

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

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



 def 						_UpperCAmelCase       (	self      ) ->			Any:
  ((a__)							,			(a__)							,			(a__))												= self.prepare_config_and_inputs()
  a__												= {'''pixel_values''': pixel_values}
  return config, inputs_dict





@require_torch
class    __UpperCamelCase			(       _lowercase	,	_lowercase	,	unittest.TestCase				):

 """simple docstring"""



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

 _lowercase   :    Optional[int]      						=					False
 _lowercase   :    Tuple      						=					False
 _lowercase   :    Dict      						=					False
 _lowercase   :    Optional[Any]      						=					False
 _lowercase   :    Tuple      						=					False

 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= SwiftFormerModelTester(self      )
  a__												= ConfigTester(
      self		,				config_class=SCREAMING_SNAKE_CASE		,				has_text_modality=SCREAMING_SNAKE_CASE		,				hidden_size=3_7		,				num_attention_heads=1_2		,				num_hidden_layers=1_2		,				)

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  self.config_tester.run_common_tests()

 @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''      )
 def 						_UpperCAmelCase       (	self      ) ->			int:
  pass

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

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

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

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

   a__												= ['''pixel_values''']
   self.assertListEqual(arg_names[:1]		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE      )

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			int:
  for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   a__												= SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE      )
   self.assertIsNotNone(SCREAMING_SNAKE_CASE      )

 @unittest.skip(reason='''SwiftFormer does not output attentions'''      )
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  pass

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  def check_hidden_states_output(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= model_class(SCREAMING_SNAKE_CASE      )
   model.to(SCREAMING_SNAKE_CASE      )
   model.eval()

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

   a__												= outputs.hidden_states

   a__												= 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),
            ]      )		,				)

  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   a__												= 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"]
   a__												= True

   check_hidden_states_output(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  def _config_zero_init(SCREAMING_SNAKE_CASE      ):
   a__												= 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      ):
     a__												= _config_zero_init(getattr(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )
     setattr(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   return configs_no_init

  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  a__												= _config_zero_init(SCREAMING_SNAKE_CASE      )
  for model_class in self.all_model_classes:
   a__												= 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 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  pass






def     __a					(		):
 a__												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
 return image

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

 """simple docstring"""



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



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__												= SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(SCREAMING_SNAKE_CASE      )

  a__												= self.default_image_processor
  a__												= prepare_img()
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

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

  # verify the logits
  a__												= torch.Size((1, 1_0_0_0)      )
  self.assertEqual(outputs.logits.shape		,				SCREAMING_SNAKE_CASE      )

  a__												= 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      )      )










from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_      :      Optional[int]    =     logging.get_logger(__name__)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= feature_size
  a__												= sampling_rate
  a__												= padding_value

  a__												= kwargs.pop('''padding_side'''		,				'''right'''      )
  a__												= kwargs.pop('''return_attention_mask'''		,				SCREAMING_SNAKE_CASE      )

  super().__init__(**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = True		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			BatchFeature:
  # If we have a list of dicts, let's convert it in a dict of lists
  # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
  if isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ) and isinstance(processed_features[0]		,				(dict, BatchFeature)      ):
   a__												= {
       key: [example[key] for example in processed_features] for key in processed_features[0].keys()
   }

  # The model's main input name, usually `input_values`, has be passed for padding
  if self.model_input_names[0] not in processed_features:
   raise ValueError(
       '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
       f" to this method that includes {self.model_input_names[0]}, but you provided"
       f" {list(processed_features.keys()      )}"      )

  a__												= processed_features[self.model_input_names[0]]
  a__												= (
      return_attention_mask if return_attention_mask is not None else self.return_attention_mask
  )

  if len(SCREAMING_SNAKE_CASE      ) == 0:
   if return_attention_mask:
    a__												= []
   return processed_features

  # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
  # and rebuild them afterwards if no return_tensors is specified
  # Note that we lose the specific device the tensor may be on for PyTorch

  a__												= required_input[0]
  if isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ):
   # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
   a__												= 0
   while len(required_input[index]      ) == 0:
    index += 1
   if index < len(SCREAMING_SNAKE_CASE      ):
    a__												= required_input[index][0]

  if return_tensors is None:
   if is_tf_tensor(SCREAMING_SNAKE_CASE      ):
    a__												= '''tf'''
   elif is_torch_tensor(SCREAMING_SNAKE_CASE      ):
    a__												= '''pt'''
   elif isinstance(SCREAMING_SNAKE_CASE		,				(int, float, list, tuple, np.ndarray)      ):
    a__												= '''np'''
   else:
    raise ValueError(
        f"type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE      )}. "
        '''Should be one of a python, numpy, pytorch or tensorflow object.'''      )

  for key, value in processed_features.items():
   if isinstance(value[0]		,				(int, float)      ):
    a__												= to_numpy(SCREAMING_SNAKE_CASE      )
   else:
    a__												= [to_numpy(SCREAMING_SNAKE_CASE      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
  a__												= self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE      )

  a__												= processed_features[self.model_input_names[0]]

  a__												= len(SCREAMING_SNAKE_CASE      )
  if not all(len(SCREAMING_SNAKE_CASE      ) == batch_size for v in processed_features.values()      ):
   raise ValueError('''Some items in the output dictionary have a different batch size than others.'''      )

  a__												= []
  for i in range(SCREAMING_SNAKE_CASE      ):
   a__												= {k: v[i] for k, v in processed_features.items()}
   # truncation
   a__												= self._truncate(
       SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE		,				)
   truncated_inputs.append(SCREAMING_SNAKE_CASE      )

  if padding_strategy == PaddingStrategy.LONGEST:
   # make sure that `max_length` cannot be longer than the longest truncated length
   a__												= max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
   a__												= PaddingStrategy.MAX_LENGTH

  a__												= {}
  for i in range(SCREAMING_SNAKE_CASE      ):
   # padding
   a__												= self._pad(
       truncated_inputs[i]		,				max_length=SCREAMING_SNAKE_CASE		,				padding_strategy=SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE		,				return_attention_mask=SCREAMING_SNAKE_CASE		,				)

   for key, value in outputs.items():
    if key not in batch_outputs:
     a__												= []
    if value.dtype is np.dtype(np.floataa      ):
     a__												= value.astype(np.floataa      )
    batch_outputs[key].append(SCREAMING_SNAKE_CASE      )

  return BatchFeature(SCREAMING_SNAKE_CASE		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			dict:
  a__												= processed_features[self.model_input_names[0]]

  if padding_strategy == PaddingStrategy.LONGEST:
   a__												= len(SCREAMING_SNAKE_CASE      )

  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   a__												= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  a__												= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE      ) < max_length

  if return_attention_mask and "attention_mask" not in processed_features:
   a__												= np.ones(len(SCREAMING_SNAKE_CASE      )		,				dtype=np.intaa      )

  if needs_to_be_padded:
   a__												= max_length - len(SCREAMING_SNAKE_CASE      )
   if self.padding_side == "right":
    if return_attention_mask:
     a__												= np.pad(
         processed_features['''attention_mask''']		,				(0, difference)      )
    a__												= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
    a__												= np.pad(
        SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''constant'''		,				constant_values=self.padding_value      )
   elif self.padding_side == "left":
    if return_attention_mask:
     a__												= np.pad(
         processed_features['''attention_mask''']		,				(difference, 0)      )
    a__												= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
    a__												= np.pad(
        SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''constant'''		,				constant_values=self.padding_value      )
   else:
    raise ValueError('''Invalid padding strategy:''' + str(self.padding_side      )      )

  return processed_features

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			Optional[Any]:
  if not truncation:
   return processed_features
  elif truncation and max_length is None:
   raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''      )

  a__												= processed_features[self.model_input_names[0]]

  # find `max_length` that fits `pad_to_multiple_of`
  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   a__												= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  a__												= len(SCREAMING_SNAKE_CASE      ) > max_length

  if needs_to_be_truncated:
   a__												= processed_features[self.model_input_names[0]][:max_length]
   if "attention_mask" in processed_features:
    a__												= processed_features['''attention_mask'''][:max_length]

  return processed_features



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=None      ) ->			Optional[Any]:

  # Get padding strategy
  if padding is not False:
   if padding is True:
    a__												= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
   elif not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    a__												= PaddingStrategy(SCREAMING_SNAKE_CASE      )
   elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    a__												= padding
  else:
   a__												= PaddingStrategy.DO_NOT_PAD

  # Set max length if needed
  if max_length is None:
   if padding_strategy == PaddingStrategy.MAX_LENGTH:
    raise ValueError(
        f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
  if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
   raise ValueError(
       '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
       ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''      )

  return padding_strategy





