






'''simple docstring'''

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)

UpperCamelCase							=[
    "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     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   Optional[Any]					    =							None
       UpperCamelCase_				:   Any					    =							os.path.abspath(os.path.join("""examples"""			,     """by_feature""" ) )
       UpperCamelCase_				:   Any					    =							os.path.abspath("""examples""" )
       for item in os.listdir(__lowerCAmelCase ):
             if item not in EXCLUDE_EXAMPLES:
                   UpperCamelCase_				:   Any					    =							os.path.join(__lowerCAmelCase			,     __lowerCAmelCase )
                   if os.path.isfile(__lowerCAmelCase ) and ".py" in item_path:
                         with self.subTest(
                             tested_script=__lowerCAmelCase			,     feature_script=__lowerCAmelCase			,     tested_section="""main()""" if parser_only else """training_function()"""			,     ):
                               UpperCamelCase_				:   Optional[Any]					    =							compare_against_test(
                                   os.path.join(__lowerCAmelCase			,     __lowerCAmelCase )			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
                               UpperCamelCase_				:   Any					    =							"""\n""".join(__lowerCAmelCase )
                               if special_strings is not None:
                                     for string in special_strings:
                                           UpperCamelCase_				:   Optional[int]					    =							diff.replace(__lowerCAmelCase			,     """""" )
                               self.assertEqual(__lowerCAmelCase			,     """""" )
 def   _UpperCAmelCase       (							self ):
       self.one_complete_example("""complete_nlp_example.py"""			,     __lowerCAmelCase )
       self.one_complete_example("""complete_nlp_example.py"""			,     __lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							os.path.abspath(os.path.join("""examples"""			,     """cv_example.py""" ) )
       UpperCamelCase_				:   Tuple					    =							[
           """ """ * 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"""			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       self.one_complete_example("""complete_cv_example.py"""			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )






@mock.patch.dict(os.environ,   {'''TESTING_MOCKED_DATALOADERS''': '''1'''}						)
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Dict      =						False
 @classmethod
 def   _UpperCAmelCase       (							cls ):
       super().setUpClass()
       UpperCamelCase_				:   List[Any]					    =							tempfile.mkdtemp()
       UpperCamelCase_				:   Dict					    =							os.path.join(cls._tmpdir			,     """default_config.yml""" )

       write_basic_config(save_location=cls.configPath )
       UpperCamelCase_				:   List[str]					    =							["""accelerate""", """launch""", """--config_file""", cls.configPath]
 @classmethod
 def   _UpperCAmelCase       (							cls ):
       super().tearDownClass()
       shutil.rmtree(cls._tmpdir )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        ".split()
       run_command(self._launch_args + testargs )
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,     """epoch_0""" ) ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        ".split()
       UpperCamelCase_				:   Union[str, Any]					    =							run_command(self._launch_args + testargs )
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,     """step_2""" ) ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir			,     'epoch_0' )}\n        ".split()
       UpperCamelCase_				:   Optional[Any]					    =							run_command(self._launch_args + testargs			,     return_stdout=__lowerCAmelCase )
       self.assertNotIn("""epoch 0:"""			,     __lowerCAmelCase )
       self.assertIn("""epoch 1:"""			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir			,     'step_2' )}\n        ".split()
       UpperCamelCase_				:   Optional[int]					    =							run_command(self._launch_args + testargs			,     return_stdout=__lowerCAmelCase )
       if torch.cuda.is_available():
             UpperCamelCase_				:   int					    =							torch.cuda.device_count()
       else:
             UpperCamelCase_				:   List[Any]					    =							1
       if num_processes > 1:
             self.assertNotIn("""epoch 0:"""			,     __lowerCAmelCase )
             self.assertIn("""epoch 1:"""			,     __lowerCAmelCase )
       else:
             self.assertIn("""epoch 0:"""			,     __lowerCAmelCase )
             self.assertIn("""epoch 1:"""			,     __lowerCAmelCase )
 @slow
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
       with mock.patch.dict(os.environ			,     {"""TESTING_MOCKED_DATALOADERS""": """0"""} ):
             UpperCamelCase_				:   str					    =							run_command(self._launch_args + testargs			,     return_stdout=__lowerCAmelCase )
             UpperCamelCase_				:   Any					    =							re.findall("""({.+})"""			,     __lowerCAmelCase )
             UpperCamelCase_				:   List[str]					    =							[r for r in results if """accuracy""" in r][-1]
             UpperCamelCase_				:   str					    =							ast.literal_eval(__lowerCAmelCase )
             self.assertGreaterEqual(results["""accuracy"""]			,     0.75 )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[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   _UpperCAmelCase       (							self ):
       with tempfile.TemporaryDirectory() as tmpdir:
             UpperCamelCase_				:   Optional[int]					    =							F"\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            ".split()
             run_command(self._launch_args + testargs )
             self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase			,     """tracking""" ) ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							["""examples/by_feature/gradient_accumulation.py"""]
       run_command(self._launch_args + testargs )





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








'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






