






from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42




class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__    ):






  """simple docstring"""





  @register_to_config
  def __init__(     self		,		_UpperCAmelCase = 16		,		_UpperCAmelCase = 88		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 1		,		_UpperCAmelCase = 0.0		,		_UpperCAmelCase = 32		,		_UpperCAmelCase = None		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = "geglu"		,		_UpperCAmelCase = True		,		_UpperCAmelCase = True		,		)			-> Tuple:
     super().__init__()
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     attention_head_dim
     UpperCamelCase_									=     num_attention_heads * attention_head_dim

     UpperCamelCase_									=     in_channels

     UpperCamelCase_									=     torch.nn.GroupNorm(num_groups=_UpperCAmelCase		,		num_channels=_UpperCAmelCase		,		eps=1e-6		,		affine=_UpperCAmelCase )
     UpperCamelCase_									=     nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase )

     # 3. Define transformers blocks
     UpperCamelCase_									=     nn.ModuleList(
         [
             BasicTransformerBlock(
                 _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		dropout=_UpperCAmelCase		,		cross_attention_dim=_UpperCAmelCase		,		activation_fn=_UpperCAmelCase		,		attention_bias=_UpperCAmelCase		,		double_self_attention=_UpperCAmelCase		,		norm_elementwise_affine=_UpperCAmelCase		,		)
             for d in range(_UpperCAmelCase )
         ] )

     UpperCamelCase_									=     nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=1		,		_UpperCAmelCase=None		,		_UpperCAmelCase = True		,		)			-> Dict:
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     hidden_states.shape
     UpperCamelCase_									=     batch_frames // num_frames

     UpperCamelCase_									=     hidden_states

     UpperCamelCase_									=     hidden_states[None, :].reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     hidden_states.permute(0		,		2		,		1		,		3		,		4 )

     UpperCamelCase_									=     self.norm(_UpperCAmelCase )
     UpperCamelCase_									=     hidden_states.permute(0		,		3		,		4		,		2		,		1 ).reshape(batch_size * height * width		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     self.proj_in(_UpperCAmelCase )

     # 2. Blocks
     for block in self.transformer_blocks:
        UpperCamelCase_									=     block(
            _UpperCAmelCase		,		encoder_hidden_states=_UpperCAmelCase		,		timestep=_UpperCAmelCase		,		cross_attention_kwargs=_UpperCAmelCase		,		class_labels=_UpperCAmelCase		,		)

     # 3. Output
     UpperCamelCase_									=     self.proj_out(_UpperCAmelCase )
     UpperCamelCase_									=     (
         hidden_states[None, None, :]
         .reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
         .permute(0		,		3		,		4		,		1		,		2 )
         .contiguous()
     )
     UpperCamelCase_									=     hidden_states.reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=_UpperCAmelCase )







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


snake_case__      :	Tuple       	=      logging.get_logger(__name__)

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


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """layoutlmv3"""




  def __init__(     self		,		_UpperCAmelCase=50265		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=128		,		_UpperCAmelCase=128		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=128		,		_UpperCAmelCase=64		,		_UpperCAmelCase=256		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=224		,		_UpperCAmelCase=3		,		_UpperCAmelCase=16		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Tuple:
     super().__init__(
         vocab_size=_UpperCAmelCase		,		hidden_size=_UpperCAmelCase		,		num_hidden_layers=_UpperCAmelCase		,		num_attention_heads=_UpperCAmelCase		,		intermediate_size=_UpperCAmelCase		,		hidden_act=_UpperCAmelCase		,		hidden_dropout_prob=_UpperCAmelCase		,		attention_probs_dropout_prob=_UpperCAmelCase		,		max_position_embeddings=_UpperCAmelCase		,		type_vocab_size=_UpperCAmelCase		,		initializer_range=_UpperCAmelCase		,		layer_norm_eps=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		**_UpperCAmelCase		,		)
     UpperCamelCase_									=     max_ad_position_embeddings
     UpperCamelCase_									=     coordinate_size
     UpperCamelCase_									=     shape_size
     UpperCamelCase_									=     has_relative_attention_bias
     UpperCamelCase_									=     rel_pos_bins
     UpperCamelCase_									=     max_rel_pos
     UpperCamelCase_									=     has_spatial_attention_bias
     UpperCamelCase_									=     rel_ad_pos_bins
     UpperCamelCase_									=     max_rel_ad_pos
     UpperCamelCase_									=     text_embed
     UpperCamelCase_									=     visual_embed
     UpperCamelCase_									=     input_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     classifier_dropout




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   version.parse("""1.12"""    )




  @property
  def    _UpperCAmelCase     (     self )			-> Mapping[str, Mapping[int, str]]:
     # 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    _UpperCAmelCase     (     self )			-> float:
     return 1e-5




  @property
  def    _UpperCAmelCase     (     self )			-> int:
     return 12




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 3		,		_UpperCAmelCase = 40		,		_UpperCAmelCase = 40		,		)			-> Mapping[str, Any]:
     setattr(processor.image_processor		,		'apply_ocr'		,		_UpperCAmelCase )

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

     # Generate dummy bounding boxes
     UpperCamelCase_									=     [[[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)
     UpperCamelCase_									=     self._generate_dummy_images(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     dict(
         processor(
             _UpperCAmelCase		,		text=_UpperCAmelCase		,		boxes=_UpperCAmelCase		,		return_tensors=_UpperCAmelCase		,		) )

     return inputs





