



'''simple docstring'''




from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


UpperCamelCase__       :	Union[str, Any]    = logging.get_logger(__name__)


# General docstring
UpperCamelCase__       :	Union[str, Any]    = '''MobileNetV1Config'''

# Base docstring
UpperCamelCase__       :	Optional[Any]    = '''google/mobilenet_v1_1.0_224'''
UpperCamelCase__       :	str    = [1, 10_24, 7, 7]

# Image classification docstring
UpperCamelCase__       :	Any    = '''google/mobilenet_v1_1.0_224'''
UpperCamelCase__       :	Optional[Any]    = '''tabby, tabby cat'''


UpperCamelCase__       :	Any    = [
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   Tuple=None					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		{}

		if isinstance(_lowerCamelCase       ,      _lowerCamelCase					):
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model.mobilenet_va
		else:
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""MobilenetV1/Conv2d_0/"""
		__SCREAMING_SNAKE_CASE       :		List[str]           =		backbone.conv_stem.convolution.weight
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		backbone.conv_stem.normalization.bias
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		backbone.conv_stem.normalization.weight
		__SCREAMING_SNAKE_CASE       :		str           =		backbone.conv_stem.normalization.running_mean
		__SCREAMING_SNAKE_CASE       :		Dict           =		backbone.conv_stem.normalization.running_var

		for i in range(13					):
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		i + 1
				__SCREAMING_SNAKE_CASE       :		Dict           =		i * 2

				__SCREAMING_SNAKE_CASE       :		List[str]           =		backbone.layer[pt_index]
				__SCREAMING_SNAKE_CASE       :		int           =		F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
				__SCREAMING_SNAKE_CASE       :		List[str]           =		pointer.convolution.weight
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		pointer.normalization.bias
				__SCREAMING_SNAKE_CASE       :		str           =		pointer.normalization.weight
				__SCREAMING_SNAKE_CASE       :		Tuple           =		pointer.normalization.running_mean
				__SCREAMING_SNAKE_CASE       :		int           =		pointer.normalization.running_var

				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		backbone.layer[pt_index + 1]
				__SCREAMING_SNAKE_CASE       :		Dict           =		F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		pointer.convolution.weight
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		pointer.normalization.bias
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		pointer.normalization.weight
				__SCREAMING_SNAKE_CASE       :		str           =		pointer.normalization.running_mean
				__SCREAMING_SNAKE_CASE       :		int           =		pointer.normalization.running_var

		if isinstance(_lowerCamelCase       ,      _lowerCamelCase					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
				__SCREAMING_SNAKE_CASE       :		str           =		model.classifier.weight
				__SCREAMING_SNAKE_CASE       :		Tuple           =		model.classifier.bias

		return tf_to_pt_map


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]       ,      _lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		try:
				import numpy as np
				import tensorflow as tf
		except ImportError:
				logger.error(
				    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
				    """https://www.tensorflow.org/install/ for installation instructions."""					)
				raise

		# Load weights from TF model
		__SCREAMING_SNAKE_CASE       :		Tuple           =		tf.train.list_variables(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		str           =		{}
		for name, shape in init_vars:
				logger.info(F"Loading TF weight {name} with shape {shape}"					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tf.train.load_variable(_lowerCamelCase       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		array

		# Build TF to PyTorch weights loading map
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		_build_tf_to_pytorch_map(_lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)

		for name, pointer in tf_to_pt_map.items():
				logger.info(F"Importing {name}"					)
				if name not in tf_weights:
						logger.info(F"{name} not in tf pre-trained weights, skipping"					)
						continue

