





import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileNetVaForImageClassification, MobileNetVaModel
    from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor

class   lowerCAmelCase				(   __UpperCamelCase     ):
 def 	A_      ( self    : Optional[int]      )     ->      Optional[Any]:
     lowerCamelCase__       : Dict   					=						self.config_class(**self.inputs_dict      )
     self.parent.assertTrue(hasattr(UpperCAmelCase     ,	'tf_padding'      )      )
     self.parent.assertTrue(hasattr(UpperCAmelCase     ,	'depth_multiplier'      )      )


class   lowerCAmelCase				:
 def __init__( self    : Any     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : Any=13     ,	UpperCAmelCase    : int=3     ,	UpperCAmelCase    : str=32     ,	UpperCAmelCase    : Optional[int]=0.2_5     ,	UpperCAmelCase    : Optional[Any]=8     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : List[Any]=1024     ,	UpperCAmelCase    : Dict=32     ,	UpperCAmelCase    : Tuple="relu6"     ,	UpperCAmelCase    : List[str]=0.1     ,	UpperCAmelCase    : List[str]=0.0_2     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : str=10     ,	UpperCAmelCase    : Union[str, Any]=None     ,	)     ->      int:
     lowerCamelCase__       : Any   					=						parent
     lowerCamelCase__       : Any   					=						batch_size
     lowerCamelCase__       : Dict   					=						num_channels
     lowerCamelCase__       : List[Any]   					=						image_size
     lowerCamelCase__       : Union[str, Any]   					=						depth_multiplier
     lowerCamelCase__       : List[str]   					=						min_depth
     lowerCamelCase__       : Dict   					=						tf_padding
     lowerCamelCase__       : List[Any]   					=						int(last_hidden_size * depth_multiplier      )
     lowerCamelCase__       : Tuple   					=						output_stride
     lowerCamelCase__       : List[str]   					=						hidden_act
     lowerCamelCase__       : int   					=						classifier_dropout_prob
     lowerCamelCase__       : Any   					=						use_labels
     lowerCamelCase__       : Any   					=						is_training
     lowerCamelCase__       : Tuple   					=						num_labels
     lowerCamelCase__       : List[str]   					=						initializer_range
     lowerCamelCase__       : Optional[Any]   					=						scope
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Any   					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     lowerCamelCase__       : Union[str, Any]   					=						None
     lowerCamelCase__       : Optional[Any]   					=						None
     if self.use_labels:
         lowerCamelCase__       : Tuple   					=						ids_tensor([self.batch_size]     ,	self.num_labels      )
         lowerCamelCase__       : Any   					=						ids_tensor([self.batch_size, self.image_size, self.image_size]     ,	self.num_labels      )

     lowerCamelCase__       : Optional[Any]   					=						self.get_config()

     return config, pixel_values, labels, pixel_labels
 def 	A_      ( self    : str      )     ->      Optional[int]:
     return MobileNetVaConfig(
         num_channels=self.num_channels     ,	image_size=self.image_size     ,	depth_multiplier=self.depth_multiplier     ,	min_depth=self.min_depth     ,	tf_padding=self.tf_padding     ,	hidden_act=self.hidden_act     ,	classifier_dropout_prob=self.classifier_dropout_prob     ,	initializer_range=self.initializer_range     ,	)
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : Optional[int]   					=						MobileNetVaModel(config=UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : int   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(
         result.last_hidden_state.shape     ,	(
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )     ,	)
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : Optional[int]   					=						self.num_labels
     lowerCamelCase__       : Optional[int]   					=						MobileNetVaForImageClassification(UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)      )







 def 	A_      ( self    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Dict   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						config_and_inputs
     lowerCamelCase__       : Dict   					=						{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 UpperCAmelCase__						=						(
     {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__						=						False
 UpperCAmelCase__						=						False
 UpperCAmelCase__						=						False
 UpperCAmelCase__						=						False
 def 	A_      ( self    : Union[str, Any]      )     ->      str:
     lowerCamelCase__       : Optional[int]   					=						MobileNetVaModelTester(self      )
     lowerCamelCase__       : Union[str, Any]   					=						MobileNetVaConfigTester(self     ,	config_class=UpperCAmelCase     ,	has_text_modality=UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='MobileNetV1 does not use inputs_embeds'      )
 def 	A_      ( self    : Dict      )     ->      Tuple:
     pass
 @unittest.skip(reason='MobileNetV1 does not support input and output embeddings'      )
 def 	A_      ( self    : List[Any]      )     ->      List[Any]:
     pass
 @unittest.skip(reason='MobileNetV1 does not output attentions'      )
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     pass
 def 	A_      ( self    : Optional[Any]      )     ->      List[Any]:
     lowerCamelCase__					,				lowerCamelCase__       : List[str]   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__       : Tuple   					=						model_class(UpperCAmelCase      )
         lowerCamelCase__       : Any   					=						inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__       : str   					=						[*signature.parameters.keys()]

