






import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

snake_case    				=		[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	bool					, UpperCAmelCase_       :	str = None					, UpperCAmelCase_       :	list = None      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    None
       SCREAMING_SNAKE_CASE       :      str							=    os.path.abspath(os.path.join("examples"					, "by_feature"      )      )
       SCREAMING_SNAKE_CASE       :      int							=    os.path.abspath("examples"      )
       for item in os.listdir(UpperCAmelCase_      ):
         if item not in EXCLUDE_EXAMPLES:
           SCREAMING_SNAKE_CASE       :      str							=    os.path.join(UpperCAmelCase_					, UpperCAmelCase_      )
           if os.path.isfile(UpperCAmelCase_      ) and ".py" in item_path:
             with self.subTest(
                 tested_script=UpperCAmelCase_					, feature_script=UpperCAmelCase_					, tested_section="main()" if parser_only else "training_function()"					, ):
               SCREAMING_SNAKE_CASE       :      int							=    compare_against_test(
                   os.path.join(UpperCAmelCase_					, UpperCAmelCase_      )					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )
               SCREAMING_SNAKE_CASE       :      Tuple							=    "\n".join(UpperCAmelCase_      )
               if special_strings is not None:
                 for string in special_strings:
                   SCREAMING_SNAKE_CASE       :      Tuple							=    diff.replace(UpperCAmelCase_					, ""      )
               self.assertEqual(UpperCAmelCase_					, ""      )


     def 				_A       (				self       :	Union[str, Any]      ):
       self.one_complete_example("complete_nlp_example.py"					, UpperCAmelCase_      )
       self.one_complete_example("complete_nlp_example.py"					, UpperCAmelCase_      )






     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    os.path.abspath(os.path.join("examples"					, "cv_example.py"      )      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [
           " " * 16 + "{\n\n",
           " " * 20 + "\"accuracy\": eval_metric[\"accuracy\"],\n\n",
           " " * 20 + "\"f1\": eval_metric[\"f1\"],\n\n",
           " " * 20 + "\"train_loss\": total_loss.item() / len(train_dataloader),\n\n",
           " " * 20 + "\"epoch\": epoch,\n\n",
           " " * 16 + "},\n\n",
           " " * 16 + "step=epoch,\n",
           " " * 12,
           " " * 8 + "for step, batch in enumerate(active_dataloader):\n",
       ]
       self.one_complete_example("complete_cv_example.py"					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )
       self.one_complete_example("complete_cv_example.py"					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )





@mock.patch.dict(os.environ      ,		{'''TESTING_MOCKED_DATALOADERS''': '''1'''}					)
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	False


     @classmethod
     def 				_A       (				cls       :	Any      ):
       super().setUpClass()
       SCREAMING_SNAKE_CASE       :      str							=    tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE       :      Dict							=    os.path.join(cls._tmpdir					, "default_config.yml"      )

       write_basic_config(save_location=cls.configPath      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    ["accelerate", "launch", "--config_file", cls.configPath]


     @classmethod
     def 				_A       (				cls       :	int      ):
       super().tearDownClass()
       shutil.rmtree(cls._tmpdir      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
       run_command(self._launch_args + testargs      )
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir					, "epoch_0"      )      )      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
       SCREAMING_SNAKE_CASE       :      Any							=    run_command(self._launch_args + testargs      )
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir					, "step_2"      )      )      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir					, 'epoch_0'      )}
        '''.split()
       SCREAMING_SNAKE_CASE       :      List[str]							=    run_command(self._launch_args + testargs					, return_stdout=UpperCAmelCase_      )
       self.assertNotIn("epoch 0:"					, UpperCAmelCase_      )
       self.assertIn("epoch 1:"					, UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Any							=    f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir					, 'step_2'      )}
        '''.split()
       SCREAMING_SNAKE_CASE       :      str							=    run_command(self._launch_args + testargs					, return_stdout=UpperCAmelCase_      )
       if torch.cuda.is_available():
         SCREAMING_SNAKE_CASE       :      int							=    torch.cuda.device_count()
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    1
       if num_processes > 1:
         self.assertNotIn("epoch 0:"					, UpperCAmelCase_      )
         self.assertIn("epoch 1:"					, UpperCAmelCase_      )
       else:
         self.assertIn("epoch 0:"					, UpperCAmelCase_      )
         self.assertIn("epoch 1:"					, UpperCAmelCase_      )


     @slow
     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Any							=    "\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        ".split()
       with mock.patch.dict(os.environ					, {"TESTING_MOCKED_DATALOADERS": "0"}      ):
         SCREAMING_SNAKE_CASE       :      Dict							=    run_command(self._launch_args + testargs					, return_stdout=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    re.findall("({.+})"					, UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      str							=    [r for r in results if "accuracy" in r][-1]
         SCREAMING_SNAKE_CASE       :      Tuple							=    ast.literal_eval(UpperCAmelCase_      )
         self.assertGreaterEqual(results["accuracy"]					, 0.75      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    ["examples/by_feature/multi_process_metrics.py"]
       run_command(self._launch_args + testargs      )


     @require_trackers
     @mock.patch.dict(os.environ					, {"WANDB_MODE": "offline"}      )
     def 				_A       (				self       :	Any      ):
       with tempfile.TemporaryDirectory() as tmpdir:
         SCREAMING_SNAKE_CASE       :      Any							=    f'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
         run_command(self._launch_args + testargs      )
         self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_					, "tracking"      )      )      )


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    ["examples/by_feature/gradient_accumulation.py"]
       run_command(self._launch_args + testargs      )






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    ["examples/by_feature/local_sgd.py"]
       run_command(self._launch_args + testargs      )














# Copyright 2022 The HuggingFace Team and The OpenBMB 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case    				=		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CpmAntForCausalLM""",
       """CpmAntModel""",
       """CpmAntPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






