





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     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=13,__lowerCamelCase=3,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=224,__lowerCamelCase=1000,__lowerCamelCase=[3, 3, 6, 4],__lowerCamelCase=[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 				UpperCamelCase			(							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 				UpperCamelCase			(							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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						SwiftFormerModel(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase )
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.embed_dims[-1], 7, 7) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						SwiftFormerForImageClassification(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase,labels=__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )

        A__											=						SwiftFormerForImageClassification(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

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




 def 				UpperCamelCase			(							self ):
        ((A__)					,	(A__)					,	(A__))											=						self.prepare_config_and_inputs()
        A__											=						{'''pixel_values''': pixel_values}
        return config, inputs_dict




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __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 				UpperCamelCase			(							self ):
        A__											=						SwiftFormerModelTester(self )
        A__											=						ConfigTester(
            self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase,hidden_size=37,num_attention_heads=12,num_hidden_layers=12,)



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



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						model_class(__lowerCamelCase )
               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],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__											=						SwiftFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
               A__											=						model_class(__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               with torch.no_grad():
                      A__											=						model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )

               A__											=						outputs.hidden_states

               A__											=						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),
                              ] ),)

        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(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        def _config_zero_init(__lowerCamelCase ):
               A__											=						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 ):
                             A__											=						_config_zero_init(getattr(__lowerCamelCase,__lowerCamelCase ) )
                             setattr(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
               return configs_no_init

        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        A__											=						_config_zero_init(__lowerCamelCase )
        for model_class in self.all_model_classes:
               A__											=						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 				UpperCamelCase			(							self ):
        pass


def 	UpperCamelCase__(       )->Tuple:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




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



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




 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(__lowerCamelCase )

        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(images=__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )

        # forward pass
        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

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

        A__											=						torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(outputs.logits[0, :3],__lowerCamelCase,atol=1E-4 ) )












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


a__:      Dict									=		logging.get_logger(__name__)

a__:      str									=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__:      Any									=		{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

a__:      Any									=		{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


a__:      Optional[Any]									=		{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	DistilBertTokenizer



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
            or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
        ):
               A__											=						getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
               A__											=						do_lower_case
               A__											=						strip_accents
               A__											=						tokenize_chinese_chars
               A__											=						normalizer_class(**__lowerCamelCase )

        A__											=						do_lower_case



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        A__											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
        return tuple(__lowerCamelCase )





