'''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 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_3     ,			_lowerCamelCase=3     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=2_2_4     ,			_lowerCamelCase=1_0_0_0     ,			_lowerCamelCase=[3, 3, 6, 4]     ,			_lowerCamelCase=[4_8, 5_6, 1_1_2, 2_2_0]     ,			):
         __UpperCamelCase	:	Optional[int]		    =  parent
         __UpperCamelCase	:	int		    =  batch_size
         __UpperCamelCase	:	Optional[Any]		    =  num_channels
         __UpperCamelCase	:	str		    =  is_training
         __UpperCamelCase	:	Optional[int]		    =  use_labels
         __UpperCamelCase	:	Optional[int]		    =  hidden_dropout_prob
         __UpperCamelCase	:	Union[str, Any]		    =  attention_probs_dropout_prob
         __UpperCamelCase	:	str		    =  num_labels
         __UpperCamelCase	:	Optional[Any]		    =  image_size
         __UpperCamelCase	:	str		    =  layer_depths
         __UpperCamelCase	:	Dict		    =  embed_dims







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

         __UpperCamelCase	:	int		    =  self.get_config()

         return config, pixel_values, labels







     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  SwiftFormerModel(config=_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	List[str]		    =  model(_lowerCamelCase    )
         self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.embed_dims[-1], 7, 7)    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  self.num_labels
         __UpperCamelCase	:	Dict		    =  SwiftFormerForImageClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	Optional[Any]		    =  model(_lowerCamelCase     ,			labels=_lowerCamelCase    )
         self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)    )

         __UpperCamelCase	:	int		    =  SwiftFormerForImageClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()

         __UpperCamelCase	:	List[str]		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
         __UpperCamelCase	:	Optional[int]		    =  model(_lowerCamelCase    )
         self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)    )







     def lowerCAmelCase     (		self    ):
         ((__UpperCamelCase)    ,					(__UpperCamelCase)    ,					(__UpperCamelCase))	:	Any		    =  self.prepare_config_and_inputs()
         __UpperCamelCase	:	Tuple		    =  {'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  SwiftFormerModelTester(self    )
         __UpperCamelCase	:	List[Any]		    =  ConfigTester(
             self     ,			config_class=_lowerCamelCase     ,			has_text_modality=_lowerCamelCase     ,			hidden_size=3_7     ,			num_attention_heads=1_2     ,			num_hidden_layers=1_2     ,			)







     def lowerCAmelCase     (		self    ):
         self.config_tester.run_common_tests()







     @unittest.skip(reason='SwiftFormer does not use inputs_embeds'    )
     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

             __UpperCamelCase	:	Union[str, Any]		    =  ['pixel_values']
             self.assertListEqual(arg_names[:1]     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase    )







     @slow
     def lowerCAmelCase     (		self    ):
         for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase	:	List[Any]		    =  SwiftFormerModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )







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







     def lowerCAmelCase     (		self    ):
         def check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  model_class(_lowerCamelCase    )
             model.to(_lowerCamelCase    )
             model.eval()

             with torch.no_grad():
                 __UpperCamelCase	:	Optional[int]		    =  model(**self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase    )    )

             __UpperCamelCase	:	Dict		    =  outputs.hidden_states

             __UpperCamelCase	:	str		    =  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),
                         ]    )     ,			)

         __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             __UpperCamelCase	:	Dict		    =  True
             check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

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

             check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         def _config_zero_init(_lowerCamelCase    ):
             __UpperCamelCase	:	Tuple		    =  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    ):
                     __UpperCamelCase	:	Tuple		    =  _config_zero_init(getattr(_lowerCamelCase     ,			_lowerCamelCase    )    )
                     setattr(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             return configs_no_init

         __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  self.model_tester.prepare_config_and_inputs_for_common()

         __UpperCamelCase	:	Any		    =  _config_zero_init(_lowerCamelCase    )
         for model_class in self.all_model_classes:
             __UpperCamelCase	:	Optional[Any]		    =  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 lowerCAmelCase     (		self    ):
         pass







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
    return image






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







     """simple docstring"""






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







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'    ).to(_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  self.default_image_processor
         __UpperCamelCase	:	int		    =  prepare_img()
         __UpperCamelCase	:	Any		    =  image_processor(images=_lowerCamelCase     ,			return_tensors='pt'    ).to(_lowerCamelCase    )

         # forward pass
         with torch.no_grad():
             __UpperCamelCase	:	Optional[int]		    =  model(**_lowerCamelCase    )

         # verify the logits
         __UpperCamelCase	:	str		    =  torch.Size((1, 1_0_0_0)    )
         self.assertEqual(outputs.logits.shape     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  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    )    )




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



