
'''simple docstring'''
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					:    Optional[Any]           = get_logger(__name__)


lowerCAmelCase					:    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 			SCREAMING_SNAKE_CASE__      :







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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


class 			SCREAMING_SNAKE_CASE__      :







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_						,				A_						,				**A_     )->		jnp.ndarray:







											'''simple docstring'''






											for processor in self:
																		UpperCamelCase   			=     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.'''     )
																									UpperCamelCase   			=     processor(A_						,				A_						,				A_						,				**A_     )
																		else:
																									UpperCamelCase   			=     processor(A_						,				A_						,				A_     )
											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		int:







											'''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}'''     )

											UpperCamelCase   			=     temperature







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     scores / self.temperature
											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''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}'''     )

											UpperCamelCase   			=     top_p
											UpperCamelCase   			=     filter_value
											UpperCamelCase   			=     min_tokens_to_keep







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     lax.top_k(A_						,				scores.shape[-1]     )

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

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

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

											UpperCamelCase   			=     jnp.where(A_						,				A_						,				A_     )
											UpperCamelCase   			=     jax.lax.sort_key_val(A_						,				A_     )[-1]

											return next_scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_ = -float('Inf'     )						,				A_ = 1     )->		Dict:







											'''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}'''     )

											UpperCamelCase   			=     max(A_						,				A_     )
											UpperCamelCase   			=     filter_value







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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

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

											UpperCamelCase   			=     next_scores_flat.at[topk_indices_flat].set(A_     )
											UpperCamelCase   			=     next_scores_flat.reshape(A_						,				A_     )
											return next_scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     bos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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

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

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

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     max_length
											UpperCamelCase   			=     eos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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

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

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

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		Tuple:







											'''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}'''     )

											UpperCamelCase   			=     min_length
											UpperCamelCase   			=     eos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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

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

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     begin_index







				def __call__(       self						,				A_						,				A_						,				A_     )->		Optional[Any]:







											'''simple docstring'''






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

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

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     list(A_     )







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






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

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     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.
											UpperCamelCase   			=     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:
																									UpperCamelCase   			=     force_token_array.at[index].set(A_     )
											UpperCamelCase   			=     jnp.intaa(A_     )







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											def _force_token(A_     ):
																		UpperCamelCase   			=     scores.shape[0]
																		UpperCamelCase   			=     self.force_token_array[generation_idx]

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

											UpperCamelCase   			=     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 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     generate_config.eos_token_id
											UpperCamelCase   			=     generate_config.no_timestamps_token_id
											UpperCamelCase   			=     generate_config.no_timestamps_token_id + 1

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







				def __call__(       self						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






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

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

																		UpperCamelCase   			=     jnp.where((cur_len - self.begin_index) < 2						,				A_						,				A_     )
																		UpperCamelCase   			=     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_						,				)

											UpperCamelCase   			=     jax.vmap(A_     )(A_						,				A_     )

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

											UpperCamelCase   			=     self.timestamp_begin + self.max_initial_timestamp_index

											UpperCamelCase   			=     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
											UpperCamelCase   			=     jax.nn.log_softmax(A_						,				axis=-1     )

											def handle_cumulative_probs(A_						,				A_     ):
																		UpperCamelCase   			=     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]						,				axis=-1     )
																		UpperCamelCase   			=     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_						,				)

											UpperCamelCase   			=     jax.vmap(A_     )(A_						,				A_     )

											return scores









'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







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







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=snake_case_					,	hidden_dropout_prob=snake_case_					,	attention_probs_dropout_prob=snake_case_					,	max_position_embeddings=snake_case_					,	type_vocab_size=snake_case_					,	initializer_range=snake_case_					,	layer_norm_eps=snake_case_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







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







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # The order of inputs is different for question answering and sequence classification
          if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ]		)
          else:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                    ]		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case__    :       Optional[Any]       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       Tuple       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=snake_case_		)
          # Generate dummy inputs according to compute batch and sequence
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
