




import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
 import jax.numpy as jnp







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __get__(				self					:  Any						,							A_					:  List[str]						,							A_					:  Optional[int]=None  )   ->	str:

       """simple docstring"""






       if obj is None:
             return self
       if self.fget is None:
             raise AttributeError('unreadable attribute'  )
       lowerCamelCase_		     =    '__cached_' + self.fget.__name__
       lowerCamelCase_		     =    getattr(A_						,							A_						,							A_  )
       if cached is None:
             lowerCamelCase_		     =    self.fget(A_  )
             setattr(A_						,							A_						,							A_  )
       return cached

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    val.lower()
      if val in {"y", "yes", "t", "true", "on", "1"}:
            return 1
      if val in {"n", "no", "f", "false", "off", "0"}:
            return 0
      raise ValueError(f"""invalid truth value {val!r}"""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]     ):


      '''simple docstring'''



      if is_torch_fx_proxy(lowercase     ):
            return True
      if is_torch_available():
            import torch

            if isinstance(lowercase	,							torch.Tensor     ):
                  return True
      if is_tf_available():
            import tensorflow as tf

            if isinstance(lowercase	,							tf.Tensor     ):
                  return True

      if is_flax_available():
            import jax.numpy as jnp
            from jax.core import Tracer

            if isinstance(lowercase	,							(jnp.ndarray, Tracer)     ):
                  return True

      return isinstance(lowercase	,							np.ndarray     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int     ):


      '''simple docstring'''



      return isinstance(lowercase	,							np.ndarray     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return _is_numpy(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      import torch

      return isinstance(lowercase	,							torch.Tensor     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return False if not is_torch_available() else _is_torch(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      import torch

      return isinstance(lowercase	,							torch.device     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



      return False if not is_torch_available() else _is_torch_device(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      import torch

      if isinstance(lowercase	,							lowercase     ):
            if hasattr(lowercase	,							lowercase     ):
                  lowerCamelCase_		     =    getattr(lowercase	,							lowercase     )
            else:
                  return False
      return isinstance(lowercase	,							torch.dtype     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      return False if not is_torch_available() else _is_torch_dtype(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]     ):


      '''simple docstring'''



      import tensorflow as tf

      return isinstance(lowercase	,							tf.Tensor     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



      return False if not is_tf_available() else _is_tensorflow(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      import tensorflow as tf

      # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
      if hasattr(lowercase	,							'is_symbolic_tensor'     ):
            return tf.is_symbolic_tensor(lowercase     )
      return type(lowercase     ) == tf.Tensor

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      return False if not is_tf_available() else _is_tf_symbolic_tensor(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      import jax.numpy as jnp  # noqa: F811

      return isinstance(lowercase	,							jnp.ndarray     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



      return False if not is_flax_available() else _is_jax(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



      if isinstance(lowercase	,							(dict, UserDict)     ):
            return {k: to_py_obj(lowercase     ) for k, v in obj.items()}
      elif isinstance(lowercase	,							(list, tuple)     ):
            return [to_py_obj(lowercase     ) for o in obj]
      elif is_tf_tensor(lowercase     ):
            return obj.numpy().tolist()
      elif is_torch_tensor(lowercase     ):
            return obj.detach().cpu().tolist()
      elif is_jax_tensor(lowercase     ):
            return np.asarray(lowercase     ).tolist()
      elif isinstance(lowercase	,							(np.ndarray, np.number)     ):  # tolist also works on 0d np arrays
            return obj.tolist()
      else:
            return obj

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      if isinstance(lowercase	,							(dict, UserDict)     ):
            return {k: to_numpy(lowercase     ) for k, v in obj.items()}
      elif isinstance(lowercase	,							(list, tuple)     ):
            return np.array(lowercase     )
      elif is_tf_tensor(lowercase     ):
            return obj.numpy()
      elif is_torch_tensor(lowercase     ):
            return obj.detach().cpu().numpy()
      elif is_jax_tensor(lowercase     ):
            return np.asarray(lowercase     )
      else:
            return obj







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Tuple  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    fields(self  )

