



"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      						=  R"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"



class   lowerCAmelCase_    (			_A					):



				"""simple docstring"""

				@add_start_docstrings(__lowerCamelCase    )
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										raise NotImplementedError('StoppingCriteria needs to be subclassed'    )





class   lowerCAmelCase_    (			_A					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			max_length
										snake_case								=			max_position_embeddings

				@add_start_docstrings(__lowerCamelCase    )
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			input_ids.shape[-1]
										snake_case								=			cur_len >= self.max_length
										if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																logger.warning_once(
																    'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
																    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
																    'exceptions, performance degradation, or nothing at all.'    )
										return is_done





class   lowerCAmelCase_    (			_A					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										warnings.warn(
										    'The class `MaxNewTokensCriteria` is deprecated. '
										    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
										    'with `max_length = start_length + max_new_tokens` instead.'		,    __lowerCamelCase		,    )
										snake_case								=			start_length
										snake_case								=			max_new_tokens
										snake_case								=			start_length + max_new_tokens

				@add_start_docstrings(__lowerCamelCase    )
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return input_ids.shape[-1] >= self.max_length





class   lowerCAmelCase_    (			_A					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			max_time
										snake_case								=			time.time() if initial_timestamp is None else initial_timestamp

				@add_start_docstrings(__lowerCamelCase    )
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return time.time() - self.initial_timestamp > self.max_time





class   lowerCAmelCase_    (			_A					):



				"""simple docstring"""

				@add_start_docstrings(__lowerCamelCase    )
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return any(criteria(__lowerCamelCase		,    __lowerCamelCase    ) for criteria in self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										for stopping_criterium in self:
																if isinstance(__lowerCamelCase		,    __lowerCamelCase    ):
																						return stopping_criterium.max_length
																elif isinstance(__lowerCamelCase		,    __lowerCamelCase    ):
																						return stopping_criterium.max_length
										return None





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Dict					)					->				int:





						"""simple docstring"""






						snake_case								=			stopping_criteria.max_length
						snake_case								=			deepcopy(_lowerCamelCase					)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter' ,			_lowerCamelCase					)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCamelCase					)					)
						return new_stopping_criteria



"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    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								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=lowerCAmelCase    )
										# Generate dummy inputs according to compute batch and sequence
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * 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								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


