


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A		:		List[Any]   =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A		:		Tuple   =   ['CamembertTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A		:		List[str]   =   ['CamembertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A		:		Optional[Any]   =   [
							    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'CamembertForCausalLM',
							    'CamembertForMaskedLM',
							    'CamembertForMultipleChoice',
							    'CamembertForQuestionAnswering',
							    'CamembertForSequenceClassification',
							    'CamembertForTokenClassification',
							    'CamembertModel',
							    'CamembertPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A		:		Union[str, Any]   =   [
							    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFCamembertForCausalLM',
							    'TFCamembertForMaskedLM',
							    'TFCamembertForMultipleChoice',
							    'TFCamembertForQuestionAnswering',
							    'TFCamembertForSequenceClassification',
							    'TFCamembertForTokenClassification',
							    'TFCamembertModel',
							    'TFCamembertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_camembert import CamembertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_camembert_fast import CamembertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_camembert import (
														    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CamembertForCausalLM,
														    CamembertForMaskedLM,
														    CamembertForMultipleChoice,
														    CamembertForQuestionAnswering,
														    CamembertForSequenceClassification,
														    CamembertForTokenClassification,
														    CamembertModel,
														    CamembertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_camembert import (
														    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCamembertForCausalLM,
														    TFCamembertForMaskedLM,
														    TFCamembertForMultipleChoice,
														    TFCamembertForQuestionAnswering,
														    TFCamembertForSequenceClassification,
														    TFCamembertForTokenClassification,
														    TFCamembertModel,
														    TFCamembertPreTrainedModel,
														)

else:
							import sys

							__A		:		Union[str, Any]   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import math
import os

import cva
import numpy as np

__UpperCAmelCase      = 255.0





def 	snake_case_      (__A     :						float					,   __A     :						float     )			->	float:
   __lowerCAmelCase  :			Any					    =						np.mean((original - contrast) ** 2     )
   if mse == 0:
      return 1_0_0

   return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__A     )     )





def 	snake_case_      ()			->	None:
   __lowerCAmelCase  :			int					    =						os.path.dirname(os.path.realpath(__A     )     )
   # Loading images (original image and compressed image)
   __lowerCAmelCase  :			Tuple					    =						cva.imread(os.path.join(__A					,   """image_data/original_image.png"""     )     )
   __lowerCAmelCase  :			Dict					    =						cva.imread(os.path.join(__A					,   """image_data/compressed_image.png"""     )					,   1     )

   __lowerCAmelCase  :			Optional[int]					    =						cva.imread(os.path.join(__A					,   """image_data/PSNR-example-base.png"""     )     )
   __lowerCAmelCase  :			Optional[int]					    =						cva.imread(
       os.path.join(__A					,   """image_data/PSNR-example-comp-10.jpg"""     )					,   1     )

   # Value expected: 29.73dB
   print("""-- First Test --"""     )
   print(f'''PSNR value is {peak_signal_to_noise_ratio(__A					,   __A     )} dB'''     )

   # # Value expected: 31.53dB (Wikipedia Example)
   print("""\n-- Second Test --"""     )
   print(f'''PSNR value is {peak_signal_to_noise_ratio(__A					,   __A     )} dB'''     )


if __name__ == "__main__":
   main()


