






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin
class        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase=None			,     __lowercase=None			,     __lowercase=None			,     __lowercase="resnet50"			,     __lowercase=3			,     __lowercase=3_2			,     __lowercase=3			,     __lowercase=True			,     __lowercase=True			,     )				->   Dict:
				lowerCamelCase   : int								=parent
				lowerCamelCase   : Union[str, Any]								=out_indices if out_indices is not None else [4]
				lowerCamelCase   : Any								=stage_names
				lowerCamelCase   : Any								=out_features
				lowerCamelCase   : Dict								=backbone
				lowerCamelCase   : Union[str, Any]								=batch_size
				lowerCamelCase   : List[Any]								=image_size
				lowerCamelCase   : Dict								=num_channels
				lowerCamelCase   : Tuple								=use_pretrained_backbone
				lowerCamelCase   : Dict								=is_training







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : List[Any]								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
				lowerCamelCase   : int								=self.get_config()

				return config, pixel_values







		def 			__lowercase   (		self   )				->   str:
				return TimmBackboneConfig(
				    image_size=self.image_size			,     num_channels=self.num_channels			,     out_features=self.out_features			,     out_indices=self.out_indices			,     stage_names=self.stage_names			,     use_pretrained_backbone=self.use_pretrained_backbone			,     backbone=self.backbone			,     )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   List[Any]:
				lowerCamelCase   : List[str]								=TimmBackbone(config=__lowercase   )
				model.to(__lowercase   )
				model.eval()
				with torch.no_grad():
						lowerCamelCase   : List[str]								=model(__lowercase   )
				self.parent.assertEqual(
				    result.feature_map[-1].shape			,     (self.batch_size, model.channels[-1], 1_4, 1_4)			,     )







		def 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : str								=self.prepare_config_and_inputs()
				lowerCamelCase   : Any								=config_and_inputs
				lowerCamelCase   : str								={'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_torch
@require_timm
class        snake_case_		(			_A						,					_A						,					_A						,					unittest.TestCase):
		lowerCamelCase    :List[Any]						=     (TimmBackbone,) if is_torch_available() else ()
		lowerCamelCase    :Optional[int]						=     {"feature-extraction": TimmBackbone} if is_torch_available() else {}
		lowerCamelCase    :Tuple						=     False
		lowerCamelCase    :List[Any]						=     False
		lowerCamelCase    :int						=     False
		lowerCamelCase    :str						=     False







		def 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : Union[str, Any]								=TimmBackboneModelTester(self   )
				lowerCamelCase   : List[Any]								=ConfigTester(self			,     config_class=__lowercase			,     has_text_modality=__lowercase   )







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				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 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : Dict								='''resnet18'''
				lowerCamelCase   : Tuple								='''microsoft/resnet-18'''

				lowerCamelCase   : Any								=AutoBackbone.from_pretrained(__lowercase			,     use_timm_backbone=__lowercase   )
				lowerCamelCase   : Tuple								=AutoBackbone.from_pretrained(__lowercase   )

				self.assertEqual(len(timm_model.out_features   )			,     len(transformers_model.out_features   )   )
				self.assertEqual(len(timm_model.stage_names   )			,     len(transformers_model.stage_names   )   )
				self.assertEqual(timm_model.channels			,     transformers_model.channels   )
				# Out indices are set to the last layer by default. For timm models, we don't know
				# the number of layers in advance, so we set it to (-1,), whereas for transformers
				# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
				self.assertEqual(timm_model.out_indices			,     (-1,)   )
				self.assertEqual(transformers_model.out_indices			,     [len(timm_model.stage_names   ) - 1]   )

				lowerCamelCase   : List[Any]								=AutoBackbone.from_pretrained(__lowercase			,     use_timm_backbone=__lowercase			,     out_indices=[1, 2, 3]   )
				lowerCamelCase   : List[Any]								=AutoBackbone.from_pretrained(__lowercase			,     out_indices=[1, 2, 3]   )

				self.assertEqual(timm_model.out_indices			,     transformers_model.out_indices   )
				self.assertEqual(len(timm_model.out_features   )			,     len(transformers_model.out_features   )   )
				self.assertEqual(timm_model.channels			,     transformers_model.channels   )







		@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''   )
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				pass







		@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''   )
		def 			__lowercase   (		self   )				->   List[str]:
				pass







		@unittest.skip('''TimmBackbone initialization is managed on the timm side'''   )
		def 			__lowercase   (		self   )				->   Optional[Any]:
				pass







		@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''   )
		def 			__lowercase   (		self   )				->   str:
				pass







		@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''   )
		def 			__lowercase   (		self   )				->   Dict:
				pass







		@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''   )
		def 			__lowercase   (		self   )				->   Optional[int]:
				pass







		@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
		def 			__lowercase   (		self   )				->   Any:
				pass







		@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''   )
		def 			__lowercase   (		self   )				->   Optional[int]:
				pass







		@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''   )
		def 			__lowercase   (		self   )				->   List[Any]:
				pass







		@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
		def 			__lowercase   (		self   )				->   Optional[int]:
				pass







		@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
		def 			__lowercase   (		self   )				->   Optional[int]:
				pass







		@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''   )
		def 			__lowercase   (		self   )				->   Dict:
				pass







		@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''   )
		def 			__lowercase   (		self   )				->   int:
				pass







		@unittest.skip('''Safetensors is not supported by timm.'''   )
		def 			__lowercase   (		self   )				->   int:
				pass







