


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


_SCREAMING_SNAKE_CASE      :			Dict      				=   get_logger(__name__)


_SCREAMING_SNAKE_CASE      :			List[str]      				=   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   A__ :







    """simple docstring"""

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






class   A__ :







    """simple docstring"""

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






class   A__ (   snake_case__     ):







    """simple docstring"""

    @add_start_docstrings(__snake_case   )
    def __call__(	self      , __snake_case      , __snake_case      , __snake_case      , **__snake_case   ):
         for processor in self:
              snake_case					=    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.'''   )
                   snake_case					=    processor(__snake_case      , __snake_case      , __snake_case      , **__snake_case   )
              else:
                   snake_case					=    processor(__snake_case      , __snake_case      , __snake_case   )
         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         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}'''   )

         snake_case					=    temperature




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    scores / self.temperature
         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case = -float('''Inf'''   )      , __snake_case = 1   ):
         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}'''   )

         snake_case					=    top_p
         snake_case					=    filter_value
         snake_case					=    min_tokens_to_keep




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case			,  snake_case					=    lax.top_k(__snake_case      , scores.shape[-1]   )

         snake_case					=    jnp.full_like(__snake_case      , self.filter_value   )
         snake_case					=    jax.nn.softmax(__snake_case      , axis=-1   ).cumsum(axis=-1   )
         snake_case					=    cumulative_probs < self.top_p

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

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

         snake_case					=    jnp.where(__snake_case      , __snake_case      , __snake_case   )
         snake_case					=    jax.lax.sort_key_val(__snake_case      , __snake_case   )[-1]

         return next_scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case = -float('''Inf'''   )      , __snake_case = 1   ):
         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}'''   )

         snake_case					=    max(__snake_case      , __snake_case   )
         snake_case					=    filter_value




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case			,  snake_case					=    scores.shape
         snake_case					=    jnp.full(batch_size * vocab_size      , self.filter_value   )

         snake_case					=    min(self.top_k      , scores.shape[-1]   )  # Safety check
         snake_case			,  snake_case					=    lax.top_k(__snake_case      , __snake_case   )
         snake_case					=    jnp.broadcast_to((jnp.arange(__snake_case   ) * vocab_size)[:, None]      , (batch_size, topk)   ).flatten()
         snake_case					=    topk_scores.flatten()
         snake_case					=    topk_indices.flatten() + shift

         snake_case					=    next_scores_flat.at[topk_indices_flat].set(__snake_case   )
         snake_case					=    next_scores_flat.reshape(__snake_case      , __snake_case   )
         return next_scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    bos_token_id




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    jnp.full(scores.shape      , -float('''inf'''   )   )

         snake_case					=    1 - jnp.bool_(cur_len - 1   )

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

         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    max_length
         snake_case					=    eos_token_id




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    jnp.full(scores.shape      , -float('''inf'''   )   )

         snake_case					=    1 - jnp.bool_(cur_len - self.max_length + 1   )

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

         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         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}'''   )

         snake_case					=    min_length
         snake_case					=    eos_token_id




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         # create boolean flag to decide if min length penalty should be applied
         snake_case					=    1 - jnp.clip(cur_len - self.min_length      , 0      , 1   )

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

         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    list(__snake_case   )
         snake_case					=    begin_index




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    1 - jnp.bool_(cur_len - self.begin_index   )

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

         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    list(__snake_case   )




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    scores.at[..., self.suppress_tokens].set(-float('''inf'''   )   )

         return scores






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    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.
         snake_case					=    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:
                   snake_case					=    force_token_array.at[index].set(__snake_case   )
         snake_case					=    jnp.intaa(__snake_case   )




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         def _force_token(__snake_case   ):
              snake_case					=    scores.shape[0]
              snake_case					=    self.force_token_array[generation_idx]

              snake_case					=    jnp.ones_like(__snake_case      , dtype=scores.dtype   ) * -float('''inf'''   )
              snake_case					=    jnp.zeros((batch_size, 1)      , dtype=scores.dtype   )
              snake_case					=    lax.dynamic_update_slice(__snake_case      , __snake_case      , (0, current_token)   )
              return new_scores

         snake_case					=    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   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    generate_config.eos_token_id
         snake_case					=    generate_config.no_timestamps_token_id
         snake_case					=    generate_config.no_timestamps_token_id + 1

         snake_case					=    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'''   ):
              snake_case					=    generate_config.max_initial_timestamp_index
         else:
              snake_case					=    model_config.vocab_size
         if self.max_initial_timestamp_index is None:
              snake_case					=    model_config.vocab_size




    def __call__(	self      , __snake_case      , __snake_case      , __snake_case   ):
         # suppress <|notimestamps|> which is handled by without_timestamps
         snake_case					=    scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''   )   )

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

              snake_case					=    jnp.where((cur_len - self.begin_index) < 2      , __snake_case      , __snake_case   )
              snake_case					=    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      , )

         snake_case					=    jax.vmap(__snake_case   )(__snake_case      , __snake_case   )

         snake_case					=    jnp.where(cur_len == self.begin_index      , __snake_case      , __snake_case   )
         snake_case					=    jnp.where(
             self.max_initial_timestamp_index is not None      , True and apply_max_initial_timestamp      , __snake_case      , )

         snake_case					=    self.timestamp_begin + self.max_initial_timestamp_index

         snake_case					=    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
         snake_case					=    jax.nn.log_softmax(__snake_case      , axis=-1   )

         def handle_cumulative_probs(__snake_case      , __snake_case   ):
              snake_case					=    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      , axis=-1   )
              snake_case					=    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      , )

         snake_case					=    jax.vmap(__snake_case   )(__snake_case      , __snake_case   )

         return scores








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    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'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    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]}''')




