



'''simple docstring'''






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


UpperCamelCase__:     List[str]										=    get_logger(__name__)


UpperCamelCase__:     Tuple										=    r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	@add_start_docstrings(__snake_case	)
	def __call__(		self    :  Tuple	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	)    ->	jnp.ndarray:
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""	)

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	@add_start_docstrings(__snake_case	)
	def __call__(		self    :  Tuple	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	)    ->	jnp.ndarray:
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""	)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	@add_start_docstrings(__snake_case	)
	def __call__(		self    :  List[Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	,    **__snake_case    :  Optional[Any]	)    ->	jnp.ndarray:
								for processor in self:
															UpperCAmelCase				: List[Any]											=	inspect.signature(processor.__call__	).parameters
															if len(__snake_case	) > 3:
																						if not all(arg in kwargs for arg in list(function_args.keys()	)[2:]	):
																													raise ValueError(
																													    F"""Make sure that all the required parameters: {list(function_args.keys()	)} for """
																													    F"""{processor.__class__} are passed to the logits processor."""	)
																						UpperCAmelCase				: Union[str, Any]											=	processor(__snake_case	,    __snake_case	,    __snake_case	,    **__snake_case	)
															else:
																						UpperCAmelCase				: Dict											=	processor(__snake_case	,    __snake_case	,    __snake_case	)
								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  float	)    ->	List[str]:
								if not isinstance(__snake_case	,    __snake_case	) or not (temperature > 0):
															raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""	)

								UpperCAmelCase				: Optional[Any]											=	temperature







	def __call__(		self    :  Dict	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase				: Dict											=	scores / self.temperature
								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Any	,    __snake_case    :  float	,    __snake_case    :  float = -float('''Inf'''	)	,    __snake_case    :  int = 1	)    ->	int:
								if not isinstance(__snake_case	,    __snake_case	) or (top_p < 0 or top_p > 1.0):
															raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""	)
								if not isinstance(__snake_case	,    __snake_case	) or (min_tokens_to_keep < 1):
															raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""	)

								UpperCAmelCase				: List[Any]											=	top_p
								UpperCAmelCase				: Union[str, Any]											=	filter_value
								UpperCAmelCase				: str											=	min_tokens_to_keep







	def __call__(		self    :  Union[str, Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	lax.top_k(__snake_case	,    scores.shape[-1]	)

								UpperCAmelCase				: Union[str, Any]											=	jnp.full_like(__snake_case	,    self.filter_value	)
								UpperCAmelCase				: Union[str, Any]											=	jax.nn.softmax(__snake_case	,    axis=-1	).cumsum(axis=-1	)
								UpperCAmelCase				: Tuple											=	cumulative_probs < self.top_p

								# include the token that is higher than top_p as well
								UpperCAmelCase				: int											=	jnp.roll(__snake_case	,    1	)
								score_mask |= score_mask.at[:, 0].set(__snake_case	)

								# min tokens to keep
								UpperCAmelCase				: List[Any]											=	score_mask.at[:, : self.min_tokens_to_keep].set(__snake_case	)

								UpperCAmelCase				: List[Any]											=	jnp.where(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: List[str]											=	jax.lax.sort_key_val(__snake_case	,    __snake_case	)[-1]

								return next_scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  float = -float('''Inf'''	)	,    __snake_case    :  int = 1	)    ->	Optional[Any]:
								if not isinstance(__snake_case	,    __snake_case	) or top_k <= 0:
															raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""	)

								UpperCAmelCase				: List[str]											=	max(__snake_case	,    __snake_case	)
								UpperCAmelCase				: Dict											=	filter_value







	def __call__(		self    :  Optional[int]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase							,	UpperCAmelCase				: str											=	scores.shape
								UpperCAmelCase				: Optional[Any]											=	jnp.full(batch_size * vocab_size	,    self.filter_value	)

								UpperCAmelCase				: Tuple											=	min(self.top_k	,    scores.shape[-1]	)  # Safety check
								UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	lax.top_k(__snake_case	,    __snake_case	)
								UpperCAmelCase				: List[str]											=	jnp.broadcast_to((jnp.arange(__snake_case	) * vocab_size)[:, None]	,    (batch_size, topk)	).flatten()
								UpperCAmelCase				: Optional[int]											=	topk_scores.flatten()
								UpperCAmelCase				: Any											=	topk_indices.flatten() + shift

								UpperCAmelCase				: int											=	next_scores_flat.at[topk_indices_flat].set(__snake_case	)
								UpperCAmelCase				: Tuple											=	next_scores_flat.reshape(__snake_case	,    __snake_case	)
								return next_scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  int	)    ->	Tuple:
								UpperCAmelCase				: List[Any]											=	bos_token_id







	def __call__(		self    :  List[Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase				: Optional[int]											=	jnp.full(scores.shape	,    -float('''inf'''	)	)

								UpperCAmelCase				: List[Any]											=	1 - jnp.bool_(cur_len - 1	)

								UpperCAmelCase				: str											=	jnp.where(__snake_case	,    new_scores.at[:, self.bos_token_id].set(0	)	,    __snake_case	)

								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  int	,    __snake_case    :  int	)    ->	int:
								UpperCAmelCase				: List[str]											=	max_length
								UpperCAmelCase				: Any											=	eos_token_id







	def __call__(		self    :  Optional[Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase				: str											=	jnp.full(scores.shape	,    -float('''inf'''	)	)

								UpperCAmelCase				: Any											=	1 - jnp.bool_(cur_len - self.max_length + 1	)

								UpperCAmelCase				: List[str]											=	jnp.where(__snake_case	,    new_scores.at[:, self.eos_token_id].set(0	)	,    __snake_case	)

								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[str]	,    __snake_case    :  int	,    __snake_case    :  int	)    ->	int:
								if not isinstance(__snake_case	,    __snake_case	) or min_length < 0:
															raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""	)

								if not isinstance(__snake_case	,    __snake_case	) or eos_token_id < 0:
															raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""	)

								UpperCAmelCase				: List[str]											=	min_length
								UpperCAmelCase				: Optional[Any]											=	eos_token_id







	def __call__(		self    :  int	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								# create boolean flag to decide if min length penalty should be applied
								UpperCAmelCase				: List[Any]											=	1 - jnp.clip(cur_len - self.min_length	,    0	,    1	)

								UpperCAmelCase				: List[Any]											=	jnp.where(__snake_case	,    scores.at[:, self.eos_token_id].set(-float('''inf'''	)	)	,    __snake_case	)

								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  List[str]	,    __snake_case    :  int	)    ->	List[str]:
								UpperCAmelCase				: Optional[Any]											=	list(__snake_case	)
								UpperCAmelCase				: Tuple											=	begin_index







	def __call__(		self    :  Tuple	,    __snake_case    :  Dict	,    __snake_case    :  int	,    __snake_case    :  int	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	1 - jnp.bool_(cur_len - self.begin_index	)

								UpperCAmelCase				: str											=	jnp.where(__snake_case	,    scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''	)	)	,    __snake_case	)

								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  list	)    ->	List[str]:
								UpperCAmelCase				: Tuple											=	list(__snake_case	)







	def __call__(		self    :  Optional[Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								UpperCAmelCase				: int											=	scores.at[..., self.suppress_tokens].set(-float('''inf'''	)	)

								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  Tuple	)    ->	Any:
								UpperCAmelCase				: List[Any]											=	dict(__snake_case	)
								# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
								# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
								# Indexes without forced tokens will have a negative value.
								UpperCAmelCase				: List[str]											=	jnp.ones((max(force_token_map.keys()	) + 1)	,    dtype=jnp.intaa	) * -1
								for index, token in force_token_map.items():
															if token is not None:
																						UpperCAmelCase				: Optional[Any]											=	force_token_array.at[index].set(__snake_case	)
								UpperCAmelCase				: Any											=	jnp.intaa(__snake_case	)







	def __call__(		self    :  Optional[Any]	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  jnp.ndarray	,    __snake_case    :  int	)    ->	jnp.ndarray:
								def _force_token(__snake_case    :  Union[str, Any]	):
															UpperCAmelCase				: List[str]											=	scores.shape[0]
															UpperCAmelCase				: str											=	self.force_token_array[generation_idx]

															UpperCAmelCase				: Optional[Any]											=	jnp.ones_like(__snake_case	,    dtype=scores.dtype	) * -float('''inf'''	)
															UpperCAmelCase				: Tuple											=	jnp.zeros((batch_size, 1)	,    dtype=scores.dtype	)
															UpperCAmelCase				: str											=	lax.dynamic_update_slice(__snake_case	,    __snake_case	,    (0, current_token)	)
															return new_scores

								UpperCAmelCase				: Optional[int]											=	lax.cond(
								    cur_len >= self.force_token_array.shape[0]	,    lambda: scores	,    lambda: lax.cond(
								        self.force_token_array[cur_len] >= 0	,    lambda: _force_token(__snake_case	)	,    lambda: scores	,    )	,    )
								return scores

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  int	)    ->	str:
								UpperCAmelCase				: Optional[int]											=	generate_config.eos_token_id
								UpperCAmelCase				: Optional[int]											=	generate_config.no_timestamps_token_id
								UpperCAmelCase				: int											=	generate_config.no_timestamps_token_id + 1

								UpperCAmelCase				: Dict											=	decoder_input_length + 1

								if generate_config.is_multilingual:
															# room for language token and task token
															self.begin_index += 2
								if hasattr(__snake_case	,    '''max_initial_timestamp_index'''	):
															UpperCAmelCase				: str											=	generate_config.max_initial_timestamp_index
								else:
															UpperCAmelCase				: int											=	model_config.vocab_size
								if self.max_initial_timestamp_index is None:
															UpperCAmelCase				: Any											=	model_config.vocab_size







	def __call__(		self    :  Any	,    __snake_case    :  int	,    __snake_case    :  str	,    __snake_case    :  List[str]	)    ->	Dict:
								# suppress <|notimestamps|> which is handled by without_timestamps
								UpperCAmelCase				: Optional[Any]											=	scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''	)	)

								def handle_pairs(__snake_case    :  Dict	,    __snake_case    :  Dict	):
															UpperCAmelCase				: str											=	jnp.where((cur_len - self.begin_index) >= 1	,    __snake_case	,    __snake_case	)
															UpperCAmelCase				: List[str]											=	jnp.where(
															    input_ids_k[cur_len - 1] >= self.timestamp_begin	,    True and last_was_timestamp	,    __snake_case	,    )

															UpperCAmelCase				: Dict											=	jnp.where((cur_len - self.begin_index) < 2	,    __snake_case	,    __snake_case	)
															UpperCAmelCase				: Dict											=	jnp.where(
															    input_ids_k[cur_len - 2] >= self.timestamp_begin	,    __snake_case	,    __snake_case	,    )

															return jnp.where(
															    __snake_case	,    jnp.where(
															        penultimate_was_timestamp > 0	,    scores_k.at[self.timestamp_begin :].set(-float('''inf'''	)	)	,    scores_k.at[: self.eos_token_id].set(-float('''inf'''	)	)	,    )	,    __snake_case	,    )

								UpperCAmelCase				: Union[str, Any]											=	jax.vmap(__snake_case	)(__snake_case	,    __snake_case	)

								UpperCAmelCase				: int											=	jnp.where(cur_len == self.begin_index	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	jnp.where(
								    self.max_initial_timestamp_index is not None	,    True and apply_max_initial_timestamp	,    __snake_case	,    )

								UpperCAmelCase				: Tuple											=	self.timestamp_begin + self.max_initial_timestamp_index

								UpperCAmelCase				: List[Any]											=	jnp.where(
								    __snake_case	,    scores.at[:, last_allowed + 1 :].set(-float('''inf'''	)	)	,    __snake_case	,    )

								# if sum of probability over timestamps is above any other token, sample timestamp
								UpperCAmelCase				: Union[str, Any]											=	jax.nn.log_softmax(__snake_case	,    axis=-1	)

								def handle_cumulative_probs(__snake_case    :  Optional[Any]	,    __snake_case    :  List[Any]	):
															UpperCAmelCase				: List[str]											=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,    axis=-1	)
															UpperCAmelCase				: Optional[int]											=	jnp.max(logprobs_k[: self.timestamp_begin]	)
															return jnp.where(
															    timestamp_logprob > max_text_token_logprob	,    scores_k.at[: self.timestamp_begin].set(-float('''inf'''	)	)	,    __snake_case	,    )

								UpperCAmelCase				: Dict											=	jax.vmap(__snake_case	)(__snake_case	,    __snake_case	)

								return scores










'''simple docstring'''






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

UpperCamelCase__:     Union[str, Any]										=    "https://www.indeed.co.in/jobs?q=mobile+app+development&l="


def 				snake_case_  (      _lowerCAmelCase						:		str = "mumbai"						)   ->     Generator[tuple[str, str], None, None]:
							UpperCAmelCase				: Optional[int]											=	BeautifulSoup(requests.get(url + location						).content     ,   '''html.parser'''						)
							# This attribute finds out all the specifics listed in a job
							for job in soup.find_all('''div'''     ,   attrs={'''data-tn-component''': '''organicJob'''}						):
														UpperCAmelCase				: Optional[Any]											=	job.find('''a'''     ,   attrs={'''data-tn-element''': '''jobTitle'''}						).text.strip()
														UpperCAmelCase				: int											=	job.find('''span'''     ,   {'''class''': '''company'''}						).text.strip()
														yield job_title, company_name


if __name__ == "__main__":
						for i, job in enumerate(fetch_jobs("Bangalore"), 1):
												print(F"Job {i:>2} is {job[0]} at {job[1]}")





