
from argparse import ArgumentParser

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


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







def        A__					(						lowerCamelCase						)  ->	Union[str, Any]:
				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(lowerCamelCase						):
												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        A__					(						lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Optional[Any]	=						pipeline(
				    task=args.task     ,    model=args.model if args.model else None     ,    config=args.config     ,    tokenizer=args.tokenizer     ,    device=args.device     ,    )
				UpperCamelCase_:							int	=						try_infer_format_from_ext(args.input						) if args.format == """infer""" else args.format
				UpperCamelCase_:							Optional[Any]	=						PipelineDataFormat.from_str(
				    format=lowerCamelCase     ,    output_path=args.output     ,    input_path=args.input     ,    column=args.column if args.column else nlp.default_input_names     ,    overwrite=args.overwrite     ,    )
				return RunCommand(lowerCamelCase     ,    lowerCamelCase						)





class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       Any						,	snake_case_ :       Pipeline						,	snake_case_ :       PipelineDataFormat      ):
							UpperCamelCase_:							Dict	=						nlp
							UpperCamelCase_:							Optional[Any]	=						reader

			@staticmethod
			def       lowerCAmelCase__	(       snake_case_ :       ArgumentParser      ):
							UpperCamelCase_:							int	=						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=snake_case_						,	help="""Path to the file to use for inference"""      )
							run_parser.add_argument("""--output"""						,	type=snake_case_						,	help="""Path to the file that will be used post to write results."""      )
							run_parser.add_argument("""--model"""						,	type=snake_case_						,	help="""Name or path to the model to instantiate."""      )
							run_parser.add_argument("""--config"""						,	type=snake_case_						,	help="""Name or path to the model's config to instantiate."""      )
							run_parser.add_argument(
							    """--tokenizer"""						,	type=snake_case_						,	help="""Name of the tokenizer to use. (default: same as the model name)"""      )
							run_parser.add_argument(
							    """--column"""						,	type=snake_case_						,	help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""						,	)
							run_parser.add_argument(
							    """--format"""						,	type=snake_case_						,	default="""infer"""						,	choices=PipelineDataFormat.SUPPORTED_FORMATS						,	help="""Input format to read from"""						,	)
							run_parser.add_argument(
							    """--device"""						,	type=snake_case_						,	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=snake_case_      )




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

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

        # Saving data
							if self._nlp.binary_output:
											UpperCamelCase_:							Optional[int]	=						self._reader.save_binary(snake_case_      )
											logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}'''      )
							else:
											self._reader.save(snake_case_      )








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






