from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


UpperCAmelCase_           =				logging.get_logger(__name__)




@dataclass
class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]



   def __init__(						self       :	Optional[int]		,	**_lowercase       :	Dict				):



    """simple docstring"""



    for deprecated_arg in self.deprecated_args:
     if deprecated_arg in kwargs:
      _UpperCamelCase:  Optional[int]    						=			deprecated_arg[3:]
      _UpperCamelCase:  List[str]    						=			not kwargs.pop(_lowercase				)
      logger.warning(
          f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
          f""" {positive_arg}={kwargs[positive_arg]}"""				)
    _UpperCamelCase:  Any    						=			kwargs.pop('''tpu_name'''		,	self.tpu_name				)
    _UpperCamelCase:  List[Any]    						=			kwargs.pop('''device_idx'''		,	self.device_idx				)
    _UpperCamelCase:  Union[str, Any]    						=			kwargs.pop('''eager_mode'''		,	self.eager_mode				)
    _UpperCamelCase:  Dict    						=			kwargs.pop('''use_xla'''		,	self.use_xla				)
    super().__init__(**_lowercase				)

   lowerCAmelCase   :     str	      = field(
       default=__a   ,						metadata={'''help''': '''Name of TPU'''}   ,						)
   lowerCAmelCase   :     int	      = field(
       default=0   ,						metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}   ,						)
   lowerCAmelCase   :     bool	      = field(default=__a   ,						metadata={'''help''': '''Benchmark models in eager model.'''}     )
   lowerCAmelCase   :     bool	      = field(
       default=__a   ,						metadata={
           '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
       }   ,						)



   @cached_property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    _UpperCamelCase:  Tuple    						=			None
    if self.tpu:
     try:
      if self.tpu_name:
       _UpperCamelCase:  Tuple    						=			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
      else:
       _UpperCamelCase:  int    						=			tf.distribute.cluster_resolver.TPUClusterResolver()
     except ValueError:
      _UpperCamelCase:  List[Any]    						=			None
    return tpu



   @cached_property
   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    if self.is_tpu:
     tf.config.experimental_connect_to_cluster(self._setup_tpu				)
     tf.tpu.experimental.initialize_tpu_system(self._setup_tpu				)

     _UpperCamelCase:  Optional[Any]    						=			tf.distribute.TPUStrategy(self._setup_tpu				)
    else:
     # currently no multi gpu is allowed
     if self.is_gpu:
      # TODO: Currently only single GPU is supported
      tf.config.set_visible_devices(self.gpu_list[self.device_idx]		,	'''GPU'''				)
      _UpperCamelCase:  Any    						=			tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""				)
     else:
      tf.config.set_visible_devices([]		,	'''GPU'''				)  # disable GPU
      _UpperCamelCase:  str    						=			tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""				)

    return strategy



   @property
   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    return self._setup_tpu is not None



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    return self._setup_strategy



   @property
   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    return tf.config.list_physical_devices('''GPU'''				)



   @property
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    requires_backends(self		,	['''tf''']				)
    if self.cuda:
     return len(self.gpu_list				)
    return 0



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return self.n_gpu > 0
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()