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tf_weights[name]

				if "depthwise_weights" in name:
						logger.info("""Transposing depthwise"""					)
						__SCREAMING_SNAKE_CASE       :		Any           =		np.transpose(_lowerCamelCase       ,      (2, 3, 0, 1)					)
				elif "weights" in name:
						logger.info("""Transposing"""					)
						if len(pointer.shape					) == 2:  # copying into linear layer
								__SCREAMING_SNAKE_CASE       :		List[str]           =		array.squeeze().transpose()
						else:
								__SCREAMING_SNAKE_CASE       :		List[Any]           =		np.transpose(_lowerCamelCase       ,      (3, 2, 0, 1)					)

				if pointer.shape != array.shape:
						raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"					)

				logger.info(F"Initialize PyTorch weight {name} {array.shape}"					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		torch.from_numpy(_lowerCamelCase					)

				tf_weights.pop(_lowerCamelCase       ,      _lowerCamelCase					)
				tf_weights.pop(name + """/RMSProp"""       ,      _lowerCamelCase					)
				tf_weights.pop(name + """/RMSProp_1"""       ,      _lowerCamelCase					)
				tf_weights.pop(name + """/ExponentialMovingAverage"""       ,      _lowerCamelCase					)

		logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()					)}"					)
		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   torch.Tensor       ,      _lowerCamelCase:   nn.Convad					):
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[str]           =		features.shape[-2:]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		conv_layer.stride
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		conv_layer.kernel_size

		if in_height % stride_height == 0:
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		max(kernel_height - stride_height       ,      0					)
		else:
				__SCREAMING_SNAKE_CASE       :		Any           =		max(kernel_height - (in_height % stride_height)       ,      0					)

		if in_width % stride_width == 0:
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		max(kernel_width - stride_width       ,      0					)
		else:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		max(kernel_width - (in_width % stride_width)       ,      0					)

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		pad_along_width // 2
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pad_along_width - pad_left
		__SCREAMING_SNAKE_CASE       :		Dict           =		pad_along_height // 2
		__SCREAMING_SNAKE_CASE       :		Any           =		pad_along_height - pad_top

		__SCREAMING_SNAKE_CASE       :		List[str]           =		(pad_left, pad_right, pad_top, pad_bottom)
		return nn.functional.pad(_lowerCamelCase       ,      _lowerCamelCase       ,      """constant"""       ,      0.0					)






class 						_UpperCamelCase  (							nn.Module    ):


			'''simple docstring'''

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : MobileNetVaConfig							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Optional[int] = 1							,     lowerCAmelCase__     : Optional[int] = 1							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[bool] = True							,     lowerCAmelCase__     : Optional[bool or str] = True							,     ):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		config

					if in_channels % groups != 0:
							raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."						)
					if out_channels % groups != 0:
							raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."						)

					__SCREAMING_SNAKE_CASE       :		Any           =		0 if config.tf_padding else int((kernel_size - 1) / 2						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Convad(
					    in_channels=lowerCAmelCase__							,     out_channels=lowerCAmelCase__							,     kernel_size=lowerCAmelCase__							,     stride=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     groups=lowerCAmelCase__							,     bias=lowerCAmelCase__							,     padding_mode="""zeros"""							,     )

					if use_normalization:
							__SCREAMING_SNAKE_CASE       :		str           =		nn.BatchNormad(
							    num_features=lowerCAmelCase__							,     eps=config.layer_norm_eps							,     momentum=0.99_97							,     affine=lowerCAmelCase__							,     track_running_stats=lowerCAmelCase__							,     )
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		None

					if use_activation:
							if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE       :		List[str]           =		ACTaFN[use_activation]
							elif isinstance(config.hidden_act							,     lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE       :		Any           =		ACTaFN[config.hidden_act]
							else:
									__SCREAMING_SNAKE_CASE       :		str           =		config.hidden_act
					else:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		None



			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : torch.Tensor						):





					"""simple docstring"""






					if self.config.tf_padding:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		apply_tf_padding(lowerCAmelCase__							,     self.convolution						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.convolution(lowerCAmelCase__						)
					if self.normalization is not None:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.normalization(lowerCAmelCase__						)
					if self.activation is not None:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.activation(lowerCAmelCase__						)
					return features






