



'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowerCamelCase      :			List[Any]       =      2_9979_2458

# Symbols
_lowerCamelCase      :			Optional[Any]       =      symbols("ct x y z")







def 							__lowerCamelCase							(  A__       )			->      float:






   """simple docstring"""





   if velocity > c:
      raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!'       )
   elif velocity < 1:
      # Usually the speed should be much higher than 1 (c order of magnitude)
      raise ValueError('Speed must be greater than or equal to 1!'       )

   return velocity / c







def 							__lowerCamelCase							(  A__       )			->      float:






   """simple docstring"""
   return 1 / sqrt(1 - beta(snake_case__       ) ** 2       )







def 							__lowerCamelCase							(  A__       )			->      np.ndarray:






   """simple docstring"""
   return np.array(
       [
           [gamma(snake_case__       ), -gamma(snake_case__       ) * beta(snake_case__       ), 0, 0],
           [-gamma(snake_case__       ) * beta(snake_case__       ), gamma(snake_case__       ), 0, 0],
           [0, 0, 1, 0],
           [0, 0, 0, 1],
       ]       )







def 							__lowerCamelCase							(  A__       ,		A__ = None       )			->      np.ndarray:






   """simple docstring"""





   if event is None:
      UpperCamelCase      							=    np.array([ct, x, y, z]       )  # Symbolic four vector
   else:
      event[0] *= c  # x0 is ct (speed of light * time)

   return transformation_matrix(snake_case__       ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _lowerCamelCase      :			List[str]       =      transform(2997_9245)
   print("Example of four vector: ")
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _lowerCamelCase      :			Optional[int]       =      {ct: c, x: 1, y: 1, z: 1}
   _lowerCamelCase      :			Tuple       =      [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')






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


_lowerCAmelCase	:   str    =   logging.get_logger(__name__)


# General docstring
_lowerCAmelCase	:   Optional[int]    =   "MobileNetV1Config"

# Base docstring
_lowerCAmelCase	:   Tuple    =   "google/mobilenet_v1_1.0_224"
_lowerCAmelCase	:   Optional[int]    =   [1, 1_0_2_4, 7, 7]

# Image classification docstring
_lowerCAmelCase	:   Tuple    =   "google/mobilenet_v1_1.0_224"
_lowerCAmelCase	:   List[Any]    =   "tabby, tabby cat"


_lowerCAmelCase	:   Tuple    =   [
    "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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=None					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  {}

				if isinstance(snake_case__    ,			snake_case__					):
								lowerCAmelCase__		  =  model.mobilenet_va
				else:
								lowerCAmelCase__		  =  model

				lowerCAmelCase__		  =  'MobilenetV1/Conv2d_0/'
				lowerCAmelCase__		  =  backbone.conv_stem.convolution.weight
				lowerCAmelCase__		  =  backbone.conv_stem.normalization.bias
				lowerCAmelCase__		  =  backbone.conv_stem.normalization.weight
				lowerCAmelCase__		  =  backbone.conv_stem.normalization.running_mean
				lowerCAmelCase__		  =  backbone.conv_stem.normalization.running_var

				for i in range(13					):
								lowerCAmelCase__		  =  i + 1
								lowerCAmelCase__		  =  i * 2

								lowerCAmelCase__		  =  backbone.layer[pt_index]
								lowerCAmelCase__		  =  f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
								lowerCAmelCase__		  =  pointer.convolution.weight
								lowerCAmelCase__		  =  pointer.normalization.bias
								lowerCAmelCase__		  =  pointer.normalization.weight
								lowerCAmelCase__		  =  pointer.normalization.running_mean
								lowerCAmelCase__		  =  pointer.normalization.running_var

								lowerCAmelCase__		  =  backbone.layer[pt_index + 1]
								lowerCAmelCase__		  =  f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
								lowerCAmelCase__		  =  pointer.convolution.weight
								lowerCAmelCase__		  =  pointer.normalization.bias
								lowerCAmelCase__		  =  pointer.normalization.weight
								lowerCAmelCase__		  =  pointer.normalization.running_mean
								lowerCAmelCase__		  =  pointer.normalization.running_var

				if isinstance(snake_case__    ,			snake_case__					):
								lowerCAmelCase__		  =  'MobilenetV1/Logits/Conv2d_1c_1x1/'
								lowerCAmelCase__		  =  model.classifier.weight
								lowerCAmelCase__		  =  model.classifier.bias

				return tf_to_pt_map







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[str]:



