code: 
  
  
  import json
  import os
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from contextlib import AbstractContextManager
  from typing import Dict, List, Optional
  
  import requests
  
  from ..utils import logging
  from . import BaseTransformersCLICommand
  
  
  A_    :					Dict             =logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  A_    :					int             ="""lfs-multipart-upload"""
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		parser.add_parser(
  													    """lfs-enable-largefiles"""			,    help=(
  													        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
  													    )			,    )
  													enable_parser.add_argument("""path"""			,    type=_UpperCAmelCase			,    help="""Local path to repository you want to configure."""		)
  													enable_parser.set_defaults(func=lambda _UpperCAmelCase		: LfsEnableCommand(_UpperCAmelCase		)		)
  
  													a_					=		parser.add_parser(
  													    _UpperCAmelCase			,    help=(
  													        """Deprecated: use `huggingface-cli` instead. """
  													        """Command will get called by git-lfs, do not call it directly."""
  													    )			,    )
  													upload_parser.set_defaults(func=lambda _UpperCAmelCase		: LfsUploadCommand(_UpperCAmelCase		)		)
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		args
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													warnings.warn(
  													    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""		)
  													a_					=		os.path.abspath(self.args.path		)
  													if not os.path.isdir(_UpperCAmelCase		):
  																			print("""This does not look like a valid git repo."""		)
  																			exit(1		)
  													subprocess.run(
  													    """git config lfs.customtransfer.multipart.path transformers-cli""".split()			,    check=_UpperCAmelCase			,    cwd=_UpperCAmelCase		)
  													subprocess.run(
  													    f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()			,    check=_UpperCAmelCase			,    cwd=_UpperCAmelCase			,    )
  													print("""Local repo set up for largefiles"""		)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		json.dumps(UpperCAmelCase__      ) + """\n"""
  						sys.stdout.write(UpperCAmelCase__      )
  						sys.stdout.flush()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		json.loads(sys.stdin.readline().strip()      )
  
  						if "terminate" in (msg.get("""type"""      ), msg.get("""event"""      )):
  												# terminate message received
  												return None
  
  						if msg.get("""event"""      ) not in ("download", "upload"):
  												logger.critical("""Received unexpected message"""      )
  												sys.exit(1      )
  
  						return msg
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		filepath
  													a_					=		seek_from
  													a_					=		read_limit
  													a_					=		0
  
  
  							def __enter__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		open(self.filepath			,    """rb"""		)
  													self.f.seek(self.seek_from		)
  													return self
  
  
  							def __len__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		os.fstat(self.f.fileno()		).st_size
  													return min(self.read_limit			,    total_length - self.seek_from		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=-1		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if self.n_seen >= self.read_limit:
  																			return b""
  													a_					=		self.read_limit - self.n_seen
  													a_					=		self.f.read(remaining_amount if n < 0 else min(_UpperCAmelCase			,    _UpperCAmelCase		)		)
  													self.n_seen += len(_UpperCAmelCase		)
  													return data
  
  
  							def __iter__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													yield self.read(n=4 * 1_024 * 1_024		)
  
  
  
  
  
  
  							def __exit__(   self			,    *_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													self.f.close()
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		args
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		json.loads(sys.stdin.readline().strip()		)
  													if not (init_msg.get("""event"""		) == "init" and init_msg.get("""operation"""		) == "upload"):
  																			write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}		)
  																			sys.exit(1		)
  
  													# The transfer process should use the information it needs from the
  													# initiation structure, and also perform any one-off setup tasks it
  													# needs to do. It should then respond on stdout with a simple empty
  													# confirmation structure, as follows:
  													write_msg({}		)
  
  													# After the initiation exchange, git-lfs will send any number of
  													# transfer requests to the stdin of the transfer process, in a serial sequence.
  													while True:
  																			a_					=		read_msg()
  																			if msg is None:
  																									# When all transfers have been processed, git-lfs will send
  																									# a terminate event to the stdin of the transfer process.
  																									# On receiving this message the transfer process should
  																									# clean up and terminate. No response is expected.
  																									sys.exit(0		)
  
  																			a_					=		msg["""oid"""]
  																			a_					=		msg["""path"""]
  																			a_					=		msg["""action"""]["""href"""]
  																			a_					=		msg["""action"""]["""header"""]
  																			a_					=		int(header.pop("""chunk_size"""		)		)
  																			a_					=		list(header.values()		)
  
