



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 EfficientNetImageProcessor



class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=13						,			lowerCamelCase_=3						,			lowerCamelCase_=18						,			lowerCamelCase_=30						,			lowerCamelCase_=400						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			lowerCamelCase_=True						,			lowerCamelCase_=[0.5, 0.5, 0.5]						,			lowerCamelCase_=[0.5, 0.5, 0.5]						,			):
						"""simple docstring"""

						a   	=  size if size is not None else {'height': 18, 'width': 18}
						a   	=  parent
						a   	=  batch_size
						a   	=  num_channels
						a   	=  image_size
						a   	=  min_resolution
						a   	=  max_resolution
						a   	=  do_resize
						a   	=  size
						a   	=  do_normalize
						a   	=  image_mean
						a   	=  image_std




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




@require_torch
@require_vision
class       _lowercase				(  lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				EfficientNetImageProcessor if is_vision_available() else None




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  EfficientNetImageProcessorTester(self						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.image_processor_tester.prepare_image_processor_dict()




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  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_						,			"size"						)						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

						a   	=  self.image_processing_class.from_dict(self.image_processor_dict						,			size=42						)
						self.assertEqual(image_processor.size						,			{"height": 42, "width": 42}						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

						# Test not batched input
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)

						# Test batched
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						a   	=  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
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)

						# Test batched
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						a   	=  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
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)

						# Test batched
						a   	=  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.size["height"],
						        self.image_processor_tester.size["width"],
						    )						,			)








"""simple docstring"""


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


_UpperCamelCase							:       int       					=		'Update an existing config file with the latest defaults while maintaining the old configuration.'




def      snake_case    (A_ :str ):







  '''simple docstring'''

  a	:     int							=  args.config_file
  if config_file is None and Path(A_ ).exists():
    a	:     List[Any]							=  default_config_file
  elif not Path(A_ ).exists():
    raise ValueError(f'''The passed config file located at {config_file} doesn\'t exist.''' )
  a	:     Dict							=  load_config_from_file(A_ )

  if config_file.endswith('.json' ):
    config.to_json_file(A_ )
  else:
    config.to_yaml_file(A_ )
  return config_file




def      snake_case    (A_ :List[str]      ,     A_ :Union[str, Any] ):







  '''simple docstring'''

  a	:     int							=  parser.add_parser('update'      ,     parents=A_      ,     help=A_      ,     formatter_class=A_ )
  parser.add_argument(
      '--config_file'      ,     default=A_      ,     help=(
          'The path to the config file to update. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )      ,     )

  parser.set_defaults(func=A_ )
  return parser






def      snake_case    (A_ :List[str] ):







  '''simple docstring'''

  a	:     List[str]							=  update_config(A_ )
  print(f'''Sucessfully updated the configuration file at {config_file}.''' )




