





'''simple docstring'''







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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


snake_case_  :							Union[str, Any]            =	logging.get_logger(__name__)

snake_case_  :							Dict            =	{
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}


class        __a   (lowerCamelCase  ):
							__a       : Dict          =							"layoutlm"

							def __init__(				self				:  Tuple							,  __magic_name__				:  List[str]=3_05_22							,  __magic_name__				:  Tuple=7_68							,  __magic_name__				:  List[Any]=12							,  __magic_name__				:  Optional[int]=12							,  __magic_name__				:  Tuple=30_72							,  __magic_name__				:  Union[str, Any]="gelu"							,  __magic_name__				:  Tuple=0.1							,  __magic_name__				:  List[Any]=0.1							,  __magic_name__				:  Dict=5_12							,  __magic_name__				:  Optional[int]=2							,  __magic_name__				:  str=0.0_2							,  __magic_name__				:  str=1E-12							,  __magic_name__				:  List[str]=0							,  __magic_name__				:  Optional[int]="absolute"							,  __magic_name__				:  Tuple=True							,  __magic_name__				:  Any=10_24							,  **__magic_name__				:  int							,  )       ->							Tuple:







													"""simple docstring"""
													super().__init__(pad_token_id=__magic_name__							,  **__magic_name__ )
													UpperCAmelCase_      :   Dict      =	vocab_size
													UpperCAmelCase_      :   Tuple      =	hidden_size
													UpperCAmelCase_      :   int      =	num_hidden_layers
													UpperCAmelCase_      :   Optional[int]      =	num_attention_heads
													UpperCAmelCase_      :   Tuple      =	hidden_act
													UpperCAmelCase_      :   Optional[Any]      =	intermediate_size
													UpperCAmelCase_      :   Union[str, Any]      =	hidden_dropout_prob
													UpperCAmelCase_      :   Optional[Any]      =	attention_probs_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	max_position_embeddings
													UpperCAmelCase_      :   Any      =	type_vocab_size
													UpperCAmelCase_      :   Optional[Any]      =	initializer_range
													UpperCAmelCase_      :   Optional[Any]      =	layer_norm_eps
													UpperCAmelCase_      :   Optional[int]      =	position_embedding_type
													UpperCAmelCase_      :   int      =	use_cache
													UpperCAmelCase_      :   Optional[Any]      =	max_ad_position_embeddings





class        __a   (lowerCamelCase  ):

							def __init__(				self				:  List[str]							,  __magic_name__				:  PretrainedConfig							,  __magic_name__				:  str = "default"							,  __magic_name__				:  List[PatchingSpec] = None							,  )       ->							int:







													"""simple docstring"""
													super().__init__(__magic_name__							,  task=__magic_name__							,  patching_specs=__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	config.max_ad_position_embeddings - 1

							@property
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Mapping[str, Mapping[int, str]]:







													"""simple docstring"""
													return OrderedDict(
													    [
													        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
													        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
													        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
													        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
													    ] )



							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  PreTrainedTokenizer							,  __magic_name__				:  int = -1							,  __magic_name__				:  int = -1							,  __magic_name__				:  bool = False							,  __magic_name__				:  Optional[TensorType] = None							,  )       ->							Mapping[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	super().generate_dummy_inputs(
													    __magic_name__							,  batch_size=__magic_name__							,  seq_length=__magic_name__							,  is_pair=__magic_name__							,  framework=__magic_name__ )

													# Generate a dummy bbox
													UpperCAmelCase_      :   str      =	[48, 84, 73, 1_28]

													if not framework == TensorType.PYTORCH:
																			raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.''' )

													if not is_torch_available():
																			raise ValueError('''Cannot generate dummy inputs without PyTorch installed.''' )
													import torch

													UpperCAmelCase_    ,							UpperCAmelCase_      :   int      =	input_dict['''input_ids'''].shape
													UpperCAmelCase_      :   Any      =	torch.tensor([*[box] * seq_length] ).tile(__magic_name__							,  1							,  1 )
													return input_dict












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
