from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A  :	Optional[Any]         =							logging.get_logger(__name__)

A  :	Union[str, Any]         =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class 	lowerCamelCase       (UpperCAmelCase__       ):
  """simple docstring"""







  lowerCamelCase__  =    '''mobilenet_v1'''







  def __init__(				self			:							Optional[int]	,  __magic_name__			:							Optional[int]=3	,  __magic_name__			:							Optional[Any]=224	,  __magic_name__			:							Optional[int]=1.0	,  __magic_name__			:							Optional[int]=8	,  __magic_name__			:							Tuple="relu6"	,  __magic_name__			:							List[Any]=True	,  __magic_name__			:							Dict=0.999	,  __magic_name__			:							str=0.02	,  __magic_name__			:							Optional[int]=0.001	,  **__magic_name__			:							Dict	,  )	->						List[str]:
        super().__init__(**UpperCAmelCase__ )

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero." )

        SCREAMING_SNAKE_CASE_    =	num_channels
        SCREAMING_SNAKE_CASE_    =	image_size
        SCREAMING_SNAKE_CASE_    =	depth_multiplier
        SCREAMING_SNAKE_CASE_    =	min_depth
        SCREAMING_SNAKE_CASE_    =	hidden_act
        SCREAMING_SNAKE_CASE_    =	tf_padding
        SCREAMING_SNAKE_CASE_    =	classifier_dropout_prob
        SCREAMING_SNAKE_CASE_    =	initializer_range
        SCREAMING_SNAKE_CASE_    =	layer_norm_eps



class 	lowerCamelCase       (UpperCAmelCase__       ):
  """simple docstring"""







  lowerCamelCase__  =    version.parse('''1.11'''       )







  @property
  def  __A  (				self			:							Any )	->						Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})] )







  @property
  def  __A  (				self			:							List[str] )	->						Mapping[str, Mapping[int, str]]:

        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})] )
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )







  @property
  def  __A  (				self			:							int )	->						float:
        return 1e-4








from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowerCamelCase      :						str							=  299792458

# Symbols
_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase      :						int							=  symbols("""ct x y z""")







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

       return 1 / sqrt(1 - beta(lowercase_		) ** 2		)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			np.ndarray:



       """simple docstring"""

       return np.array(
           [
               [gamma(lowercase_		), -gamma(lowercase_		) * beta(lowercase_		), 0, 0],
               [-gamma(lowercase_		) * beta(lowercase_		), gamma(lowercase_		), 0, 0],
               [0, 0, 1, 0],
               [0, 0, 0, 1],
           ]		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = None		)  ->			np.ndarray:



       """simple docstring"""

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

       return transformation_matrix(lowercase_		) @ event


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Example of symbolic vector:
  _lowerCamelCase      :						Tuple							=  transform(29979245)
  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      :						int							=  {ct: c, x: 1, y: 1, z: 1}
  _lowerCamelCase      :						Any							=  [four_vector[i].subs(sub_dict) for i in range(4)]

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