  																			a_					=		[]
  																			for i, presigned_url in enumerate(_UpperCAmelCase		):
  																									with FileSlice(_UpperCAmelCase			,    seek_from=i * chunk_size			,    read_limit=_UpperCAmelCase		) as data:
  																															a_					=		requests.put(_UpperCAmelCase			,    data=_UpperCAmelCase		)
  																															r.raise_for_status()
  																															parts.append(
  																															    {
  																															        """etag""": r.headers.get("""etag"""		),
  																															        """partNumber""": i + 1,
  																															    }		)
  																															# In order to support progress reporting while data is uploading / downloading,
  																															# the transfer process should post messages to stdout
  																															write_msg(
  																															    {
  																															        """event""": """progress""",
  																															        """oid""": oid,
  																															        """bytesSoFar""": (i + 1) * chunk_size,
  																															        """bytesSinceLast""": chunk_size,
  																															    }		)
  																															# Not precise but that's ok.
  
  																			a_					=		requests.post(
  																			    _UpperCAmelCase			,    json={
  																			        """oid""": oid,
  																			        """parts""": parts,
  																			    }			,    )
  																			r.raise_for_status()
  
  																			write_msg({"""event""": """complete""", """oid""": oid}		)
style_context: 
  
  
  from collections.abc import Callable
  from math import log
  from os import name, system
  from random import gauss, seed
  from typing import TypeVar
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						seed(1      )
  						return [gauss(UpperCAmelCase__       ,    UpperCAmelCase__      ) for _ in range(UpperCAmelCase__      )]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return [k for k in range(UpperCAmelCase__      ) for _ in range(instance_count[k]      )]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return sum(UpperCAmelCase__      ) / instance_count
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return instance_count / total_count
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]  # An empty list to store all squared differences
  						# iterate over number of elements in items
  						for i in range(len(UpperCAmelCase__      )      ):
  												# for loop iterates over number of elements in inner layer of items
  												for j in range(len(items[i]      )      ):
  																		# appending squared differences to 'squared_diff' list
  																		squared_diff.append((items[i][j] - means[i]) ** 2      )
  
      # one divided by (the number of all instances - number of classes) multiplied by
      # sum of all squared differences
  						a_					=		len(UpperCAmelCase__      )  # Number of classes in dataset
  						return 1 / (total_count - n_classes) * sum(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]
  						# for loop iterates over number of elements in list
  						for i in range(len(UpperCAmelCase__      )      ):
  												# for loop iterates over number of inner items of each element
  												for j in range(len(x_items[i]      )      ):
  																		a_					=		[]  # to store all discriminant values of each item as a list
  																		# for loop iterates over number of classes we have in our dataset
  																		for k in range(len(UpperCAmelCase__      )      ):
  																								# appending values of discriminants for each class to 'temp' list
  																								temp.append(
  																								    x_items[i][j] * (means[k] / variance)
  																								    - (means[k] ** 2 / (2 * variance))
  																								    + log(probabilities[k]      )      )
  																		# appending discriminant values of each item to 'results' list
  																		results.append(UpperCAmelCase__      )
  
  						return [result.index(max(UpperCAmelCase__      )      ) for result in results]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		sum(1 for i, j in zip(UpperCAmelCase__       ,    UpperCAmelCase__      ) if i == j      )
  						# percentage of accuracy equals to number of correct predictions divided by number
  						# of all data and multiplied by 100
  						return (correct / len(UpperCAmelCase__      )) * 100
  
  
  A_    :					Dict             =TypeVar("""num""")
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__ = lambda UpperCAmelCase__      : True       ,    UpperCAmelCase__ = None       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						while True:
  												try:
  																		a_					=		input_type(input(UpperCAmelCase__      ).strip() or default      )
  																		if condition(UpperCAmelCase__      ):
  																								return user_input
  																		else:
  																								print(F"{user_input}: {err_msg}"      )
  																								continue
  												except ValueError:
  																		print(
  																		    F"{user_input}: Incorrect input type, expected {input_type.__name__!r}"      )
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						while True:
  												print(""" Linear Discriminant Analysis """.center(50       ,    """*"""      )      )
  												print("""*""" * 50       ,    """\n"""      )
  												print("""First of all we should specify the number of classes that"""      )
  												print("""we want to generate as training dataset"""      )
  												# Trying to get number of classes
  												a_					=		valid_input(
  												    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : x > 0       ,    input_msg="""Enter the number of classes (Data Groupings): """       ,    err_msg="""Number of classes should be positive!"""       ,    )
  
  												print("""-""" * 100      )
  