				"""simple docstring"""





				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
				lowerCAmelCase__		  =  tf.train.list_variables(snake_case__					)
				lowerCAmelCase__		  =  {}
				for name, shape in init_vars:
								logger.info(f'Loading TF weight {name} with shape {shape}'					)
								lowerCAmelCase__		  =  tf.train.load_variable(snake_case__    ,			snake_case__					)
								lowerCAmelCase__		  =  array

				# Build TF to PyTorch weights loading map
				lowerCAmelCase__		  =  _build_tf_to_pytorch_map(snake_case__    ,			snake_case__    ,			snake_case__					)

				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

								lowerCAmelCase__		  =  tf_weights[name]

								if "depthwise_weights" in name:
												logger.info('Transposing depthwise'					)
												lowerCAmelCase__		  =  np.transpose(snake_case__    ,			(2, 3, 0, 1)					)
								elif "weights" in name:
												logger.info('Transposing'					)
												if len(pointer.shape					) == 2:  # copying into linear layer
																lowerCAmelCase__		  =  array.squeeze().transpose()
												else:
																lowerCAmelCase__		  =  np.transpose(snake_case__    ,			(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}'					)
								lowerCAmelCase__		  =  torch.from_numpy(snake_case__					)

								tf_weights.pop(snake_case__    ,			snake_case__					)
								tf_weights.pop(name + '/RMSProp'    ,			snake_case__					)
								tf_weights.pop(name + '/RMSProp_1'    ,			snake_case__					)
								tf_weights.pop(name + '/ExponentialMovingAverage'    ,			snake_case__					)

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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     torch.Tensor:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  features.shape[-2:]
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  conv_layer.stride
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  conv_layer.kernel_size

				if in_height % stride_height == 0:
								lowerCAmelCase__		  =  max(kernel_height - stride_height    ,			0					)
				else:
								lowerCAmelCase__		  =  max(kernel_height - (in_height % stride_height)    ,			0					)

				if in_width % stride_width == 0:
								lowerCAmelCase__		  =  max(kernel_width - stride_width    ,			0					)
				else:
								lowerCAmelCase__		  =  max(kernel_width - (in_width % stride_width)    ,			0					)

				lowerCAmelCase__		  =  pad_along_width // 2
				lowerCAmelCase__		  =  pad_along_width - pad_left
				lowerCAmelCase__		  =  pad_along_height // 2
				lowerCAmelCase__		  =  pad_along_height - pad_top

				lowerCAmelCase__		  =  (pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(snake_case__    ,			snake_case__    ,			'constant'    ,			0.0					)


class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_    ,a_ = 1    ,a_ = 1    ,a_ = False    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  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.'						)

						lowerCAmelCase__		  =  0 if config.tf_padding else int((kernel_size - 1) / 2						)

						lowerCAmelCase__		  =  nn.Convad(
						    in_channels=a_    ,out_channels=a_    ,kernel_size=a_    ,stride=a_    ,padding=a_    ,groups=a_    ,bias=a_    ,padding_mode='zeros'    ,)

						if use_normalization:
										lowerCAmelCase__		  =  nn.BatchNormad(
										    num_features=a_    ,eps=config.layer_norm_eps    ,momentum=0.9997    ,affine=a_    ,track_running_stats=a_    ,)
						else:
										lowerCAmelCase__		  =  None

						if use_activation:
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  ACTaFN[use_activation]
										elif isinstance(config.hidden_act    ,a_						):
														lowerCAmelCase__		  =  ACTaFN[config.hidden_act]
										else:
														lowerCAmelCase__		  =  config.hidden_act
						else:
										lowerCAmelCase__		  =  None



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if self.config.tf_padding:
										lowerCAmelCase__		  =  apply_tf_padding(a_    ,self.convolution						)
						lowerCAmelCase__		  =  self.convolution(a_						)
						if self.normalization is not None:
										lowerCAmelCase__		  =  self.normalization(a_						)
						if self.activation is not None:
										lowerCAmelCase__		  =  self.activation(a_						)
						return features



