

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 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											snake_case_          =   inspect.getfile(accelerate.test_utils	)
											snake_case_          =   os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
											snake_case_          =   os.path.sep.join(
											    mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
											snake_case_          =   os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)




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




				@require_multi_gpu
				def    a							( self	):
											print(F'''Found {torch.cuda.device_count()} devices.'''	)
											snake_case_          =   ['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(snake_case				,       env=os.environ.copy()	)




				@require_multi_gpu
				def    a							( self	):
											snake_case_          =   ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__	)]
											with patch_environment(omp_num_threads=1	):
																		execute_subprocess_async(snake_case				,       env=os.environ.copy()	)




				@require_multi_gpu
				def    a							( self	):
											print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''	)
											snake_case_          =   ['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(snake_case				,       env=os.environ.copy()	)


if __name__ == "__main__":
					_UpperCAmelCase		:       Union[str, Any]						    =	Accelerator()
					_UpperCAmelCase		:       Optional[Any]						    =	(accelerator.state.process_index + 2, 10)
					_UpperCAmelCase		:       Dict						    =	torch.randint(0, 10, shape).to(accelerator.device)

					_UpperCAmelCase		:       Any						    =	""""""

					_UpperCAmelCase		:       List[Any]						    =	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)."

					_UpperCAmelCase		:       int						    =	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."
					_UpperCAmelCase		:       Dict						    =	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)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase		:       int						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''roformer'''




				def __init__( self				,       snake_case=5_0000				,       snake_case=None				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1536				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=False				,       snake_case=True				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       **snake_case	)

											snake_case_          =   vocab_size
											snake_case_          =   hidden_size if embedding_size is None else embedding_size
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_act
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_vocab_size
											snake_case_          =   initializer_range
											snake_case_          =   layer_norm_eps
											snake_case_          =   rotary_value
											snake_case_          =   use_cache







class 			lowercase    (	lowercase_      ):




				@property
				def    a							( self	):
											if self.task == "multiple-choice":
																		snake_case_          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		snake_case_          =   {0: 'batch', 1: 'sequence'}
											snake_case_          =   {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]	)




