


"""simple docstring"""



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					inspect.getfile(accelerate.test_utils		)
       snake_case					:   Optional[int]         =					os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
       snake_case					:   str         =					os.path.sep.join(
           mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
       snake_case					:   str         =					os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)







   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices.'		)
       snake_case					:   Any         =					["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
       with patch_environment(omp_num_threads=1		):
           execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy()		)







   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices.'		)
       snake_case					:   Optional[int]         =					["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
       print(F'Command: {cmd}'		)
       with patch_environment(omp_num_threads=1		):
           execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy()		)







   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   List[str]         =					["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
       with patch_environment(omp_num_threads=1		):
           execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy()		)







   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'		)
       snake_case					:   Union[str, Any]         =					["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
       with patch_environment(omp_num_threads=1		,			cuda_visible_devices="0,1"		):
           execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy()		)


if __name__ == "__main__":
    __snake_case         =					Accelerator()
    __snake_case         =					(accelerator.state.process_index + 2, 10)
    __snake_case         =					torch.randint(0, 10, shape).to(accelerator.device)

    __snake_case         =					""""""

    __snake_case         =					accelerator.pad_across_processes(tensor)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
        error_msg += "Padding was not done with the right value (0)."

    __snake_case         =					accelerator.pad_across_processes(tensor, pad_first=True)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    __snake_case         =					accelerator.state.num_processes - accelerator.state.process_index - 1
    if not torch.equal(tensora[index:], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[:index] == 0):
        error_msg += "Padding was not done with the right value (0)."

    # Raise error at the end to make sure we don't stop at the first failure.
    if len(error_msg) > 0:
        raise ValueError(error_msg)









"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





