"""simple docstring"""






import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

						from transformers import RegNetForImageClassification, RegNetModel
						from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			List[str]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Tuple=3					,lowerCamelCase__     :			Union[str, Any]=32					,lowerCamelCase__     :			str=3					,lowerCamelCase__     :			List[Any]=10					,lowerCamelCase__     :			str=[10, 20, 30, 40]					,lowerCamelCase__     :			int=[1, 1, 2, 1]					,lowerCamelCase__     :			Tuple=True					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			Optional[int]="relu"					,lowerCamelCase__     :			Dict=3					,lowerCamelCase__     :			List[str]=None					,):
									UpperCAmelCase__    =							parent
									UpperCAmelCase__    =							batch_size
									UpperCAmelCase__    =							image_size
									UpperCAmelCase__    =							num_channels
									UpperCAmelCase__    =							embeddings_size
									UpperCAmelCase__    =							hidden_sizes
									UpperCAmelCase__    =							depths
									UpperCAmelCase__    =							is_training
									UpperCAmelCase__    =							use_labels
									UpperCAmelCase__    =							hidden_act
									UpperCAmelCase__    =							num_labels
									UpperCAmelCase__    =							scope
									UpperCAmelCase__    =							len(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

									UpperCAmelCase__    =							None
									if self.use_labels:
													UpperCAmelCase__    =							ids_tensor([self.batch_size]					,self.num_labels    )

									UpperCAmelCase__    =							self.get_config()

									return config, pixel_values, labels







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return RegNetConfig(
									    num_channels=self.num_channels					,embeddings_size=self.embeddings_size					,hidden_sizes=self.hidden_sizes					,depths=self.depths					,hidden_act=self.hidden_act					,num_labels=self.num_labels					,)







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							RegNetModel(config=lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape					,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,)







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							self.num_labels
									UpperCAmelCase__    =							RegNetForImageClassification(lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()
									UpperCAmelCase__    =							model(lowerCamelCase__					,labels=lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							config_and_inputs
									UpperCAmelCase__    =							{'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					snake_case__       					=				(
					    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							RegNetModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=lowerCamelCase__					,has_text_modality=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									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     :			Tuple    ):
									return







					@unittest.skip(reason='RegNet does not use inputs_embeds'    )
					def 	__lowerCAmelCase		(       self     :			Any    ):
									pass







					@unittest.skip(reason='RegNet does not support input and output embeddings'    )
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase__    =							['pixel_values']
													self.assertListEqual(arg_names[:1]					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__    =							model_class(config=lowerCamelCase__    )
													for name, module in model.named_modules():
																	if isinstance(lowerCamelCase__					,(nn.BatchNormad, nn.GroupNorm)    ):
																					self.assertTrue(
																					    torch.all(module.weight == 1    )					,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''					,)
																					self.assertTrue(
																					    torch.all(module.bias == 0    )					,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''					,)







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									def check_hidden_states_output(lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Tuple    ):
													UpperCAmelCase__    =							model_class(lowerCamelCase__    )
													model.to(lowerCamelCase__    )
													model.eval()

													with torch.no_grad():
																	UpperCAmelCase__    =							model(**self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__    )    )

													UpperCAmelCase__    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													UpperCAmelCase__    =							self.model_tester.num_stages
													self.assertEqual(len(lowerCamelCase__    )					,expected_num_stages + 1    )

													# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]    )					,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,)

									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()
									UpperCAmelCase__    =							['basic', 'bottleneck']
									for model_class in self.all_model_classes:
													for layer_type in layers_type:
																	UpperCAmelCase__    =							layer_type
																	UpperCAmelCase__    =							True
																	check_hidden_states_output(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )

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

																	check_hidden_states_output(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase__    =							RegNetModel.from_pretrained(lowerCamelCase__    )
													self.assertIsNotNone(lowerCamelCase__    )






def 						a_		(      ):
				UpperCAmelCase__    =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
				return image







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




					"""simple docstring"""

					@cached_property
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
									    if is_vision_available()
									    else None
									)







					@slow
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowerCamelCase__    )

									UpperCAmelCase__    =							self.default_image_processor
									UpperCAmelCase__    =							prepare_img()
									UpperCAmelCase__    =							image_processor(images=lowerCamelCase__					,return_tensors='pt'    ).to(lowerCamelCase__    )

									# forward pass
									with torch.no_grad():
													UpperCAmelCase__    =							model(**lowerCamelCase__    )

									# verify the logits
									UpperCAmelCase__    =							torch.Size((1, 1_000)    )
									self.assertEqual(outputs.logits.shape					,lowerCamelCase__    )

									UpperCAmelCase__    =							torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]    ).to(lowerCamelCase__    )

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




"""simple docstring"""






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							""
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42

				for keychar, cipherchar in zip(cycle(lowerCamelCase )      ,    lowerCamelCase ):
								UpperCAmelCase__    =							cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(lowerCamelCase )

				return decoded



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for key in product(lowerCamelCase      ,    repeat=3 ):
								UpperCAmelCase__    =							try_key(lowerCamelCase      ,    lowerCamelCase )
								if encoded is not None:
												possibles.append(lowerCamelCase )
				return possibles



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							Path(lowerCamelCase ).parent.joinpath(lowerCamelCase ).read_text(encoding='utf-8' )

				UpperCAmelCase__    =							[int(lowerCamelCase ) for number in data.strip().split(',' )]

				UpperCAmelCase__    =							filter_valid_chars(lowerCamelCase )
				for common_word in COMMON_WORDS:
								UpperCAmelCase__    =							filter_common_word(lowerCamelCase      ,    lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												break

				UpperCAmelCase__    =							possibles[0]
				return sum(ord(lowerCamelCase ) for char in decoded_text )


if __name__ == "__main__":
						print(F"""{solution() = }""")



