



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


_lowercase: Optional[Any]      			=		get_logger(__name__)


_lowercase: 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 	_lowercase							:





	"""simple docstring"""




	@add_start_docstrings(__a		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




class 	_lowercase							:





	"""simple docstring"""




	@add_start_docstrings(__a		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	@add_start_docstrings(__a		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						for processor in self:
											a					     =   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.'''		)
																a					     =   processor(__a		,       __a		,       __a		,       **__a		)
											else:
																a					     =   processor(__a		,       __a		,       __a		)
						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""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}'''		)

						a					     =   temperature




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   scores / self.temperature
						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




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

						a					     =   top_p
						a					     =   filter_value
						a					     =   min_tokens_to_keep




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a     ,				a					     =   lax.top_k(__a		,       scores.shape[-1]		)

						a					     =   jnp.full_like(__a		,       self.filter_value		)
						a					     =   jax.nn.softmax(__a		,       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(__a		,       1		)
						score_mask |= score_mask.at[:, 0].set(__a		)

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

						a					     =   jnp.where(__a		,       __a		,       __a		)
						a					     =   jax.lax.sort_key_val(__a		,       __a		)[-1]

						return next_scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




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

						a					     =   max(__a		,       __a		)
						a					     =   filter_value




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""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(__a		,       __a		)
						a					     =   jnp.broadcast_to((jnp.arange(__a		) * 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(__a		)
						a					     =   next_scores_flat.reshape(__a		,       __a		)
						return next_scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   bos_token_id




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   jnp.full(scores.shape		,       -float("inf"		)		)

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

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

						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   max_length
						a					     =   eos_token_id




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   jnp.full(scores.shape		,       -float("inf"		)		)

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

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

						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""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}'''		)

						a					     =   min_length
						a					     =   eos_token_id




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						a					     =   jnp.where(__a		,       scores.at[:, self.eos_token_id].set(-float("inf"		)		)		,       __a		)

						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   list(__a		)
						a					     =   begin_index




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						a					     =   jnp.where(__a		,       scores.at[:, self.begin_suppress_tokens].set(-float("inf"		)		)		,       __a		)

						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   list(__a		)




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   scores.at[..., self.suppress_tokens].set(-float("inf"		)		)

						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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.
						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(__a		)
						a					     =   jnp.intaa(__a		)




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						def _force_token(lowerCamelCase_		):
											a					     =   scores.shape[0]
											a					     =   self.force_token_array[generation_idx]

											a					     =   jnp.ones_like(__a		,       dtype=scores.dtype		) * -float("inf"		)
											a					     =   jnp.zeros((batch_size, 1)		,       dtype=scores.dtype		)
											a					     =   lax.dynamic_update_slice(__a		,       __a		,       (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(__a		)		,       lambda: scores		,       )		,       )
						return scores




class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""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(__a		,       "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		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						def handle_pairs(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   jnp.where((cur_len - self.begin_index) >= 1		,       __a		,       __a		)
											a					     =   jnp.where(
											    input_ids_k[cur_len - 1] >= self.timestamp_begin		,       True and last_was_timestamp		,       __a		,       )

											a					     =   jnp.where((cur_len - self.begin_index) < 2		,       __a		,       __a		)
											a					     =   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		,       )

						a					     =   jax.vmap(__a		)(__a		,       __a		)

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

						a					     =   self.timestamp_begin + self.max_initial_timestamp_index

						a					     =   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
						a					     =   jax.nn.log_softmax(__a		,       axis=-1		)

						def handle_cumulative_probs(lowerCamelCase_		,       lowerCamelCase_		):
											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"		)		)		,       __a		,       )

						a					     =   jax.vmap(__a		)(__a		,       __a		)

						return scores







'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
