'''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 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



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




      @require_multi_gpu
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         print(f'''Found {torch.cuda.device_count()} devices.'''				)
         UpperCAmelCase__				:					Dict 					=	['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
         with patch_environment(omp_num_threads=1				):
            execute_subprocess_async(lowerCamelCase_       ,env=os.environ.copy()				)




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



         '''simple docstring'''



         print(f'''Found {torch.cuda.device_count()} devices.'''				)
         UpperCAmelCase__				:					str 					=	['''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(lowerCamelCase_       ,env=os.environ.copy()				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__				)]
         with patch_environment(omp_num_threads=1				):
            execute_subprocess_async(lowerCamelCase_       ,env=os.environ.copy()				)




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



         '''simple docstring'''



         print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''				)
         UpperCAmelCase__				:					Optional[int] 					=	['''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(lowerCamelCase_       ,env=os.environ.copy()				)


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

  UpperCamelCase__  :						Tuple					=      ''

  UpperCamelCase__  :						Optional[int]					=      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__  :						Tuple					=      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)





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__  :						List[str]					=      logging.get_logger(__name__)

UpperCamelCase__  :						str					=      {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Dict   				= '''camembert'''




      def __init__(					self       ,lowerCamelCase_=30522       ,lowerCamelCase_=768       ,lowerCamelCase_=12       ,lowerCamelCase_=12       ,lowerCamelCase_=3072       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1e-12       ,lowerCamelCase_=1       ,lowerCamelCase_=0       ,lowerCamelCase_=2       ,lowerCamelCase_="absolute"       ,lowerCamelCase_=True       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      str:



         '''simple docstring'''



         super().__init__(pad_token_id=lowerCamelCase_       ,bos_token_id=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	vocab_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Tuple 					=	num_hidden_layers
         UpperCAmelCase__				:					List[str] 					=	num_attention_heads
         UpperCAmelCase__				:					int 					=	hidden_act
         UpperCAmelCase__				:					Tuple 					=	intermediate_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					str 					=	max_position_embeddings
         UpperCAmelCase__				:					Union[str, Any] 					=	type_vocab_size
         UpperCAmelCase__				:					int 					=	initializer_range
         UpperCAmelCase__				:					Optional[Any] 					=	layer_norm_eps
         UpperCAmelCase__				:					Optional[Any] 					=	position_embedding_type
         UpperCAmelCase__				:					str 					=	use_cache
         UpperCAmelCase__				:					List[Any] 					=	classifier_dropout




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


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



         '''simple docstring'''



         if self.task == "multiple-choice":
            UpperCAmelCase__				:					Tuple 					=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            UpperCAmelCase__				:					Union[str, Any] 					=	{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]				)




