
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    lowerCAmelCase__     :






		@add_start_docstrings(SCREAMING_SNAKE_CASE__  )
		def __call__(		self		:							int							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray  )     ->							jnp.ndarray:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''  )





class    lowerCAmelCase__     :






		@add_start_docstrings(SCREAMING_SNAKE_CASE__  )
		def __call__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray  )     ->							jnp.ndarray:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''  )





class    lowerCAmelCase__     (	__lowercase			):






		@add_start_docstrings(SCREAMING_SNAKE_CASE__  )
		def __call__(		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int							,			**SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							jnp.ndarray:
						for processor in self:
										__lowerCamelCase    					=	inspect.signature(processor.__call__  ).parameters
										if len(SCREAMING_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.'''  )
														__lowerCamelCase    					=	processor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )
										else:
														__lowerCamelCase    					=	processor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							int							,			SCREAMING_SNAKE_CASE__		:							float  )     ->							Any:
						if not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) or not (temperature > 0):
										raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}'''  )

						__lowerCamelCase    					=	temperature







		def __call__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						__lowerCamelCase    					=	scores / self.temperature
						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							float							,			SCREAMING_SNAKE_CASE__		:							float = -float('''Inf'''  )							,			SCREAMING_SNAKE_CASE__		:							int = 1  )     ->							int:
						if not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_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(SCREAMING_SNAKE_CASE__							,			SCREAMING_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}'''  )

						__lowerCamelCase    					=	top_p
						__lowerCamelCase    					=	filter_value
						__lowerCamelCase    					=	min_tokens_to_keep







		def __call__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						__lowerCamelCase					,    __lowerCamelCase    					=	lax.top_k(SCREAMING_SNAKE_CASE__							,			scores.shape[-1]  )

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

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

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

						__lowerCamelCase    					=	jnp.where(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	jax.lax.sort_key_val(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )[-1]

						return next_scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							float = -float('''Inf'''  )							,			SCREAMING_SNAKE_CASE__		:							int = 1  )     ->							Any:
						if not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) or top_k <= 0:
										raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}'''  )

						__lowerCamelCase    					=	max(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	filter_value







		def __call__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						__lowerCamelCase					,    __lowerCamelCase    					=	scores.shape
						__lowerCamelCase    					=	jnp.full(batch_size * vocab_size							,			self.filter_value  )

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

						__lowerCamelCase    					=	next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	next_scores_flat.reshape(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						return next_scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Any:
						__lowerCamelCase    					=	bos_token_id







		def __call__(		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						__lowerCamelCase    					=	jnp.full(scores.shape							,			-float('''inf'''  )  )

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

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

						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						__lowerCamelCase    					=	max_length
						__lowerCamelCase    					=	eos_token_id







		def __call__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						__lowerCamelCase    					=	jnp.full(scores.shape							,			-float('''inf'''  )  )

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

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

						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) or min_length < 0:
										raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}'''  )

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

						__lowerCamelCase    					=	min_length
						__lowerCamelCase    					=	eos_token_id







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

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

						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							str:
						__lowerCamelCase    					=	list(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	begin_index







		def __call__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Any:
						__lowerCamelCase    					=	1 - jnp.bool_(cur_len - self.begin_index  )

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

						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							list  )     ->							Tuple:
						__lowerCamelCase    					=	list(SCREAMING_SNAKE_CASE__  )







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

						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							Tuple:
						__lowerCamelCase    					=	dict(SCREAMING_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.
						__lowerCamelCase    					=	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    					=	force_token_array.at[index].set(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	jnp.intaa(SCREAMING_SNAKE_CASE__  )







		def __call__(		self		:							int							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							jnp.ndarray							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							jnp.ndarray:
						def _force_token(SCREAMING_SNAKE_CASE__		:							Optional[int]  ):
										__lowerCamelCase    					=	scores.shape[0]
										__lowerCamelCase    					=	self.force_token_array[generation_idx]

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

						__lowerCamelCase    					=	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(SCREAMING_SNAKE_CASE__  )							,			lambda: scores							,			)							,			)
						return scores





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	generate_config.eos_token_id
						__lowerCamelCase    					=	generate_config.no_timestamps_token_id
						__lowerCamelCase    					=	generate_config.no_timestamps_token_id + 1

						__lowerCamelCase    					=	decoder_input_length + 1

						if generate_config.is_multilingual:
										# room for language token and task token
										self.begin_index += 2
						if hasattr(SCREAMING_SNAKE_CASE__							,			'''max_initial_timestamp_index'''  ):
										__lowerCamelCase    					=	generate_config.max_initial_timestamp_index
						else:
										__lowerCamelCase    					=	model_config.vocab_size
						if self.max_initial_timestamp_index is None:
										__lowerCamelCase    					=	model_config.vocab_size







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

						def handle_pairs(SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple  ):
										__lowerCamelCase    					=	jnp.where((cur_len - self.begin_index) >= 1							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	jnp.where(
										    input_ids_k[cur_len - 1] >= self.timestamp_begin							,			True and last_was_timestamp							,			SCREAMING_SNAKE_CASE__							,			)

										__lowerCamelCase    					=	jnp.where((cur_len - self.begin_index) < 2							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	jnp.where(
										    input_ids_k[cur_len - 2] >= self.timestamp_begin							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			)

										return jnp.where(
										    SCREAMING_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'''  )  )							,			)							,			SCREAMING_SNAKE_CASE__							,			)

						__lowerCamelCase    					=	jax.vmap(SCREAMING_SNAKE_CASE__  )(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

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

						__lowerCamelCase    					=	self.timestamp_begin + self.max_initial_timestamp_index

						__lowerCamelCase    					=	jnp.where(
						    SCREAMING_SNAKE_CASE__							,			scores.at[:, last_allowed + 1 :].set(-float('''inf'''  )  )							,			SCREAMING_SNAKE_CASE__							,			)

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

						def handle_cumulative_probs(SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str  ):
										__lowerCamelCase    					=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]							,			axis=-1  )
										__lowerCamelCase    					=	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'''  )  )							,			SCREAMING_SNAKE_CASE__							,			)

						__lowerCamelCase    					=	jax.vmap(SCREAMING_SNAKE_CASE__  )(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						return scores





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