         lowerCamelCase__       : Optional[Any]   					=						['pixel_values']
         self.assertListEqual(arg_names[:1]     ,	UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : Dict   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase      )
 def 	A_      ( self    : List[str]      )     ->      List[str]:
     def check_hidden_states_output(UpperCAmelCase    : Dict     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : int      ):
         lowerCamelCase__       : Any   					=						model_class(UpperCAmelCase      )
         model.to(UpperCAmelCase      )
         model.eval()

         with torch.no_grad():
             lowerCamelCase__       : Optional[Any]   					=						model(**self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )      )

         lowerCamelCase__       : Dict   					=						outputs.hidden_states

         lowerCamelCase__       : Tuple   					=						26
         self.assertEqual(len(UpperCAmelCase      )     ,	UpperCAmelCase      )

     lowerCamelCase__					,				lowerCamelCase__       : Any   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__       : int   					=						True
         check_hidden_states_output(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__       : Dict   					=						True

         check_hidden_states_output(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]      )     ->      List[str]:
     lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase      )







 @slow
 def 	A_      ( self    : Any      )     ->      List[str]:
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__       : Dict   					=						MobileNetVaModel.from_pretrained(UpperCAmelCase      )
         self.assertIsNotNone(UpperCAmelCase      )


def 			SCREAMING_SNAKE_CASE (			)     ->	Optional[Any]:
    lowerCamelCase__       : int   					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image


@require_torch
@require_vision
class   lowerCAmelCase				(   unittest.TestCase     ):
 @cached_property
 def 	A_      ( self    : int      )     ->      Any:
     return (
         MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'      ) if is_vision_available() else None
     )







 @slow
 def 	A_      ( self    : List[str]      )     ->      str:
     lowerCamelCase__       : Optional[Any]   					=						MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'      ).to(UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						self.default_image_processor
     lowerCamelCase__       : Dict   					=						prepare_img()
     lowerCamelCase__       : int   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : List[str]   					=						model(**UpperCAmelCase      )

     # verify the logits
     lowerCamelCase__       : Any   					=						torch.Size((1, 1001)      )
     self.assertEqual(outputs.logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : str   					=						torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]      ).to(UpperCAmelCase      )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	UpperCAmelCase     ,	atol=1e-4      )      )












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Dict   					=						''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	dict[str, str]:
    lowerCamelCase__       : List[str]   					=						[chr(i + 65					) for i in range(26					)]
    # Remove duplicate characters from key
    lowerCamelCase__       : Any   					=						remove_duplicates(key.upper()					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)
    # First fill cipher with key characters
    lowerCamelCase__       : Optional[int]   					=						{alphabet[i]: char for i, char in enumerate(_UpperCAmelCase					)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase					)		,      26					):
        lowerCamelCase__       : Any   					=						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowerCamelCase__       : Union[str, Any]   					=						alphabet[i - offset]
        lowerCamelCase__       : int   					=						char
    return cipher_alphabet


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    return "".join(cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : List[str]   					=						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : str   					=						input('Enter message to encode or decode: '					).strip()
    lowerCamelCase__       : Union[str, Any]   					=						input('Enter keyword: '					).strip()
    lowerCamelCase__       : Dict   					=						input('Encipher or decipher? E/D:'					).strip()[0].lower()
    try:
        lowerCamelCase__       : Dict   					=						{'e': encipher, 'd': decipher}[option]
    except KeyError:
        raise KeyError('invalid input option'					)
    lowerCamelCase__       : int   					=						create_cipher_map(_UpperCAmelCase					)
    print(func(_UpperCAmelCase		,      _UpperCAmelCase					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





