



import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor



class        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase ):





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.config_class(**self.inputs_dict)
       self.parent.assertTrue(hasattr(UpperCAmelCase    ,  '''width_multiplier'''))







class        _SCREAMING_SNAKE_CASE		:





   def __init__(self    ,  UpperCAmelCase    ,  UpperCAmelCase=1_3    ,  UpperCAmelCase=6_4    ,  UpperCAmelCase=2    ,  UpperCAmelCase=3    ,  UpperCAmelCase="swish"    ,  UpperCAmelCase=3    ,  UpperCAmelCase=3_2    ,  UpperCAmelCase=0.1    ,  UpperCAmelCase=0.02    ,  UpperCAmelCase=True    ,  UpperCAmelCase=True    ,  UpperCAmelCase=1_0    ,  UpperCAmelCase=None    ,  UpperCAmelCase=0.25    ,  UpperCAmelCase=0.0    ,  UpperCAmelCase=0.0    ,  ):
       '''simple docstring'''







       __UpperCAmelCase			 =parent
       __UpperCAmelCase			 =batch_size
       __UpperCAmelCase			 =image_size
       __UpperCAmelCase			 =patch_size
       __UpperCAmelCase			 =num_channels
       __UpperCAmelCase			 =make_divisible(5_1_2 * width_multiplier    ,  divisor=8)
       __UpperCAmelCase			 =hidden_act
       __UpperCAmelCase			 =conv_kernel_size
       __UpperCAmelCase			 =output_stride
       __UpperCAmelCase			 =classifier_dropout_prob
       __UpperCAmelCase			 =use_labels
       __UpperCAmelCase			 =is_training
       __UpperCAmelCase			 =num_labels
       __UpperCAmelCase			 =initializer_range
       __UpperCAmelCase			 =scope
       __UpperCAmelCase			 =width_multiplier
       __UpperCAmelCase			 =ffn_dropout
       __UpperCAmelCase			 =attn_dropout





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

       __UpperCAmelCase			 =None
       __UpperCAmelCase			 =None
       if self.use_labels:
           __UpperCAmelCase			 =ids_tensor([self.batch_size]    ,  self.num_labels)
           __UpperCAmelCase			 =ids_tensor([self.batch_size, self.image_size, self.image_size]    ,  self.num_labels)

       __UpperCAmelCase			 =self.get_config()

       return config, pixel_values, labels, pixel_labels





   def 	A__      (self):
       '''simple docstring'''







       return MobileViTVaConfig(
           image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  hidden_act=self.hidden_act    ,  conv_kernel_size=self.conv_kernel_size    ,  output_stride=self.output_stride    ,  classifier_dropout_prob=self.classifier_dropout_prob    ,  initializer_range=self.initializer_range    ,  width_multiplier=self.width_multiplier    ,  ffn_dropout=self.ffn_dropout_prob    ,  attn_dropout=self.attn_dropout_prob    ,  )





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =MobileViTVaModel(config=UpperCAmelCase)
       model.to(UpperCAmelCase)
       model.eval()
       __UpperCAmelCase			 =model(UpperCAmelCase)
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,  (
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )    ,  )





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =self.num_labels
       __UpperCAmelCase			 =MobileViTVaForImageClassification(UpperCAmelCase)
       model.to(UpperCAmelCase)
       model.eval()
       __UpperCAmelCase			 =model(UpperCAmelCase    ,  labels=UpperCAmelCase)
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =self.num_labels
       __UpperCAmelCase			 =MobileViTVaForSemanticSegmentation(UpperCAmelCase)
       model.to(UpperCAmelCase)
       model.eval()
       __UpperCAmelCase			 =model(UpperCAmelCase)
       self.parent.assertEqual(
           result.logits.shape    ,  (
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )    ,  )
       __UpperCAmelCase			 =model(UpperCAmelCase    ,  labels=UpperCAmelCase)
       self.parent.assertEqual(
           result.logits.shape    ,  (
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )    ,  )






   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.prepare_config_and_inputs()
       __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase			 =config_and_inputs
       __UpperCAmelCase			 ={'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase			,			_lowerCAmelCase			,			unittest.TestCase ):
   a_							:      Optional[int]						= (
       (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )

   a_							:      Any						= (
       {
           '''feature-extraction''': MobileViTVaModel,
           '''image-classification''': MobileViTVaForImageClassification,
           '''image-segmentation''': MobileViTVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   a_							:      Dict						= False
   a_							:      Any						= False
   a_							:      int						= False
   a_							:      Optional[Any]						= False





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =MobileViTVaModelTester(self)
       __UpperCAmelCase			 =MobileViTVaConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase)





   def 	A__      (self):
       '''simple docstring'''







       self.config_tester.run_common_tests()