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Optional[int]			    =		MobileNetVaConfig
			_A      :		Optional[int]			    =		load_tf_weights_in_mobilenet_va
			_A      :		Tuple			    =		'''mobilenet_v1'''
			_A      :		Tuple			    =		'''pixel_values'''
			_A      :		Optional[Any]			    =		False

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Union[nn.Linear, nn.Convad]						):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,     (nn.Linear, nn.Convad)						):
							module.weight.data.normal_(mean=0.0							,     std=self.config.initializer_range						)
							if module.bias is not None:
									module.bias.data.zero_()
					elif isinstance(lowerCAmelCase__							,     nn.BatchNormad						):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0						)
UpperCamelCase__       :	int    = R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

UpperCamelCase__       :	Any    = R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''






@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''    ,					lowerCamelCase__    ,					)
class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def __init__(							self     : Optional[int]							,     lowerCAmelCase__     : MobileNetVaConfig							,     lowerCAmelCase__     : bool = True						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		config

					__SCREAMING_SNAKE_CASE       :		Any           =		3_2
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		max(int(depth * config.depth_multiplier						)							,     config.min_depth						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		MobileNetVaConvLayer(
					    lowerCAmelCase__							,     in_channels=config.num_channels							,     out_channels=lowerCAmelCase__							,     kernel_size=3							,     stride=2							,     )

					__SCREAMING_SNAKE_CASE       :		Dict           =		[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

					__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.ModuleList()
					for i in range(1_3						):
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		out_channels

							if strides[i] == 2 or i == 0:
									depth *= 2
									__SCREAMING_SNAKE_CASE       :		int           =		max(int(depth * config.depth_multiplier						)							,     config.min_depth						)

							self.layer.append(
							    MobileNetVaConvLayer(
							        lowerCAmelCase__							,     in_channels=lowerCAmelCase__							,     out_channels=lowerCAmelCase__							,     kernel_size=3							,     stride=strides[i]							,     groups=lowerCAmelCase__							,     )						)

							self.layer.append(
							    MobileNetVaConvLayer(
							        lowerCAmelCase__							,     in_channels=lowerCAmelCase__							,     out_channels=lowerCAmelCase__							,     kernel_size=1							,     )						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.AdaptiveAvgPoolad((1, 1)						) if add_pooling_layer else None

					# Initialize weights and apply final processing
					self.post_init()

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					raise NotImplementedError



			@add_start_docstrings_to_model_forward(lowerCAmelCase__						)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC							,     output_type=lowerCAmelCase__							,     config_class=_CONFIG_FOR_DOC							,     modality="""vision"""							,     expected_output=_EXPECTED_OUTPUT_SHAPE							,     )
			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Optional[torch.Tensor] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		return_dict if return_dict is not None else self.config.use_return_dict

					if pixel_values is None:
							raise ValueError("""You have to specify pixel_values"""						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.conv_stem(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		str           =		() if output_hidden_states else None

					for i, layer_module in enumerate(self.layer						):
							__SCREAMING_SNAKE_CASE       :		Any           =		layer_module(lowerCAmelCase__						)

							if output_hidden_states:
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		all_hidden_states + (hidden_states,)

					__SCREAMING_SNAKE_CASE       :		str           =		hidden_states

					if self.pooler is not None:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		torch.flatten(self.pooler(lowerCAmelCase__						)							,     start_dim=1						)
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		None

					if not return_dict:
							return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None						)

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=lowerCAmelCase__							,     pooler_output=lowerCAmelCase__							,     hidden_states=lowerCAmelCase__							,     )






@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''    ,					lowerCamelCase__    ,					)
class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def __init__(							self     : str							,     lowerCAmelCase__     : MobileNetVaConfig						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		config.num_labels
					__SCREAMING_SNAKE_CASE       :		int           =		MobileNetVaModel(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.mobilenet_va.layer[-1].convolution.out_channels