       # Safety and consistency checks
       if not len(A_  ):
             raise ValueError(f"""{self.__class__.__name__} has no fields."""  )
       if not all(field.default is None for field in class_fields[1:]  ):
             raise ValueError(f"""{self.__class__.__name__} should not have more than one required field."""  )

       lowerCamelCase_		     =    getattr(self						,							class_fields[0].name  )
       lowerCamelCase_		     =    all(getattr(self						,							field.name  ) is None for field in class_fields[1:]  )

       if other_fields_are_none and not is_tensor(A_  ):
             if isinstance(A_						,							A_  ):
                   lowerCamelCase_		     =    first_field.items()
                   lowerCamelCase_		     =    True
             else:
                   try:
                         lowerCamelCase_		     =    iter(A_  )
                         lowerCamelCase_		     =    True
                   except TypeError:
                         lowerCamelCase_		     =    False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
             if first_field_iterator:
                   for idx, element in enumerate(A_  ):
                         if (
                             not isinstance(A_						,							(list, tuple)  )
                             or not len(A_  ) == 2
                             or not isinstance(element[0]						,							A_  )
                         ):
                               if idx == 0:
                                     # If we do not have an iterator of key/values, set it as attribute
                                     lowerCamelCase_		     =    first_field
                               else:
                                     # If we have a mixed iterator, raise an error
                                     raise ValueError(
                                         f"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""  )
                               break
                         setattr(self						,							element[0]						,							element[1]  )
                         if element[1] is not None:
                               lowerCamelCase_		     =    element[1]
             elif first_field is not None:
                   lowerCamelCase_		     =    first_field
       else:
             for field in class_fields:
                   lowerCamelCase_		     =    getattr(self						,							field.name  )
                   if v is not None:
                         lowerCamelCase_		     =    v

 def __delitem__(				self					:  Tuple						,							*A_					:  Union[str, Any]						,							**A_					:  Optional[int]  )   ->	Optional[Any]:

       """simple docstring"""






       raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""  )

 def 				a__ (				self					:  List[Any]						,							*A_					:  int						,							**A_					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""  )

 def 				a__ (				self					:  List[Any]						,							*A_					:  int						,							**A_					:  str  )   ->	str:

       """simple docstring"""






       raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""  )

 def 				a__ (				self					:  Optional[int]						,							*A_					:  Dict						,							**A_					:  Tuple  )   ->	Tuple:

       """simple docstring"""






       raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""  )

 def __getitem__(				self					:  Optional[Any]						,							A_					:  int  )   ->	List[str]:

       """simple docstring"""






       if isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    dict(self.items()  )
             return inner_dict[k]
       else:
             return self.to_tuple()[k]

 def __setattr__(				self					:  List[str]						,							A_					:  Any						,							A_					:  Optional[int]  )   ->	Union[str, Any]:

       """simple docstring"""






       if name in self.keys() and value is not None:
             # Don't call self.__setitem__ to avoid recursion errors
             super().__setitem__(A_						,							A_  )
       super().__setattr__(A_						,							A_  )

 def __setitem__(				self					:  Union[str, Any]						,							A_					:  Union[str, Any]						,							A_					:  Optional[Any]  )   ->	List[str]:

       """simple docstring"""






       super().__setitem__(A_						,							A_  )
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(A_						,							A_  )





 def 				a__ (				self					:  Optional[int]  )   ->	Tuple[Any]:

       """simple docstring"""






       return tuple(self[k] for k in self.keys()  )







class   A(						UpperCamelCase							,      UpperCamelCase   ):



 '''simple docstring'''


 @classmethod
 def 				a__ (				cls					:  Optional[Any]						,							A_					:  Optional[Any]  )   ->	List[str]:

       """simple docstring"""






       raise ValueError(
           f"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()  )}"""  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''longest'''
 UpperCamelCase						   =						'''max_length'''
 UpperCamelCase						   =						'''do_not_pad'''







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''pt'''
 UpperCamelCase						   =						'''tf'''
 UpperCamelCase						   =						'''np'''
 UpperCamelCase						   =						'''jax'''







class   A:



 '''simple docstring'''


 def __init__(				self					:  Union[str, Any]						,							A_					:  List[ContextManager]  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    context_managers
       lowerCamelCase_		     =    ExitStack()

 def __enter__(				self					:  int  )   ->	List[Any]:

       """simple docstring"""






       for context_manager in self.context_managers:
             self.stack.enter_context(A_  )





 def __exit__(				self					:  Tuple						,							*A_					:  Optional[int]						,							**A_					:  Union[str, Any]  )   ->	str:

       """simple docstring"""






       self.stack.__exit__(*A_						,							**A_  )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    infer_framework(lowercase     )
      if framework == "tf":
            lowerCamelCase_		     =    inspect.signature(model_class.call     )  # TensorFlow models
      elif framework == "pt":
            lowerCamelCase_		     =    inspect.signature(model_class.forward     )  # PyTorch models
      else:
            lowerCamelCase_		     =    inspect.signature(model_class.__call__     )  # Flax models

      for p in signature.parameters:
            if p == "return_loss" and signature.parameters[p].default is True:
                  return True

      return False

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Any     ):


      '''simple docstring'''



      lowerCamelCase_		     =    model_class.__name__
      lowerCamelCase_		     =    infer_framework(lowercase     )
      if framework == "tf":
            lowerCamelCase_		     =    inspect.signature(model_class.call     )  # TensorFlow models
      elif framework == "pt":
            lowerCamelCase_		     =    inspect.signature(model_class.forward     )  # PyTorch models
      else:
            lowerCamelCase_		     =    inspect.signature(model_class.__call__     )  # Flax models

      if "QuestionAnswering" in model_name:
            return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
      else:
            return [p for p in signature.parameters if "label" in p]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       MutableMapping	,							lowercase	:       str = ""	,							lowercase	:       str = "."     ):


      '''simple docstring'''



      def _flatten_dict(lowercase	:       List[str]	,							lowercase	:       Optional[int]=""	,							lowercase	:       Optional[Any]="."     ):
            for k, v in d.items():
                  lowerCamelCase_		     =    str(lowercase     ) + delimiter + str(lowercase     ) if parent_key else k
                  if v and isinstance(lowercase	,							lowercase     ):
                        yield from flatten_dict(lowercase	,							lowercase	,							delimiter=lowercase     ).items()
                  else:
                        yield key, v

      return dict(_flatten_dict(lowercase	,							lowercase	,							lowercase     )     )

@contextmanager
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       bool = False     ):


      '''simple docstring'''



      if use_temp_dir:
            with tempfile.TemporaryDirectory() as tmp_dir:
                  yield tmp_dir
      else:
            yield working_dir

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       int=None     ):


      '''simple docstring'''



      if is_numpy_array(lowercase     ):
            return np.transpose(lowercase	,							axes=lowercase     )
      elif is_torch_tensor(lowercase     ):
            return array.T if axes is None else array.permute(*lowercase     )
      elif is_tf_tensor(lowercase     ):
            import tensorflow as tf

            return tf.transpose(lowercase	,							perm=lowercase     )
      elif is_jax_tensor(lowercase     ):
            return jnp.transpose(lowercase	,							axes=lowercase     )
      else:
            raise ValueError(f"""Type not supported for transpose: {type(lowercase     )}."""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]	,							lowercase	:       int     ):


      '''simple docstring'''



      if is_numpy_array(lowercase     ):
            return np.reshape(lowercase	,							lowercase     )
      elif is_torch_tensor(lowercase     ):
            return array.reshape(*lowercase     )
      elif is_tf_tensor(lowercase     ):
            import tensorflow as tf

            return tf.reshape(lowercase	,							lowercase     )
      elif is_jax_tensor(lowercase     ):
            return jnp.reshape(lowercase	,							lowercase     )
      else:
            raise ValueError(f"""Type not supported for reshape: {type(lowercase     )}."""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       Union[str, Any]=None     ):


      '''simple docstring'''



      if is_numpy_array(lowercase     ):
            return np.squeeze(lowercase	,							axis=lowercase     )
      elif is_torch_tensor(lowercase     ):
            return array.squeeze() if axis is None else array.squeeze(dim=lowercase     )
      elif is_tf_tensor(lowercase     ):
            import tensorflow as tf

            return tf.squeeze(lowercase	,							axis=lowercase     )
      elif is_jax_tensor(lowercase     ):
            return jnp.squeeze(lowercase	,							axis=lowercase     )
      else:
            raise ValueError(f"""Type not supported for squeeze: {type(lowercase     )}."""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]	,							lowercase	:       List[str]     ):


