



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =      {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMRobertaForCausalLM''',
							    '''XLMRobertaForMaskedLM''',
							    '''XLMRobertaForMultipleChoice''',
							    '''XLMRobertaForQuestionAnswering''',
							    '''XLMRobertaForSequenceClassification''',
							    '''XLMRobertaForTokenClassification''',
							    '''XLMRobertaModel''',
							    '''XLMRobertaPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXLMRobertaForCausalLM''',
							    '''TFXLMRobertaForMaskedLM''',
							    '''TFXLMRobertaForMultipleChoice''',
							    '''TFXLMRobertaForQuestionAnswering''',
							    '''TFXLMRobertaForSequenceClassification''',
							    '''TFXLMRobertaForTokenClassification''',
							    '''TFXLMRobertaModel''',
							    '''TFXLMRobertaPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaxXLMRobertaForMaskedLM''',
							    '''FlaxXLMRobertaForCausalLM''',
							    '''FlaxXLMRobertaForMultipleChoice''',
							    '''FlaxXLMRobertaForQuestionAnswering''',
							    '''FlaxXLMRobertaForSequenceClassification''',
							    '''FlaxXLMRobertaForTokenClassification''',
							    '''FlaxXLMRobertaModel''',
							    '''FlaxXLMRobertaPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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__       =      get_logger(__name__)


SCREAMING_SNAKE_CASE__       =      r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''


class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""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(_snake_case					)
					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


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







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Any				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								for processor in self:
											A__       =    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.'''					)
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case				,					**_snake_case					)
											else:
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       float					):






								"""simple docstring"""


								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}'''					)

								A__       =    temperature



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores / self.temperature
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       float				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 1					):






								"""simple docstring"""


								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}'''					)

								A__       =    top_p
								A__       =    filter_value
								A__       =    min_tokens_to_keep



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    lax.top_k(_snake_case				,					scores.shape[-1]					)

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

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

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

								A__       =    jnp.where(_snake_case				,					_snake_case				,					_snake_case					)
								A__       =    jax.lax.sort_key_val(_snake_case				,					_snake_case					)[-1]

								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






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






								"""simple docstring"""


								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}'''					)

								A__       =    max(_snake_case				,					_snake_case					)
								A__       =    filter_value



					def __call__(     self					:       Optional[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    scores.shape
								A__       =    jnp.full(batch_size * vocab_size				,					self.filter_value					)

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

								A__       =    next_scores_flat.at[topk_indices_flat].set(_snake_case					)
								A__       =    next_scores_flat.reshape(_snake_case				,					_snake_case					)
								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    bos_token_id



					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    max_length
								A__       =    eos_token_id



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								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}'''					)

								A__       =    min_length
								A__       =    eos_token_id



					def __call__(     self					:       int				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.clip(cur_len - self.min_length				,					0				,					1					)

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






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






								"""simple docstring"""


								A__       =    list(_snake_case					)
								A__       =    begin_index



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






								"""simple docstring"""


								A__       =    1 - jnp.bool_(cur_len - self.begin_index					)

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       list					):






								"""simple docstring"""


								A__       =    list(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    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.
								A__       =    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:
														A__       =    force_token_array.at[index].set(_snake_case					)
								A__       =    jnp.intaa(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								def _force_token(_snake_case					:       Dict					):
											A__       =    scores.shape[0]
											A__       =    self.force_token_array[generation_idx]

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

								A__       =    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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






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






								"""simple docstring"""


								A__       =    generate_config.eos_token_id
								A__       =    generate_config.no_timestamps_token_id
								A__       =    generate_config.no_timestamps_token_id + 1

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



					def __call__(     self					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Dict				,					_snake_case					:       Dict					):






								"""simple docstring"""


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

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

											A__       =    jnp.where((cur_len - self.begin_index) < 2				,					_snake_case				,					_snake_case					)
											A__       =    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				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

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

								A__       =    self.timestamp_begin + self.max_initial_timestamp_index

								A__       =    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
								A__       =    jax.nn.log_softmax(_snake_case				,					axis=-1					)

								def handle_cumulative_probs(_snake_case					:       List[Any]				,					_snake_case					:       Union[str, Any]					):
											A__       =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,					axis=-1					)
											A__       =    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				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

								return scores




