



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 MobileNetVaImageProcessor

class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int=7     ,  UpperCamelCase :							int=3     ,  UpperCamelCase :							Dict=1_8     ,  UpperCamelCase :							Dict=3_0     ,  UpperCamelCase :							Tuple=4_0_0     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							Union[str, Any]=None     ,  UpperCamelCase :							Optional[Any]=True     ,  UpperCamelCase :							Optional[Any]=None     ,  )->Tuple:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	size if size is not None else {"shortest_edge": 2_0}
      __SCREAMING_SNAKE_CASE       :    str       =	crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	parent
      __SCREAMING_SNAKE_CASE       :    List[str]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Any       =	num_channels
      __SCREAMING_SNAKE_CASE       :    str       =	image_size
      __SCREAMING_SNAKE_CASE       :    Any       =	min_resolution
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	max_resolution
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_resize
      __SCREAMING_SNAKE_CASE       :    Tuple       =	size
      __SCREAMING_SNAKE_CASE       :    Any       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	crop_size






     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(__lowercase			,    unittest.TestCase ):
     lowerCAmelCase					=			MobileNetVaImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							Optional[Any]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	MobileNetVaImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							str				)->Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__A     ,  "do_resize"				)				)
      self.assertTrue(hasattr(__A     ,  "size"				)				)
      self.assertTrue(hasattr(__A     ,  "do_center_crop"				)				)
      self.assertTrue(hasattr(__A     ,  "crop_size"				)				)





     def 	__snake_case    (       self :							List[Any]				)->str:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.image_processing_class.from_dict(self.image_processor_dict				)
      self.assertEqual(image_processor.size     ,  {"shortest_edge": 2_0}				)
      self.assertEqual(image_processor.crop_size     ,  {"height": 1_8, "width": 1_8}				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processing_class.from_dict(self.image_processor_dict     ,  size=4_2     ,  crop_size=8_4				)
      self.assertEqual(image_processor.size     ,  {"shortest_edge": 4_2}				)
      self.assertEqual(image_processor.crop_size     ,  {"height": 8_4, "width": 8_4}				)





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





     def 	__snake_case    (       self :							List[Any]				)->int:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=__A				)
      for image in image_inputs:
       self.assertIsInstance(__A     ,  Image.Image				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    int       =	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       :    Tuple       =	image_processing(__A     ,  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 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    Dict       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=__A     ,  numpify=__A				)
      for image in image_inputs:
       self.assertIsInstance(__A     ,  np.ndarray				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    str       =	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       :    Union[str, Any]       =	image_processing(__A     ,  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 	__snake_case    (       self :							Dict				)->List[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=__A     ,  torchify=__A				)
      for image in image_inputs:
       self.assertIsInstance(__A     ,  torch.Tensor				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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       :    Optional[Any]       =	image_processing(__A     ,  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"],
          )     ,  )










import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






