





import copy
import inspect
import unittest

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

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


if is_torch_available():
			import torch
			from torch import nn

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


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor


class       __lowerCAmelCase					:


	'''simple docstring'''





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

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      str      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

			lowercase__					:      str      =		None
			if self.use_labels:
					lowercase__					:      List[str]      =		ids_tensor([self.batch_size],					self.num_labels			)

			lowercase__					:      str      =		self.get_config()

			return config, pixel_values, labels

	def    snake_case__(						self:						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=lowerCamelCase_,					layer_scale_init_value=1E-5,					)

	def    snake_case__(						self:						int,					lowerCamelCase_:						str,					lowerCamelCase_:						str,					lowerCamelCase_:						Dict			):
			lowercase__					:      Tuple      =		SwiftFormerModel(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Dict      =		model(lowerCamelCase_			)
			self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.embed_dims[-1], 7, 7)			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any]			):
			lowercase__					:      str      =		self.num_labels
			lowercase__					:      Optional[Any]      =		SwiftFormerForImageClassification(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Dict      =		model(lowerCamelCase_,					labels=lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)			)

			lowercase__					:      List[str]      =		SwiftFormerForImageClassification(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()

			lowercase__					:      Any      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
			lowercase__					:      Optional[int]      =		model(lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)			)





	def    snake_case__(						self:						List[Any]			):
			((lowercase__)   ,				(lowercase__)   ,				(lowercase__))					:      Dict      =		self.prepare_config_and_inputs()
			lowercase__					:      List[Any]      =		{'pixel_values': pixel_values}
			return config, inputs_dict


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


	'''simple docstring'''





	_A													=				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	_A													=				(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A													=				False
	_A													=				False
	_A													=				False
	_A													=				False
	_A													=				False

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      int      =		SwiftFormerModelTester(self			)
			lowercase__					:      Union[str, Any]      =		ConfigTester(
			    self,					config_class=lowerCamelCase_,					has_text_modality=lowerCamelCase_,					hidden_size=37,					num_attention_heads=12,					num_hidden_layers=12,					)

	def    snake_case__(						self:						Optional[int]			):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='SwiftFormer does not use inputs_embeds'			)
	def    snake_case__(						self:						Optional[int]			):
			pass

	def    snake_case__(						self:						List[str]			):
			lowercase__   ,				lowercase__					:      Tuple      =		self.model_tester.prepare_config_and_inputs_for_common()

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

	def    snake_case__(						self:						int			):
			lowercase__   ,				lowercase__					:      Dict      =		self.model_tester.prepare_config_and_inputs_for_common()

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

					lowercase__					:      str      =		['pixel_values']
					self.assertListEqual(arg_names[:1],					lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      List[Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Optional[Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_			)

	@slow
	def    snake_case__(						self:						List[str]			):
			for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowercase__					:      Optional[int]      =		SwiftFormerModel.from_pretrained(lowerCamelCase_			)
					self.assertIsNotNone(lowerCamelCase_			)

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

	def    snake_case__(						self:						Dict			):
			def check_hidden_states_output(lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Optional[Any]			):
					lowercase__					:      str      =		model_class(lowerCamelCase_			)
					model.to(lowerCamelCase_			)
					model.eval()

					with torch.no_grad():
							lowercase__					:      Dict      =		model(**self._prepare_for_class(lowerCamelCase_,					lowerCamelCase_			)			)

					lowercase__					:      List[str]      =		outputs.hidden_states

					lowercase__					:      Any      =		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),
							        ]			),					)

			lowercase__   ,				lowercase__					:      List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowercase__					:      str      =		True
					check_hidden_states_output(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

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

					check_hidden_states_output(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			def _config_zero_init(lowerCamelCase_:						Union[str, Any]			):
					lowercase__					:      Optional[Any]      =		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-10			)
							if isinstance(getattr(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			),					lowerCamelCase_			):
									lowercase__					:      Union[str, Any]      =		_config_zero_init(getattr(lowerCamelCase_,					lowerCamelCase_			)			)
									setattr(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)
					return configs_no_init

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

			lowercase__					:      str      =		_config_zero_init(lowerCamelCase_			)
			for model_class in self.all_model_classes:
					lowercase__					:      int      =		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    snake_case__(						self:						str			):
			pass


def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image





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


	'''simple docstring'''





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





	@slow
	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'			).to(lowerCamelCase_			)

			lowercase__					:      Tuple      =		self.default_image_processor
			lowercase__					:      Optional[int]      =		prepare_img()
			lowercase__					:      Dict      =		image_processor(images=lowerCamelCase_,					return_tensors='pt'			).to(lowerCamelCase_			)

			# forward pass
			with torch.no_grad():
					lowercase__					:      str      =		model(**lowerCamelCase_			)

			# verify the logits
			lowercase__					:      Optional[Any]      =		torch.Size((1, 1000)			)
			self.assertEqual(outputs.logits.shape,					lowerCamelCase_			)

			lowercase__					:      Tuple      =		torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]			).to(lowerCamelCase_			)
			self.assertTrue(torch.allclose(outputs.logits[0, :3],					lowerCamelCase_,					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





