



'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class     __snake_case		:
					def __init__(     self,    A,    A=3,    A=32,    A=3,    A=10,    A=[10, 20, 30, 40],    A=[1, 1, 2, 1],    A=True,    A=True,    A="relu",    A=3,    A=None,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= parent
									lowerCamelCase					:			Optional[Any]									= batch_size
									lowerCamelCase					:			Optional[Any]									= image_size
									lowerCamelCase					:			Optional[int]									= num_channels
									lowerCamelCase					:			List[Any]									= embeddings_size
									lowerCamelCase					:			Optional[int]									= hidden_sizes
									lowerCamelCase					:			int									= depths
									lowerCamelCase					:			List[str]									= is_training
									lowerCamelCase					:			Tuple									= use_labels
									lowerCamelCase					:			Optional[int]									= hidden_act
									lowerCamelCase					:			Optional[Any]									= num_labels
									lowerCamelCase					:			Dict									= scope
									lowerCamelCase					:			Optional[int]									= len(__lowercase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									lowerCamelCase					:			Optional[int]									= None
									if self.use_labels:
													lowerCamelCase					:			Any									= ids_tensor([self.batch_size],    self.num_labels      )

									lowerCamelCase					:			List[str]									= self.get_config()

									return config, pixel_values, labels
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return ResNetConfig(
									    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,    image_size=self.image_size,    )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= TFResNetModel(config=__lowercase      )
									lowerCamelCase					:			Any									= model(__lowercase      )
									# 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     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.num_labels
									lowerCamelCase					:			List[str]									= TFResNetForImageClassification(__lowercase      )
									lowerCamelCase					:			Optional[Any]									= model(__lowercase,    labels=__lowercase      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.prepare_config_and_inputs()
									lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= config_and_inputs
									lowerCamelCase					:			str									= {'pixel_values': pixel_values}
									return config, inputs_dict





@require_tf
class     __snake_case		(   lowercase__    ,   lowercase__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					_lowerCAmelCase			     =	(
					    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= TFResNetModelTester(self      )
									lowerCamelCase					:			Optional[Any]									= ConfigTester(self,    config_class=__lowercase,    has_text_modality=__lowercase      )
					def     UpperCAmelCase_							(     self      ):

									"""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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return
					@unittest.skip(reason='ResNet does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='ResNet does not support input and output embeddings'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

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

													lowerCamelCase					:			Union[str, Any]									= ['pixel_values']
													self.assertListEqual(arg_names[:1],    __lowercase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowercase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									def check_hidden_states_output(A,    A,    A      ):
													lowerCamelCase					:			Dict									= model_class(__lowercase      )
													lowerCamelCase					:			Union[str, Any]									= model(**self._prepare_for_class(__lowercase,    __lowercase      )      )

													lowerCamelCase					:			List[Any]									= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase					:			Tuple									= self.model_tester.num_stages
													self.assertEqual(len(__lowercase      ),    expected_num_stages + 1      )

													# ResNet'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 // 4, self.model_tester.image_size // 4],    )

									lowerCamelCase			, lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase					:			str									= ['basic', 'bottleneck']
									for model_class in self.all_model_classes:
													for layer_type in layers_type:
																	lowerCamelCase					:			Any									= layer_type
																	lowerCamelCase					:			Dict									= True
																	check_hidden_states_output(__lowercase,    __lowercase,    __lowercase      )

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

																	check_hidden_states_output(__lowercase,    __lowercase,    __lowercase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowercase      )




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			str									= TFResNetModel.from_pretrained(__lowercase      )
													self.assertIsNotNone(__lowercase      )





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Any									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
				return image




@require_tf
@require_vision
class     __snake_case		(   unittest.TestCase):
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
									    if is_vision_available()
									    else None
									)




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

									lowerCamelCase					:			str									= self.default_image_processor
									lowerCamelCase					:			List[Any]									= prepare_img()
									lowerCamelCase					:			Any									= image_processor(images=__lowercase,    return_tensors='tf'      )

									# forward pass
									lowerCamelCase					:			List[str]									= model(**__lowercase      )

									# verify the logits
									lowerCamelCase					:			Optional[int]									= tf.TensorShape((1, 1000)      )
									self.assertEqual(outputs.logits.shape,    __lowercase      )

									lowerCamelCase					:			List[Any]									= tf.constant([-11.1069, -9.7877, -8.3777]      )

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


'''simple docstring'''






import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A			      =      {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
A			      =      'ETAOINSHRDLCUMWFGYPBVKJXQZ'
A			      =      'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= {letter: 0 for letter in string.ascii_uppercase}
				for letter in message.upper():
								if letter in LETTERS:
												letter_count[letter] += 1

				return letter_count





def    UpperCAmelCase							(  UpperCAmelCase__							:							tuple):
				return x[0]





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_letter_count(UpperCAmelCase__)
				lowerCamelCase					:			dict[int, list[str]]									= {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCAmelCase__)

				lowerCamelCase					:			dict[int, str]									= {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find       ,							reverse=UpperCAmelCase__)
								lowerCamelCase					:			int									= ''.join(freq_to_letter[freq])

				lowerCamelCase					:			Any									= list(freq_to_letter_str.items())
				freq_pairs.sort(key=UpperCAmelCase__       ,							reverse=UpperCAmelCase__)

				lowerCamelCase					:			list[str]									= [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_frequency_order(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0
				for common_letter in ETAOIN[:6]:
								if common_letter in freq_order[:6]:
												match_score += 1

				for uncommon_letter in ETAOIN[-6:]:
								if uncommon_letter in freq_order[-6:]:
												match_score += 1

				return match_score


if __name__ == "__main__":
					import doctest

					doctest.testmod()