  												# Trying to get the value of standard deviation
  												a_					=		valid_input(
  												    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : x >= 0       ,    input_msg=(
  												        """Enter the value of standard deviation"""
  												        """(Default value is 1.0 for all classes): """
  												    )       ,    err_msg="""Standard deviation should not be negative!"""       ,    default="""1.0"""       ,    )
  
  												print("""-""" * 100      )
  
  												# Trying to get number of instances in classes and theirs means to generate
  												# dataset
  												a_					=		[]  # An empty list to store instance counts of classes in dataset
  												for i in range(UpperCAmelCase__      ):
  																		a_					=		valid_input(
  																		    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : x > 0       ,    input_msg=(F"Enter The number of instances for class_{i+1}: ")       ,    err_msg="""Number of instances should be positive!"""       ,    )
  																		counts.append(UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# An empty list to store values of user-entered means of classes
  												a_					=		[]
  												for a in range(UpperCAmelCase__      ):
  																		a_					=		valid_input(
  																		    input_type=UpperCAmelCase__       ,    input_msg=(F"Enter the value of mean for class_{a+1}: ")       ,    err_msg="""This is an invalid value."""       ,    )
  																		user_means.append(UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												print("""Standard deviation: """       ,    UpperCAmelCase__      )
  												# print out the number of instances in classes in separated line
  												for i, count in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Number of instances in class_{i} is: {count}"      )
  												print("""-""" * 100      )
  
  												# print out mean values of classes separated line
  												for i, user_mean in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Mean of class_{i} is: {user_mean}"      )
  												print("""-""" * 100      )
  
  												# Generating training dataset drawn from gaussian distribution
  												a_					=		[
  												    gaussian_distribution(user_means[j]       ,    UpperCAmelCase__       ,    counts[j]      )
  												    for j in range(UpperCAmelCase__      )
  												]
  												print("""Generated Normal Distribution: \n"""       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Generating Ys to detecting corresponding classes
  												a_					=		y_generator(UpperCAmelCase__       ,    UpperCAmelCase__      )
  												print("""Generated Corresponding Ys: \n"""       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Calculating the value of actual mean for each class
  												a_					=		[calculate_mean(counts[k]       ,    x[k]      ) for k in range(UpperCAmelCase__      )]
  												# for loop iterates over number of elements in 'actual_means' list and print
  												# out them in separated line
  												for i, actual_mean in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Actual(Real) mean of class_{i} is: {actual_mean}"      )
  												print("""-""" * 100      )
  
  												# Calculating the value of probabilities for each class
  												a_					=		[
  												    calculate_probabilities(counts[i]       ,    sum(UpperCAmelCase__      )      ) for i in range(UpperCAmelCase__      )
  												]
  
  												# for loop iterates over number of elements in 'probabilities' list and print
  												# out them in separated line
  												for i, probability in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Probability of class_{i} is: {probability}"      )
  												print("""-""" * 100      )
  
  												# Calculating the values of variance for each class
  												a_					=		calculate_variance(UpperCAmelCase__       ,    UpperCAmelCase__       ,    sum(UpperCAmelCase__      )      )
  												print("""Variance: """       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Predicting Y values
  												# storing predicted Y values in 'pre_indexes' variable
  												a_					=		predict_y_values(UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Calculating Accuracy of the model
  												print(F"Accuracy: {accuracy(UpperCAmelCase__       ,    UpperCAmelCase__      )}"      )
  												print("""-""" * 100      )
  												print(""" DONE """.center(100       ,    """+"""      )      )
  
  												if input("""Press any key to restart or 'q' for quit: """      ).strip().lower() == "q":
  																		print("""\n""" + """GoodBye!""".center(100       ,    """-"""      ) + """\n"""      )
  																		break
  												system("""cls""" if name == """nt""" else """clear"""      )  # noqa: S605
  
  
  if __name__ == "__main__":
  	main()