					# Classifier head
					__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Dropout(config.classifier_dropout_prob							,     inplace=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		nn.Linear(lowerCAmelCase__							,     config.num_labels						) if config.num_labels > 0 else nn.Identity()

					# Initialize weights and apply final processing
					self.post_init()



			@add_start_docstrings_to_model_forward(lowerCAmelCase__						)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT							,     output_type=lowerCAmelCase__							,     config_class=_CONFIG_FOR_DOC							,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,     )
			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Optional[torch.Tensor] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : Optional[torch.Tensor] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		return_dict if return_dict is not None else self.config.use_return_dict

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.mobilenet_va(lowerCAmelCase__							,     output_hidden_states=lowerCAmelCase__							,     return_dict=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		outputs.pooler_output if return_dict else outputs[1]

					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.classifier(self.dropout(lowerCAmelCase__						)						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		None
					if labels is not None:
							if self.config.problem_type is None:
									if self.num_labels == 1:
											__SCREAMING_SNAKE_CASE       :		List[str]           =		"""regression"""
									elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""single_label_classification"""
									else:
											__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""multi_label_classification"""

							if self.config.problem_type == "regression":
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		MSELoss()
									if self.num_labels == 1:
											__SCREAMING_SNAKE_CASE       :		Any           =		loss_fct(logits.squeeze()							,     labels.squeeze()						)
									else:
											__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		loss_fct(lowerCAmelCase__							,     lowerCAmelCase__						)
							elif self.config.problem_type == "single_label_classification":
									__SCREAMING_SNAKE_CASE       :		List[str]           =		CrossEntropyLoss()
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		loss_fct(logits.view(-1							,     self.num_labels						)							,     labels.view(-1						)						)
							elif self.config.problem_type == "multi_label_classification":
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		BCEWithLogitsLoss()
									__SCREAMING_SNAKE_CASE       :		Tuple           =		loss_fct(lowerCAmelCase__							,     lowerCAmelCase__						)

					if not return_dict:
							__SCREAMING_SNAKE_CASE       :		Any           =		(logits,) + outputs[2:]
							return ((loss,) + output) if loss is not None else output

					return ImageClassifierOutputWithNoAttention(
					    loss=lowerCAmelCase__							,     logits=lowerCAmelCase__							,     hidden_states=outputs.hidden_states							,     )




'''simple docstring'''




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__       :	List[Any]    = {
    '''E''': 1_2.7_0,
    '''T''': 9.0_6,
    '''A''': 8.1_7,
    '''O''': 7.5_1,
    '''I''': 6.9_7,
    '''N''': 6.7_5,
    '''S''': 6.3_3,
    '''H''': 6.0_9,
    '''R''': 5.9_9,
    '''D''': 4.2_5,
    '''L''': 4.0_3,
    '''C''': 2.7_8,
    '''U''': 2.7_6,
    '''M''': 2.4_1,
    '''W''': 2.3_6,
    '''F''': 2.2_3,
    '''G''': 2.0_2,
    '''Y''': 1.9_7,
    '''P''': 1.9_3,
    '''B''': 1.2_9,
    '''V''': 0.9_8,
    '''K''': 0.7_7,
    '''J''': 0.1_5,
    '''X''': 0.1_5,
    '''Q''': 0.1_0,
    '''Z''': 0.0_7,
}
UpperCamelCase__       :	Optional[Any]    = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
UpperCamelCase__       :	Dict    = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		int           =		{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 				lowerCAmelCase_   (	_lowerCamelCase:   tuple					):
		return x[0]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		get_letter_count(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		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(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		dict[int, str]           =		{}

		for freq in freq_to_letter:
				freq_to_letter[freq].sort(key=ETAOIN.find       ,      reverse=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		"""""".join(freq_to_letter[freq]					)

		__SCREAMING_SNAKE_CASE       :		str           =		list(freq_to_letter_str.items()					)
		freq_pairs.sort(key=_lowerCamelCase       ,      reverse=_lowerCamelCase					)

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

		return "".join(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_frequency_order(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, 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()