      '''simple docstring'''



      if is_numpy_array(lowercase     ):
            return np.expand_dims(lowercase	,							lowercase     )
      elif is_torch_tensor(lowercase     ):
            return array.unsqueeze(dim=lowercase     )
      elif is_tf_tensor(lowercase     ):
            import tensorflow as tf

            return tf.expand_dims(lowercase	,							axis=lowercase     )
      elif is_jax_tensor(lowercase     ):
            return jnp.expand_dims(lowercase	,							axis=lowercase     )
      else:
            raise ValueError(f"""Type not supported for expand_dims: {type(lowercase     )}."""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



      if is_numpy_array(lowercase     ):
            return np.size(lowercase     )
      elif is_torch_tensor(lowercase     ):
            return array.numel()
      elif is_tf_tensor(lowercase     ):
            import tensorflow as tf

            return tf.size(lowercase     )
      elif is_jax_tensor(lowercase     ):
            return array.size
      else:
            raise ValueError(f"""Type not supported for expand_dims: {type(lowercase     )}."""     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       int     ):


      '''simple docstring'''



      for key, value in auto_map.items():
            if isinstance(lowercase	,							(tuple, list)     ):
                  lowerCamelCase_		     =    [f"""{repo_id}--{v}""" if (v is not None and '--' not in v) else v for v in value]
            elif value is not None and "--" not in value:
                  lowerCamelCase_		     =    f"""{repo_id}--{value}"""

      return auto_map






def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



      for base_class in inspect.getmro(lowercase     ):
            lowerCamelCase_		     =    base_class.__module__
            lowerCamelCase_		     =    base_class.__name__
            if module.startswith('tensorflow'     ) or module.startswith('keras'     ) or name == "TFPreTrainedModel":
                  return "tf"
            elif module.startswith('torch'     ) or name == "PreTrainedModel":
                  return "pt"
            elif module.startswith('flax'     ) or module.startswith('jax'     ) or name == "FlaxPreTrainedModel":
                  return "flax"
      else:
            raise TypeError(f"""Could not infer framework from class {model_class}."""     )









from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str = "laptop"     ):


      '''simple docstring'''



      lowerCamelCase_		     =    f"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase_		     =    {
          'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
          'Accept-Language': 'en-US, en;q=0.5',
      }
      lowerCamelCase_		     =    BeautifulSoup(requests.get(lowercase	,							headers=lowercase     ).text     )
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase_		     =    DataFrame(
          columns=[
              'Product Title',
              'Product Link',
              'Current Price of the product',
              'Product Rating',
              'MRP of the product',
              'Discount',
          ]     )
      # Loop through each entry and store them in the dataframe
      for item, _ in zip_longest(
          soup.find_all(
              'div'	,							attrs={'class': 's-result-item', 'data-component-type': 's-search-result'}	,							)	,							soup.find_all('div'	,							attrs={'class': 'a-row a-size-base a-color-base'}     )	,							):
            try:
                  lowerCamelCase_		     =    item.ha.text
                  lowerCamelCase_		     =    'https://www.amazon.in/' + item.ha.a['href']
                  lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-offscreen'}     ).text
                  try:
                        lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-icon-alt'}     ).text
                  except AttributeError:
                        lowerCamelCase_		     =    'Not available'
                  try:
                        lowerCamelCase_		     =    (
                            '₹'
                            + item.find(
                                'span'	,							attrs={'class': 'a-price a-text-price'}     ).text.split('₹'     )[1]
                        )
                  except AttributeError:
                        lowerCamelCase_		     =    ''
                  try:
                        lowerCamelCase_		     =    float(
                            (
                                (
                                    float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                                    - float(product_price.strip('₹'     ).replace(','	,							''     )     )
                                )
                                / float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                            )
                            * 1_00     )
                  except ValueError:
                        lowerCamelCase_		     =    float('nan'     )
            except AttributeError:
                  pass
            lowerCamelCase_		     =    [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase_		     =    ' '
      lowerCamelCase_		     =    ' '
      data_frame.index += 1
      return data_frame


if __name__ == "__main__":
 lowerCamelCase				:		Tuple     =   "headphones"
 get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



