


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


snake_case							:	Optional[Any]					      =				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Optional[int]					      =				[
						    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TrOCRForCausalLM''',
						    '''TrOCRPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						snake_case							:	int					      =				_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


snake_case							:	Dict					      =				get_logger(__name__)


snake_case							:	str					      =				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 	snake_case_       :
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :Dict      ,__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 	snake_case_       :
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :List[str]      ,__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 	snake_case_       (lowerCamelCase_				):
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :Dict      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int      ,**__snake_case       :Any				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :str      ,__snake_case       :float				) ->   Tuple:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												a__					=					scores / self.temperature
												return scores







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Any      ,__snake_case       :float      ,__snake_case       :float = -float('Inf'				)      ,__snake_case       :int = 1				) ->   Dict:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :List[str]      ,__snake_case       :int      ,__snake_case       :float = -float('Inf'				)      ,__snake_case       :int = 1				) ->   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}'				)

												a__					=					max(__snake_case      ,__snake_case				)
												a__					=					filter_value



					def __call__(				self       :int      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :int      ,__snake_case       :int				) ->   str:
												a__					=					bos_token_id



					def __call__(				self       :List[Any]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :int				) ->   List[Any]:
												a__					=					max_length
												a__					=					eos_token_id



					def __call__(				self       :int      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :str      ,__snake_case       :int      ,__snake_case       :int				) ->   List[str]:
												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       :Any      ,__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
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Optional[int]      ,__snake_case       :List[str]      ,__snake_case       :Optional[int]				) ->   Tuple:
												a__					=					list(__snake_case				)
												a__					=					begin_index



					def __call__(				self       :str      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :int				) ->   str:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :List[str]      ,__snake_case       :list				) ->   List[Any]:
												a__					=					list(__snake_case				)



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

												return scores







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Dict      ,__snake_case       :Optional[int]				) ->   Union[str, Any]:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												def _force_token(__snake_case       :Optional[Any]				):
																			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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Any      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :List[Any]				) ->   Optional[int]:
												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       :Any      ,__snake_case       :List[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]				) ->   Tuple:
												# suppress <|notimestamps|> which is handled by without_timestamps
												a__					=					scores.at[:, self.no_timestamps_token_id].set(-float('inf'				)				)

												def handle_pairs(__snake_case       :List[str]      ,__snake_case       :Union[str, Any]				):
																			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       :Dict      ,__snake_case       :List[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




