





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


_lowerCamelCase      							=   get_logger(__name__)


_lowerCamelCase      							=   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(a__)
      def __call__(		self   ,				a__   ,				a__):



             """simple docstring"""





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







class       __A							:




      """simple docstring"""







      @add_start_docstrings(a__)
      def __call__(		self   ,				a__   ,				a__):



             """simple docstring"""





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







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      @add_start_docstrings(a__)
      def __call__(		self   ,				a__   ,				a__   ,				a__   ,				**a__):



             """simple docstring"""





             for processor in self:
                    _lowerCamelCase				:  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.""")
                           _lowerCamelCase				:  Tuple						=		processor(a__   ,				a__   ,				a__   ,				**a__)
                    else:
                           _lowerCamelCase				:  Optional[int]						=		processor(a__   ,				a__   ,				a__)
             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__):



             """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}""")

             _lowerCamelCase				:  Tuple						=		temperature



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		scores / self.temperature
             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__ = -float('''Inf''')   ,				a__ = 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}""")

             _lowerCamelCase				:  int						=		top_p
             _lowerCamelCase				:  Union[str, Any]						=		filter_value
             _lowerCamelCase				:  Optional[Any]						=		min_tokens_to_keep



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase,	_lowerCamelCase				:  int						=		lax.top_k(a__   ,				scores.shape[-1])

             _lowerCamelCase				:  int						=		jnp.full_like(a__   ,				self.filter_value)
             _lowerCamelCase				:  Optional[int]						=		jax.nn.softmax(a__   ,				axis=-1).cumsum(axis=-1)
             _lowerCamelCase				:  List[Any]						=		cumulative_probs < self.top_p

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

             # min tokens to keep
             _lowerCamelCase				:  str						=		score_mask.at[:, : self.min_tokens_to_keep].set(a__)

             _lowerCamelCase				:  int						=		jnp.where(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  Any						=		jax.lax.sort_key_val(a__   ,				a__)[-1]

             return next_scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__ = -float('''Inf''')   ,				a__ = 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}""")

             _lowerCamelCase				:  List[Any]						=		max(a__   ,				a__)
             _lowerCamelCase				:  Union[str, Any]						=		filter_value



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase,	_lowerCamelCase				:  List[Any]						=		scores.shape
             _lowerCamelCase				:  List[Any]						=		jnp.full(batch_size * vocab_size   ,				self.filter_value)

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

             _lowerCamelCase				:  str						=		next_scores_flat.at[topk_indices_flat].set(a__)
             _lowerCamelCase				:  Optional[int]						=		next_scores_flat.reshape(a__   ,				a__)
             return next_scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		bos_token_id



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





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

             _lowerCamelCase				:  Dict						=		1 - jnp.bool_(cur_len - 1)

             _lowerCamelCase				:  List[str]						=		jnp.where(a__   ,				new_scores.at[:, self.bos_token_id].set(0)   ,				a__)

             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		max_length
             _lowerCamelCase				:  Any						=		eos_token_id



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





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

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

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

             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """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}""")

             _lowerCamelCase				:  List[Any]						=		min_length
             _lowerCamelCase				:  Any						=		eos_token_id



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		1 - jnp.clip(cur_len - self.min_length   ,				0   ,				1)

             _lowerCamelCase				:  int						=		jnp.where(a__   ,				scores.at[:, self.eos_token_id].set(-float('''inf'''))   ,				a__)

             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  int						=		list(a__)
             _lowerCamelCase				:  Union[str, Any]						=		begin_index



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		1 - jnp.bool_(cur_len - self.begin_index)

             _lowerCamelCase				:  Dict						=		jnp.where(a__   ,				scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''))   ,				a__)

             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		list(a__)



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





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

             return scores







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[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.
             _lowerCamelCase				:  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:
                           _lowerCamelCase				:  List[Any]						=		force_token_array.at[index].set(a__)
             _lowerCamelCase				:  Tuple						=		jnp.intaa(a__)



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             def _force_token(a__):
                    _lowerCamelCase				:  Any						=		scores.shape[0]
                    _lowerCamelCase				:  Tuple						=		self.force_token_array[generation_idx]

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

             _lowerCamelCase				:  Any						=		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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		generate_config.eos_token_id
             _lowerCamelCase				:  List[str]						=		generate_config.no_timestamps_token_id
             _lowerCamelCase				:  str						=		generate_config.no_timestamps_token_id + 1

             _lowerCamelCase				:  List[Any]						=		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'''):
                    _lowerCamelCase				:  str						=		generate_config.max_initial_timestamp_index
             else:
                    _lowerCamelCase				:  List[str]						=		model_config.vocab_size
             if self.max_initial_timestamp_index is None:
                    _lowerCamelCase				:  Dict						=		model_config.vocab_size



      def __call__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''))

             def handle_pairs(a__   ,				a__):
                    _lowerCamelCase				:  Optional[Any]						=		jnp.where((cur_len - self.begin_index) >= 1   ,				a__   ,				a__)
                    _lowerCamelCase				:  int						=		jnp.where(
                        input_ids_k[cur_len - 1] >= self.timestamp_begin   ,				True and last_was_timestamp   ,				a__   ,				)

                    _lowerCamelCase				:  Tuple						=		jnp.where((cur_len - self.begin_index) < 2   ,				a__   ,				a__)
                    _lowerCamelCase				:  int						=		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__   ,				)

             _lowerCamelCase				:  Any						=		jax.vmap(a__)(a__   ,				a__)

             _lowerCamelCase				:  Any						=		jnp.where(cur_len == self.begin_index   ,				a__   ,				a__)
             _lowerCamelCase				:  Any						=		jnp.where(
                 self.max_initial_timestamp_index is not None   ,				True and apply_max_initial_timestamp   ,				a__   ,				)

             _lowerCamelCase				:  List[Any]						=		self.timestamp_begin + self.max_initial_timestamp_index

             _lowerCamelCase				:  str						=		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
             _lowerCamelCase				:  str						=		jax.nn.log_softmax(a__   ,				axis=-1)

             def handle_cumulative_probs(a__   ,				a__):
                    _lowerCamelCase				:  Any						=		jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,				axis=-1)
                    _lowerCamelCase				:  str						=		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__   ,				)

             _lowerCamelCase				:  List[Any]						=		jax.vmap(a__)(a__   ,				a__)

             return scores








import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