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	MobileNetVaConfig
		SCREAMING_SNAKE_CASE__								=	load_tf_weights_in_mobilenet_va
		SCREAMING_SNAKE_CASE__								=	'mobilenet_v1'
		SCREAMING_SNAKE_CASE__								=	'pixel_values'
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if isinstance(a_    ,(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(a_    ,nn.BatchNormad						):
										module.bias.data.zero_()
										module.weight.data.fill_(1.0						)



_lowerCAmelCase	:   Tuple    =   R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

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


@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'							, SCREAMING_SNAKE_CASE							, )
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_    ,a_ = True						):







						"""simple docstring"""
						super().__init__(a_						)
						lowerCAmelCase__		  =  config

						lowerCAmelCase__		  =  32
						lowerCAmelCase__		  =  max(int(depth * config.depth_multiplier						)    ,config.min_depth						)

						lowerCAmelCase__		  =  MobileNetVaConvLayer(
						    a_    ,in_channels=config.num_channels    ,out_channels=a_    ,kernel_size=3    ,stride=2    ,)

						lowerCAmelCase__		  =  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						lowerCAmelCase__		  =  nn.ModuleList()
						for i in range(13						):
										lowerCAmelCase__		  =  out_channels

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

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

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

						lowerCAmelCase__		  =  nn.AdaptiveAvgPoolad((1, 1)						) if add_pooling_layer else None

						# Initialize weights and apply final processing
						self.post_init()
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						raise NotImplementedError



		@add_start_docstrings_to_model_forward(a_						)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC    ,output_type=a_    ,config_class=_CONFIG_FOR_DOC    ,modality='vision'    ,expected_output=_EXPECTED_OUTPUT_SHAPE    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None    ,a_ = None    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__		  =  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'						)

						lowerCAmelCase__		  =  self.conv_stem(a_						)

						lowerCAmelCase__		  =  () if output_hidden_states else None

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

										if output_hidden_states:
														lowerCAmelCase__		  =  all_hidden_states + (hidden_states,)

						lowerCAmelCase__		  =  hidden_states

						if self.pooler is not None:
										lowerCAmelCase__		  =  torch.flatten(self.pooler(a_						)    ,start_dim=1						)
						else:
										lowerCAmelCase__		  =  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=a_    ,pooler_output=a_    ,hidden_states=a_    ,)



@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							, SCREAMING_SNAKE_CASE							, )
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__(a_						)

						lowerCAmelCase__		  =  config.num_labels
						lowerCAmelCase__		  =  MobileNetVaModel(a_						)

						lowerCAmelCase__		  =  self.mobilenet_va.layer[-1].convolution.out_channels

						# Classifier head
						lowerCAmelCase__		  =  nn.Dropout(config.classifier_dropout_prob    ,inplace=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,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(a_						)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,output_type=a_    ,config_class=_CONFIG_FOR_DOC    ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						lowerCAmelCase__		  =  self.mobilenet_va(a_    ,output_hidden_states=a_    ,return_dict=a_						)

						lowerCAmelCase__		  =  outputs.pooler_output if return_dict else outputs[1]

						lowerCAmelCase__		  =  self.classifier(self.dropout(a_						)						)

						lowerCAmelCase__		  =  None
						if labels is not None:
										if self.config.problem_type is None:
														if self.num_labels == 1:
																		lowerCAmelCase__		  =  'regression'
														elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																		lowerCAmelCase__		  =  'single_label_classification'
														else:
																		lowerCAmelCase__		  =  'multi_label_classification'

										if self.config.problem_type == "regression":
														lowerCAmelCase__		  =  MSELoss()
														if self.num_labels == 1:
																		lowerCAmelCase__		  =  loss_fct(logits.squeeze()    ,labels.squeeze()						)
														else:
																		lowerCAmelCase__		  =  loss_fct(a_    ,a_						)
										elif self.config.problem_type == "single_label_classification":
														lowerCAmelCase__		  =  CrossEntropyLoss()
														lowerCAmelCase__		  =  loss_fct(logits.view(-1    ,self.num_labels						)    ,labels.view(-1						)						)
										elif self.config.problem_type == "multi_label_classification":
														lowerCAmelCase__		  =  BCEWithLogitsLoss()
														lowerCAmelCase__		  =  loss_fct(a_    ,a_						)

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

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