



import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class 					_A :



       def __init__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=13  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=32  ,	__SCREAMING_SNAKE_CASE							:  int=2  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=16  ,	__SCREAMING_SNAKE_CASE							:  List[str]=[32, 64, 128]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=[1, 2, 1]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=[2, 2, 4]  ,	__SCREAMING_SNAKE_CASE							:  Any=2  ,	__SCREAMING_SNAKE_CASE							:  str=2.0  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  int=0.0  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=0.0  ,	__SCREAMING_SNAKE_CASE							:  Any=0.1  ,	__SCREAMING_SNAKE_CASE							:  Tuple="gelu"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.02  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=1E-5  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Dict=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=10  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=8  ,	__SCREAMING_SNAKE_CASE							:  str=["stage1", "stage2"]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=[1, 2]  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       image_size
           __a		 =       patch_size
           __a		 =       num_channels
           __a		 =       embed_dim
           __a		 =       hidden_sizes
           __a		 =       depths
           __a		 =       num_heads
           __a		 =       window_size
           __a		 =       mlp_ratio
           __a		 =       qkv_bias
           __a		 =       hidden_dropout_prob
           __a		 =       attention_probs_dropout_prob
           __a		 =       drop_path_rate
           __a		 =       hidden_act
           __a		 =       use_absolute_embeddings
           __a		 =       patch_norm
           __a		 =       layer_norm_eps
           __a		 =       initializer_range
           __a		 =       is_training
           __a		 =       scope
           __a		 =       use_labels
           __a		 =       type_sequence_label_size
           __a		 =       encoder_stride
           __a		 =       out_features
           __a		 =       out_indices



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           __a		 =       None
           if self.use_labels:
               __a		 =       ids_tensor([self.batch_size]  ,	self.type_sequence_label_size)

           __a		 =       self.get_config()

           return config, pixel_values, labels



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           return FocalNetConfig(
               image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	embed_dim=self.embed_dim  ,	hidden_sizes=self.hidden_sizes  ,	depths=self.depths  ,	num_heads=self.num_heads  ,	window_size=self.window_size  ,	mlp_ratio=self.mlp_ratio  ,	qkv_bias=self.qkv_bias  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	drop_path_rate=self.drop_path_rate  ,	hidden_act=self.hidden_act  ,	use_absolute_embeddings=self.use_absolute_embeddings  ,	path_norm=self.patch_norm  ,	layer_norm_eps=self.layer_norm_eps  ,	initializer_range=self.initializer_range  ,	encoder_stride=self.encoder_stride  ,	out_features=self.out_features  ,	out_indices=self.out_indices  ,	)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           __a		 =       FocalNetModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)

           __a		 =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           __a		 =       int(config.embed_dim * 2 ** (len(config.depths) - 1))

           self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, expected_seq_len, expected_dim))



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       FocalNetBackbone(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps)  ,	len(config.out_features))
           self.parent.assertListEqual(list(result.feature_maps[0].shape)  ,	[self.batch_size, self.image_size, 8, 8])

           # verify channels
           self.parent.assertEqual(len(model.channels)  ,	len(config.out_features))
           self.parent.assertListEqual(model.channels  ,	config.hidden_sizes[:-1])

           # verify backbone works with out_features=None
           __a		 =       None
           __a		 =       FocalNetBackbone(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps)  ,	1)
           self.parent.assertListEqual(list(result.feature_maps[0].shape)  ,	[self.batch_size, self.image_size * 2, 4, 4])

           # verify channels
           self.parent.assertEqual(len(model.channels)  ,	1)
           self.parent.assertListEqual(model.channels  ,	[config.hidden_sizes[-1]])



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       FocalNetForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.reconstruction.shape  ,	(self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           __a		 =       1
           __a		 =       FocalNetForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.reconstruction.shape  ,	(self.batch_size, 1, self.image_size, self.image_size))



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.type_sequence_label_size
           __a		 =       FocalNetForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           __a		 =       1
           __a		 =       FocalNetForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()

           __a ,     __a ,     __a		 =       config_and_inputs
           __a		 =       {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Optional[int]		      =		(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase__						:					Optional[Any]		      =		(
           {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       UpperCamelCase__						:					Optional[Any]		      =		False

       UpperCamelCase__						:					int		      =		False
       UpperCamelCase__						:					int		      =		False
       UpperCamelCase__						:					Dict		      =		False
       UpperCamelCase__						:					Dict		      =		False



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       FocalNetModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	embed_dim=37  ,	has_text_modality=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       @unittest.skip(reason='''FocalNet does not use inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''FocalNet does not use feedforward chunking''')
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               self.assertIsInstance(model.get_input_embeddings()  ,	(nn.Module))
               __a		 =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE  ,	nn.Linear))



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               __a		 =       inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a		 =       [*signature.parameters.keys()]

               __a		 =       ['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           __a		 =       model_class(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
               __a		 =       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE))

           __a		 =       outputs.hidden_states

           __a		 =       getattr(
               self.model_tester  ,	'''expected_num_hidden_layers'''  ,	len(self.model_tester.depths) + 1)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	__SCREAMING_SNAKE_CASE)

           # FocalNet has a different seq_length
           __a		 =       (
               config.patch_size
               if isinstance(config.patch_size  ,	collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a		 =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:])  ,	[num_patches, self.model_tester.embed_dim]  ,	)

           __a		 =       outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	__SCREAMING_SNAKE_CASE)

           __a ,     __a ,     __a ,     __a		 =       reshaped_hidden_states[0].shape
           __a		 =       (
               reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	height * width).permute(0  ,	2  ,	1)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])  ,	[num_patches, self.model_tester.embed_dim]  ,	)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           __a		 =       (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size  ,	collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
               __a		 =       True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a		 =       True

               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
           __a		 =       3

           __a		 =       (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size  ,	collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           __a		 =       (
               config.patch_size
               if isinstance(config.patch_size  ,	collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a		 =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __a		 =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
               __a		 =       True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	(padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a		 =       True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	(padded_height, padded_width))



       @slow
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a		 =       FocalNetModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           __a		 =       _config_zero_init(__SCREAMING_SNAKE_CASE)
           for model_class in self.all_model_classes:
               __a		 =       model_class(config=__SCREAMING_SNAKE_CASE)
               for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()  ,	[0.0, 1.0]  ,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'  ,	)


@require_vision
@require_torch
class 					_A (			unittest.TestCase					):



       @cached_property
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''') if is_vision_available() else None





       @slow
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''').to(__SCREAMING_SNAKE_CASE)
           __a		 =       self.default_image_processor

           __a		 =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a		 =       torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape  ,	__SCREAMING_SNAKE_CASE)
           __a		 =       torch.tensor([0.21_66, -0.43_68, 0.21_91]).to(__SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,	__SCREAMING_SNAKE_CASE  ,	atol=1E-4))
           self.assertTrue(outputs.logits.argmax(dim=-1).item()  ,	281)


@require_torch
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					List[Any]		      =		(FocalNetBackbone,) if is_torch_available() else ()
       UpperCamelCase__						:					int		      =		FocalNetConfig

       UpperCamelCase__						:					Dict		      =		False



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       FocalNetModelTester(self)










def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    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   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {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       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





