





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import LevitImageProcessor

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Optional[Any]=7						,lowerCamelCase__						:    Dict=3						,lowerCamelCase__						:    List[str]=18						,lowerCamelCase__						:    Tuple=30						,lowerCamelCase__						:    Optional[Any]=400						,lowerCamelCase__						:    int=True						,lowerCamelCase__						:    Tuple=None						,lowerCamelCase__						:    int=True						,lowerCamelCase__						:    Optional[int]=None						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Dict=[0.5, 0.5, 0.5]						,lowerCamelCase__						:    Dict=[0.5, 0.5, 0.5]						,)     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""shortest_edge""": 18}
           SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						min_resolution
           SCREAMING_SNAKE_CASE		=						max_resolution
           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           SCREAMING_SNAKE_CASE		=						do_center_crop
           SCREAMING_SNAKE_CASE		=						crop_size
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean
           SCREAMING_SNAKE_CASE		=						image_std






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		List[Any]:




           '''simple docstring'''


           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "do_center_crop": self.do_center_crop,
               "size": self.size,
               "crop_size": self.crop_size,
           }

@require_torch
@require_vision
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			LevitImageProcessor if is_vision_available() else None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						LevitImageProcessingTester(self       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		str:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_mean"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_std"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_normalize"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_resize"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_center_crop"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""size"""       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size						,{"""shortest_edge""": 18}       )
           self.assertEqual(image_processor.crop_size						,{"""height""": 18, """width""": 18}       )

           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict						,size=42						,crop_size=84       )
           self.assertEqual(image_processor.size						,{"""shortest_edge""": 42}       )
           self.assertEqual(image_processor.crop_size						,{"""height""": 84, """width""": 84}       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,Image.Image       )

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,numpify=lowerCamelCase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,np.ndarray       )

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,torchify=lowerCamelCase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,torch.Tensor       )

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )						,)









from pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
      fire.Fire(minify)