   @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''')
   def 	A__      (self):
       '''simple docstring'''







       pass





   @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''')
   def 	A__      (self):
       '''simple docstring'''







       pass





   @unittest.skip(reason='''MobileViTV2 does not output attentions''')
   def 	A__      (self):
       '''simple docstring'''







       pass





   @require_torch_multi_gpu
   @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''')
   def 	A__      (self):
       '''simple docstring'''







       pass





   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
   def 	A__      (self):
       '''simple docstring'''







       pass





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase , __UpperCAmelCase			 =self.model_tester.prepare_config_and_inputs_for_common()

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

           __UpperCAmelCase			 =['''pixel_values''']
           self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase)





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase)





   def 	A__      (self):
       '''simple docstring'''







       def check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
           __UpperCAmelCase			 =model_class(UpperCAmelCase)
           model.to(UpperCAmelCase)
           model.eval()

           with torch.no_grad():
               __UpperCAmelCase			 =model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase))

           __UpperCAmelCase			 =outputs.hidden_states

           __UpperCAmelCase			 =5
           self.assertEqual(len(UpperCAmelCase)    ,  UpperCAmelCase)

           # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           __UpperCAmelCase			 =2
           for i in range(len(UpperCAmelCase)):
               self.assertListEqual(
                   list(hidden_states[i].shape[-2:])    ,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,  )
               divisor *= 2

           self.assertEqual(self.model_tester.output_stride    ,  divisor // 2)

       __UpperCAmelCase , __UpperCAmelCase			 =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           __UpperCAmelCase			 =True
           check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase)

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

           check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase)





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase)






   @slow
   def 	A__      (self):
       '''simple docstring'''







       for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase			 =MobileViTVaModel.from_pretrained(UpperCAmelCase)
           self.assertIsNotNone(UpperCAmelCase)







def 		SCREAMING_SNAKE_CASE     (     )   ->	List[Any]:
    __UpperCAmelCase			 =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    return image



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





   @cached_property
   def 	A__      (self):
       '''simple docstring'''







       return (
           MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''')
           if is_vision_available()
           else None
       )





   @slow
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''').to(
           UpperCAmelCase)

       __UpperCAmelCase			 =self.default_image_processor
       __UpperCAmelCase			 =prepare_img()
       __UpperCAmelCase			 =image_processor(images=UpperCAmelCase    ,  return_tensors='''pt''').to(UpperCAmelCase)

       # forward pass
       with torch.no_grad():
           __UpperCAmelCase			 =model(**UpperCAmelCase)

       # verify the logits
       __UpperCAmelCase			 =torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase)

       __UpperCAmelCase			 =torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(UpperCAmelCase)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  UpperCAmelCase    ,  atol=1e-4))





   @slow
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''')
       __UpperCAmelCase			 =model.to(UpperCAmelCase)

       __UpperCAmelCase			 =MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''')

       __UpperCAmelCase			 =prepare_img()
       __UpperCAmelCase			 =image_processor(images=UpperCAmelCase    ,  return_tensors='''pt''').to(UpperCAmelCase)

       # forward pass
       with torch.no_grad():
           __UpperCAmelCase			 =model(**UpperCAmelCase)
       __UpperCAmelCase			 =outputs.logits

       # verify the logits
       __UpperCAmelCase			 =torch.Size((1, 2_1, 3_2, 3_2))
       self.assertEqual(logits.shape    ,  UpperCAmelCase)

       __UpperCAmelCase			 =torch.tensor(
           [
               [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
               [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
               [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
           ]    ,  device=UpperCAmelCase    ,  )

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,  UpperCAmelCase    ,  atol=1e-4))






   @slow
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''')
       __UpperCAmelCase			 =model.to(UpperCAmelCase)

       __UpperCAmelCase			 =MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''')

       __UpperCAmelCase			 =prepare_img()
       __UpperCAmelCase			 =image_processor(images=UpperCAmelCase    ,  return_tensors='''pt''').to(UpperCAmelCase)

       # forward pass
       with torch.no_grad():
           __UpperCAmelCase			 =model(**UpperCAmelCase)

       __UpperCAmelCase			 =outputs.logits.detach().cpu()

       __UpperCAmelCase			 =image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase    ,  target_sizes=[(5_0, 6_0)])
       __UpperCAmelCase			 =torch.Size((5_0, 6_0))
       self.assertEqual(segmentation[0].shape    ,  UpperCAmelCase)

       __UpperCAmelCase			 =image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase)
       __UpperCAmelCase			 =torch.Size((3_2, 3_2))
       self.assertEqual(segmentation[0].shape    ,  UpperCAmelCase)











'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )