
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    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




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


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




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


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__	: Tuple     		=   ["""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(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




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



 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




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


if __name__ == "__main__":
     lowerCamelCase__     :		Any        = Accelerator()
     lowerCamelCase__     :		Union[str, Any]        = (accelerator.state.process_index + 2, 1_0)
     lowerCamelCase__     :		Tuple        = torch.randint(0, 1_0, shape).to(accelerator.device)

     lowerCamelCase__     :		List[Any]        = """"""

     lowerCamelCase__     :		str        = 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)."

     lowerCamelCase__     :		Any        = 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."
     lowerCamelCase__     :		List[str]        = 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)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