		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				pass







		def 			__lowercase   (		self   )				->   Optional[int]:
				lowerCamelCase   : Optional[int]								=self.model_tester.prepare_config_and_inputs_for_common()

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

						lowerCamelCase   : Optional[int]								=['''pixel_values''']
						self.assertListEqual(arg_names[:1]			,     __lowercase   )







		def 			__lowercase   (		self   )				->   Tuple:
				lowerCamelCase   : int								=self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase   : List[str]								=True
				lowerCamelCase   : Optional[Any]								=self.has_attentions

				# no need to test all models as different heads yield the same functionality
				lowerCamelCase   : List[Any]								=self.all_model_classes[0]
				lowerCamelCase   : Tuple								=model_class(__lowercase   )
				model.to(__lowercase   )

				lowerCamelCase   : List[Any]								=self._prepare_for_class(__lowercase			,     __lowercase   )
				lowerCamelCase   : Tuple								=model(**__lowercase   )
				lowerCamelCase   : List[Any]								=outputs[0][-1]

				# Encoder-/Decoder-only models
				lowerCamelCase   : List[str]								=outputs.hidden_states[0]
				hidden_states.retain_grad()

				if self.has_attentions:
						lowerCamelCase   : str								=outputs.attentions[0]
						attentions.retain_grad()

				output.flatten()[0].backward(retain_graph=__lowercase   )

				self.assertIsNotNone(hidden_states.grad   )

				if self.has_attentions:
						self.assertIsNotNone(attentions.grad   )







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : Any								=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowerCamelCase   : List[str]								=model_class(__lowercase   )
						model.to(__lowercase   )
						model.eval()
						lowerCamelCase   : List[str]								=model(**__lowercase   )

						self.assertEqual(len(result.feature_maps   )			,     len(config.out_indices   )   )
						self.assertEqual(len(model.channels   )			,     len(config.out_indices   )   )

						# Check output of last stage is taken if out_features=None, out_indices=None
						lowerCamelCase   : Union[str, Any]								=copy.deepcopy(__lowercase   )
						lowerCamelCase   : int								=None
						lowerCamelCase   : int								=model_class(__lowercase   )
						model.to(__lowercase   )
						model.eval()
						lowerCamelCase   : Any								=model(**__lowercase   )

						self.assertEqual(len(result.feature_maps   )			,     1   )
						self.assertEqual(len(model.channels   )			,     1   )

						# Check backbone can be initialized with fresh weights
						lowerCamelCase   : str								=copy.deepcopy(__lowercase   )
						lowerCamelCase   : Union[str, Any]								=False
						lowerCamelCase   : List[str]								=model_class(__lowercase   )
						model.to(__lowercase   )
						model.eval()
						lowerCamelCase   : Optional[Any]								=model(**__lowercase   )








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = False							,       )   ->     tuple[int, float, str]:
		lowerCamelCase   : Union[str, Any]								=cipher_alphabet or [chr(SCREAMING_SNAKE_CASE_		) for i in range(9_7							,       1_2_3		)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				lowerCamelCase   : str								={
				    '''a''': 0.0_8_4_9_7,
				    '''b''': 0.0_1_4_9_2,
				    '''c''': 0.0_2_2_0_2,
				    '''d''': 0.0_4_2_5_3,
				    '''e''': 0.1_1_1_6_2,
				    '''f''': 0.0_2_2_2_8,
				    '''g''': 0.0_2_0_1_5,
				    '''h''': 0.0_6_0_9_4,
				    '''i''': 0.0_7_5_4_6,
				    '''j''': 0.0_0_1_5_3,
				    '''k''': 0.0_1_2_9_2,
				    '''l''': 0.0_4_0_2_5,
				    '''m''': 0.0_2_4_0_6,
				    '''n''': 0.0_6_7_4_9,
				    '''o''': 0.0_7_5_0_7,
				    '''p''': 0.0_1_9_2_9,
				    '''q''': 0.0_0_0_9_5,
				    '''r''': 0.0_7_5_8_7,
				    '''s''': 0.0_6_3_2_7,
				    '''t''': 0.0_9_3_5_6,
				    '''u''': 0.0_2_7_5_8,
				    '''v''': 0.0_0_9_7_8,
				    '''w''': 0.0_2_5_6_0,
				    '''x''': 0.0_0_1_5_0,
				    '''y''': 0.0_1_9_9_4,
				    '''z''': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				lowerCamelCase   : Tuple								=frequencies_dict

		if not case_sensitive:
				lowerCamelCase   : Any								=ciphertext.lower()

		# Chi squared statistic values
		lowerCamelCase   : dict[int, tuple[float, str]]								={}

		# cycle through all of the shifts
		for shift in range(len(SCREAMING_SNAKE_CASE_		)		):
				lowerCamelCase   : int								=''''''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								lowerCamelCase   : Dict								=(alphabet_letters.index(letter.lower()		) - shift) % len(
								    SCREAMING_SNAKE_CASE_		)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				lowerCamelCase   : str								=0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								lowerCamelCase   : Union[str, Any]								=letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : List[str]								=decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Optional[Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : Tuple								=((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : Optional[int]								=decrypted_with_shift.count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Union[str, Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : int								=((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				lowerCamelCase   : Dict								=(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(SCREAMING_SNAKE_CASE_		) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		lowerCamelCase   : int								=min(
		    SCREAMING_SNAKE_CASE_							,       key=SCREAMING_SNAKE_CASE_							,       )

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    lowerCamelCase
		)		,   (
		    lowerCamelCase
		)		,   
		)   : Any								=chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)

