



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   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=224     ,lowercase=1000     ,lowercase=[3, 3, 6, 4]     ,lowercase=[48, 56, 112, 220]     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		parent
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	Union[str, Any]				      =		num_channels
             UpperCAmelCase_  :	Tuple				      =		is_training
             UpperCAmelCase_  :	List[Any]				      =		use_labels
             UpperCAmelCase_  :	List[Any]				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Any				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Union[str, Any]				      =		num_labels
             UpperCAmelCase_  :	Any				      =		image_size
             UpperCAmelCase_  :	Optional[int]				      =		layer_depths
             UpperCAmelCase_  :	int				      =		embed_dims



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

             UpperCAmelCase_  :	int				      =		self.get_config()

             return config, pixel_values, labels



       def  A_   (     self):

             """simple docstring"""





             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=lowercase     ,layer_scale_init_value=1E-5     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		SwiftFormerModel(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Any				      =		model(lowercase)
             self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.embed_dims[-1], 7, 7))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self.num_labels
             UpperCAmelCase_  :	List[str]				      =		SwiftFormerForImageClassification(lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	List[Any]				      =		model(lowercase     ,labels=lowercase)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))

             UpperCAmelCase_  :	Union[str, Any]				      =		SwiftFormerForImageClassification(lowercase)
             model.to(lowercase)
             model.eval()

             UpperCAmelCase_  :	Union[str, Any]				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Any				      =		model(lowercase)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))



       def  A_   (     self):

             """simple docstring"""





             ((UpperCAmelCase_)      ,      (UpperCAmelCase_)      ,      (UpperCAmelCase_))  :	str				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_  :	Dict				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
class   snake_case_ (lowercase__ , lowercase__ , 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  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		SwiftFormerModelTester(self)
             UpperCAmelCase_  :	Dict				      =		ConfigTester(
                 self     ,config_class=lowercase     ,has_text_modality=lowercase     ,hidden_size=37     ,num_attention_heads=12     ,num_hidden_layers=12     ,)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.run_common_tests()



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

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





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

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_  :	Tuple				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_  :	Tuple				      =		SwiftFormerModel.from_pretrained(lowercase)
                   self.assertIsNotNone(lowercase)



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

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             def check_hidden_states_output(lowercase     ,lowercase     ,lowercase):
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()

                   with torch.no_grad():
                         UpperCAmelCase_  :	Tuple				      =		model(**self._prepare_for_class(lowercase     ,lowercase))

                   UpperCAmelCase_  :	str				      =		outputs.hidden_states

                   UpperCAmelCase_  :	Optional[Any]				      =		8
                   self.assertEqual(len(lowercase)     ,lowercase)  # 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(lowercase)):
                         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_  :	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(lowercase     ,lowercase     ,lowercase)

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

                   check_hidden_states_output(lowercase     ,lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             def _config_zero_init(lowercase):
                   UpperCAmelCase_  :	List[Any]				      =		copy.deepcopy(lowercase)
                   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(lowercase     ,lowercase     ,1E-10)
                         if isinstance(getattr(lowercase     ,lowercase     ,lowercase)     ,lowercase):
                               UpperCAmelCase_  :	List[str]				      =		_config_zero_init(getattr(lowercase     ,lowercase))
                               setattr(lowercase     ,lowercase     ,lowercase)
                   return configs_no_init

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

             UpperCAmelCase_  :	Optional[Any]				      =		_config_zero_init(lowercase)
             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	List[str]				      =		model_class(config=lowercase)
                   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  A_   (     self):

             """simple docstring"""





             pass







def    _snake_case (       )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
      return image





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




       """simple docstring"""






       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(lowercase)

             UpperCAmelCase_  :	Any				      =		self.default_image_processor
             UpperCAmelCase_  :	Dict				      =		prepare_img()
             UpperCAmelCase_  :	List[str]				      =		image_processor(images=lowercase     ,return_tensors="pt").to(lowercase)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_  :	List[str]				      =		model(**lowercase)

             # verify the logits
             UpperCAmelCase_  :	str				      =		torch.Size((1, 1000))
             self.assertEqual(outputs.logits.shape     ,lowercase)

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""trocr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }



       def __init__(     self     ,lowercase=50265     ,lowercase=1024     ,lowercase=12     ,lowercase=16     ,lowercase=4096     ,lowercase="gelu"     ,lowercase=512     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=2     ,lowercase=0.02     ,lowercase=0.0     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=True     ,lowercase=1     ,lowercase=0     ,lowercase=2     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		vocab_size
             UpperCAmelCase_  :	Tuple				      =		d_model
             UpperCAmelCase_  :	List[str]				      =		decoder_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		decoder_attention_heads
             UpperCAmelCase_  :	Optional[Any]				      =		decoder_ffn_dim
             UpperCAmelCase_  :	Tuple				      =		activation_function
             UpperCAmelCase_  :	List[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	List[Any]				      =		dropout
             UpperCAmelCase_  :	List[str]				      =		attention_dropout
             UpperCAmelCase_  :	Union[str, Any]				      =		activation_dropout
             UpperCAmelCase_  :	Optional[int]				      =		init_std
             UpperCAmelCase_  :	Dict				      =		decoder_layerdrop
             UpperCAmelCase_  :	List[str]				      =		use_cache
             UpperCAmelCase_  :	Dict				      =		scale_embedding
             UpperCAmelCase_  :	Dict				      =		use_learned_position_embeddings
             UpperCAmelCase_  :	Optional[Any]				      =		layernorm_embedding

             super().__init__(
                 pad_token_id=lowercase     ,bos_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
