
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


_UpperCAmelCase   :					Dict    =			logging.get_logger(__name__)

_UpperCAmelCase   :					Tuple    =			{
    "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 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			str      	=							"layoutlm"



		def __init__(					self							, A_=30_522							, A_=768							, A_=12							, A_=12							, A_=3_072							, A_="gelu"							, A_=0.1							, A_=0.1							, A_=512							, A_=2							, A_=0.02							, A_=1e-12							, A_=0							, A_="absolute"							, A_=True							, A_=1_024							, **A_							, )    ->   Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_							, **A_			)
				UpperCamelCase            =	vocab_size
				UpperCamelCase            =	hidden_size
				UpperCamelCase            =	num_hidden_layers
				UpperCamelCase            =	num_attention_heads
				UpperCamelCase            =	hidden_act
				UpperCamelCase            =	intermediate_size
				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            =	position_embedding_type
				UpperCamelCase            =	use_cache
				UpperCamelCase            =	max_ad_position_embeddings






class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __init__(					self							, A_							, A_ = "default"							, A_ = None							, )    ->   List[Any]:
				"""simple docstring"""
				super().__init__(A_							, task=A_							, patching_specs=A_			)
				UpperCamelCase            =	config.max_ad_position_embeddings - 1



		@property
		def __UpperCamelCase					(					self			)    ->   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							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	super().generate_dummy_inputs(
				    A_							, batch_size=A_							, seq_length=A_							, is_pair=A_							, framework=A_			)

				# Generate a dummy bbox
				UpperCamelCase            =	[48, 84, 73, 128]

				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            =	input_dict['input_ids'].shape
				UpperCamelCase            =	torch.tensor([*[box] * seq_length]			).tile(A_							, 1							, 1			)
				return input_dict


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
