


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  ,       __A  ,       __A=13  ,       __A=3  ,       __A=True  ,       __A=True  ,       __A=0.1  ,       __A=0.1  ,       __A=224  ,       __A=1000  ,       __A=[3, 3, 6, 4]  ,       __A=[48, 56, 112, 220]  ,       ):
        __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 	snake_case_  (	self    ):
        __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 	snake_case_  (	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=__A  ,       layer_scale_init_value=1E-5  ,       )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  SwiftFormerModel(config=__A    )
        model.to(__A    )
        model.eval()
        __a					=  model(__A    )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.embed_dims[-1], 7, 7)    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  self.num_labels
        __a					=  SwiftFormerForImageClassification(__A    )
        model.to(__A    )
        model.eval()
        __a					=  model(__A  ,       labels=__A    )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels)    )

        __a					=  SwiftFormerForImageClassification(__A    )
        model.to(__A    )
        model.eval()

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


     def 	snake_case_  (	self    ):
        ((__a)	,    (__a)	,    (__a))					=  self.prepare_config_and_inputs()
        __a					=  {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     _lowerCamelCase   =      (
         {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        __a					=  SwiftFormerModelTester(self    )
        __a					=  ConfigTester(
            self  ,       config_class=__A  ,       has_text_modality=__A  ,       hidden_size=37  ,       num_attention_heads=12  ,       num_hidden_layers=12  ,       )


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


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


     def 	snake_case_  (	self    ):
        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

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


     def 	snake_case_  (	self    ):
        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __a					=  model_class(__A    )
           __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]  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A    )


     @slow
     def 	snake_case_  (	self    ):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __a					=  SwiftFormerModel.from_pretrained(__A    )
           self.assertIsNotNone(__A    )


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


     def 	snake_case_  (	self    ):
        def check_hidden_states_output(__A  ,       __A  ,       __A    ):
           __a					=  model_class(__A    )
           model.to(__A    )
           model.eval()

           with torch.no_grad():
              __a					=  model(**self._prepare_for_class(__A  ,       __A    )    )

           __a					=  outputs.hidden_states

           __a					=  8
           self.assertEqual(len(__A    )  ,       __A    )  # 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(__A    )    ):
              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(__A  ,       __A  ,       __A    )

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

           check_hidden_states_output(__A  ,       __A  ,       __A    )


     def 	snake_case_  (	self    ):
        def _config_zero_init(__A    ):
           __a					=  copy.deepcopy(__A    )
           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(__A  ,       __A  ,       1E-10    )
              if isinstance(getattr(__A  ,       __A  ,       __A    )  ,       __A    ):
                 __a					=  _config_zero_init(getattr(__A  ,       __A    )    )
                 setattr(__A  ,       __A  ,       __A    )
           return configs_no_init

        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

        __a					=  _config_zero_init(__A    )
        for model_class in self.all_model_classes:
           __a					=  model_class(config=__A    )
           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    ):
        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 	snake_case_  (	self    ):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""    ) if is_vision_available() else None


     @slow
     def 	snake_case_  (	self    ):
        __a					=  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""    ).to(__A    )

        __a					=  self.default_image_processor
        __a					=  prepare_img()
        __a					=  image_processor(images=__A  ,       return_tensors="""pt"""    ).to(__A    )

        # forward pass
        with torch.no_grad():
           __a					=  model(**__A    )

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

        __a					=  torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]    ).to(__A    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,       __A  ,       atol=1E-4    )    )




'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




