"""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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   '''Update an existing config file with the latest defaults while maintaining the old configuration.'''



def       A__    (	lowercase_     :	Dict						)  ->							Any:
   __lowerCamelCase    		=					args.config_file
   if config_file is None and Path(lowercase_						).exists():
      __lowerCamelCase    		=					default_config_file
   elif not Path(lowercase_						).exists():
      raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.'''						)
   __lowerCamelCase    		=					load_config_from_file(lowercase_						)

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



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Any						)  ->							Optional[int]:
   __lowerCamelCase    		=					parser.add_parser('update'      , parents=lowercase_      , help=lowercase_      , formatter_class=lowercase_						)
   parser.add_argument(
       '--config_file'      , default=lowercase_      , 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=lowercase_						)
   return parser



def       A__    (	lowercase_     :	str						)  ->							List[Any]:
   __lowerCamelCase    		=					update_config(lowercase_						)
   print(F'''Sucessfully updated the configuration file at {config_file}.'''						)














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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=None							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]							, _UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]							, _UpperCAmelCase=True							, )		->				List[str]:
        lowerCamelCase									=size if size is not None else {"""height""": 20, """width""": 20}
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_resize
        lowerCamelCase									=size
        lowerCamelCase									=do_normalize
        lowerCamelCase									=image_mean
        lowerCamelCase									=image_std
        lowerCamelCase									=do_pad
        lowerCamelCase									=do_convert_rgb





     def 						lowerCamelCase							(	self  )		->				str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				Optional[int]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    str 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=BlipImageProcessingTester(self  )





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





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        pass





     def 						lowerCamelCase							(	self  )		->				int:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(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
        lowerCamelCase									=image_processor(_UpperCAmelCase							, 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 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(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
        lowerCamelCase									=image_processor(_UpperCAmelCase							, 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 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(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
        lowerCamelCase									=image_processor(_UpperCAmelCase							, 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"""],
            )							, )




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Any 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=BlipImageProcessingTester(self							, num_channels=4  )
        lowerCamelCase									=3





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





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





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )