






"""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_     :				Any         =				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 							lowerCAmelCase__       :






      '''simple docstring'''

      @add_start_docstrings(A_)
      def __call__(		self  :						Any		,			lowercase_  :						Tuple		,			lowercase_  :						List[str]):




          '''simple docstring'''


          raise NotImplementedError(
              F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.')




class 							lowerCAmelCase__       :






      '''simple docstring'''

      @add_start_docstrings(A_)
      def __call__(		self  :						Any		,			lowercase_  :						int		,			lowercase_  :						Tuple):




          '''simple docstring'''


          raise NotImplementedError(
              F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.')




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      @add_start_docstrings(A_)
      def __call__(		self  :						Union[str, Any]		,			lowercase_  :						int		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						List[str]		,			**lowercase_  :						Union[str, Any]):




          '''simple docstring'''


          for processor in self:
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	inspect.signature(processor.__call__).parameters
              if len(A_) > 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.')
                  SCREAMING_SNAKE_CASE_   :						Any							=	processor(A_		,			A_		,			A_		,			**A_)
              else:
                  SCREAMING_SNAKE_CASE_   :						Dict							=	processor(A_		,			A_		,			A_)
          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						Tuple):




          '''simple docstring'''


          if not isinstance(A_		,			A_) or not (temperature > 0):
              raise ValueError(F'`temperature` has to be a strictly positive float, but is {temperature}')

          SCREAMING_SNAKE_CASE_   :						int							=	temperature



      def __call__(		self  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						int		,			lowercase_  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	scores / self.temperature
          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						List[Any]		,			lowercase_  :						List[str]		,			lowercase_  :						str = -float('''Inf''')		,			lowercase_  :						Union[str, Any] = 1):




          '''simple docstring'''


          if not isinstance(A_		,			A_) 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(A_		,			A_) 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}')

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	top_p
          SCREAMING_SNAKE_CASE_   :						int							=	filter_value
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	min_tokens_to_keep



      def __call__(		self  :						int		,			lowercase_  :						Tuple		,			lowercase_  :						List[str]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Dict							=	lax.top_k(A_		,			scores.shape[-1])

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

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

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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	jnp.where(A_		,			A_		,			A_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jax.lax.sort_key_val(A_		,			A_)[-1]

          return next_scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			lowercase_  :						List[Any]		,			lowercase_  :						Optional[int] = -float('''Inf''')		,			lowercase_  :						Tuple = 1):




          '''simple docstring'''


          if not isinstance(A_		,			A_) or top_k <= 0:
              raise ValueError(F'`top_k` has to be a strictly positive integer, but is {top_k}')

          SCREAMING_SNAKE_CASE_   :						int							=	max(A_		,			A_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	filter_value



      def __call__(		self  :						Any		,			lowercase_  :						Optional[int]		,			lowercase_  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						str							=	scores.shape
          SCREAMING_SNAKE_CASE_   :						Tuple							=	jnp.full(batch_size * vocab_size		,			self.filter_value)

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

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	next_scores_flat.at[topk_indices_flat].set(A_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	next_scores_flat.reshape(A_		,			A_)
          return next_scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						List[str]		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	bos_token_id



      def __call__(		self  :						Union[str, Any]		,			lowercase_  :						Dict		,			lowercase_  :						Dict		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.full(scores.shape		,			-float('''inf'''))

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1 - jnp.bool_(cur_len - 1)

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.where(A_		,			new_scores.at[:, self.bos_token_id].set(0)		,			A_)

          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			lowercase_  :						List[str]		,			lowercase_  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	max_length
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	eos_token_id



      def __call__(		self  :						Union[str, Any]		,			lowercase_  :						List[str]		,			lowercase_  :						int		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	jnp.full(scores.shape		,			-float('''inf'''))

          SCREAMING_SNAKE_CASE_   :						Dict							=	1 - jnp.bool_(cur_len - self.max_length + 1)

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

          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						List[str]		,			lowercase_  :						Tuple		,			lowercase_  :						int):




          '''simple docstring'''


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

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

          SCREAMING_SNAKE_CASE_   :						str							=	min_length
          SCREAMING_SNAKE_CASE_   :						Tuple							=	eos_token_id



      def __call__(		self  :						Optional[Any]		,			lowercase_  :						int		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	1 - jnp.clip(cur_len - self.min_length		,			0		,			1)

          SCREAMING_SNAKE_CASE_   :						str							=	jnp.where(A_		,			scores.at[:, self.eos_token_id].set(-float('''inf'''))		,			A_)

          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Any		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	list(A_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	begin_index



      def __call__(		self  :						List[Any]		,			lowercase_  :						List[str]		,			lowercase_  :						str		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	1 - jnp.bool_(cur_len - self.begin_index)

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

          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Optional[int]		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	list(A_)



      def __call__(		self  :						List[Any]		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	scores.at[..., self.suppress_tokens].set(-float('''inf'''))

          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Optional[int]		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	dict(A_)
          # 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.
          SCREAMING_SNAKE_CASE_   :						int							=	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:
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	force_token_array.at[index].set(A_)
          SCREAMING_SNAKE_CASE_   :						str							=	jnp.intaa(A_)



      def __call__(		self  :						Union[str, Any]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						str):




          '''simple docstring'''


          def _force_token(lowercase_  :						Any):
              SCREAMING_SNAKE_CASE_   :						Dict							=	scores.shape[0]
              SCREAMING_SNAKE_CASE_   :						Any							=	self.force_token_array[generation_idx]

              SCREAMING_SNAKE_CASE_   :						Dict							=	jnp.ones_like(A_		,			dtype=scores.dtype) * -float('''inf''')
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.zeros((batch_size, 1)		,			dtype=scores.dtype)
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	lax.dynamic_update_slice(A_		,			A_		,			(0, current_token))
              return new_scores

          SCREAMING_SNAKE_CASE_   :						str							=	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(A_)		,			lambda: scores		,			)		,			)
          return scores




class 							lowerCAmelCase__       (    _SCREAMING_SNAKE_CASE				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	generate_config.eos_token_id
          SCREAMING_SNAKE_CASE_   :						Tuple							=	generate_config.no_timestamps_token_id
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	generate_config.no_timestamps_token_id + 1

          SCREAMING_SNAKE_CASE_   :						Tuple							=	decoder_input_length + 1

          if generate_config.is_multilingual:
              # room for language token and task token
              self.begin_index += 2
          if hasattr(A_		,			'''max_initial_timestamp_index'''):
              SCREAMING_SNAKE_CASE_   :						int							=	generate_config.max_initial_timestamp_index
          else:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	model_config.vocab_size
          if self.max_initial_timestamp_index is None:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	model_config.vocab_size



      def __call__(		self  :						Union[str, Any]		,			lowercase_  :						Tuple		,			lowercase_  :						str		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''))

          def handle_pairs(lowercase_  :						Tuple		,			lowercase_  :						Optional[int]):
              SCREAMING_SNAKE_CASE_   :						List[str]							=	jnp.where((cur_len - self.begin_index) >= 1		,			A_		,			A_)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.where(
                  input_ids_k[cur_len - 1] >= self.timestamp_begin		,			True and last_was_timestamp		,			A_		,			)

              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	jnp.where((cur_len - self.begin_index) < 2		,			A_		,			A_)
              SCREAMING_SNAKE_CASE_   :						Dict							=	jnp.where(
                  input_ids_k[cur_len - 2] >= self.timestamp_begin		,			A_		,			A_		,			)

              return jnp.where(
                  A_		,			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'''))		,			)		,			A_		,			)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	jax.vmap(A_)(A_		,			A_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.where(cur_len == self.begin_index		,			A_		,			A_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.where(
              self.max_initial_timestamp_index is not None		,			True and apply_max_initial_timestamp		,			A_		,			)

          SCREAMING_SNAKE_CASE_   :						Any							=	self.timestamp_begin + self.max_initial_timestamp_index

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	jnp.where(
              A_		,			scores.at[:, last_allowed + 1 :].set(-float('''inf'''))		,			A_		,			)

          # if sum of probability over timestamps is above any other token, sample timestamp
          SCREAMING_SNAKE_CASE_   :						Dict							=	jax.nn.log_softmax(A_		,			axis=-1)

          def handle_cumulative_probs(lowercase_  :						Optional[int]		,			lowercase_  :						int):
              SCREAMING_SNAKE_CASE_   :						Any							=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]		,			axis=-1)
              SCREAMING_SNAKE_CASE_   :						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'''))		,			A_		,			)

          SCREAMING_SNAKE_CASE_   :						Dict							=	jax.vmap(A_)(A_		,			A_)

          return scores










"""simple docstring"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


