


from argparse import ArgumentParser

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


_UpperCAmelCase			:  Optional[Any]     =logging.get_logger(__name__)  # pylint: disable=invalid-name



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				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 			lowerCAmelCase (		lowerCAmelCase_ )-> Tuple:
				lowerCAmelCase_ :      str    				=				pipeline(
				    task=args.task ,   model=args.model if args.model else None ,   config=args.config ,   tokenizer=args.tokenizer ,   device=args.device ,   )
				lowerCAmelCase_ :      Dict    				=				try_infer_format_from_ext(args.input ) if args.format == '''infer''' else args.format
				lowerCAmelCase_ :      List[str]    				=				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 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase							)					->    List[Any]:
										lowerCAmelCase_ :      str    				=				nlp
										lowerCAmelCase_ :      Any    				=				reader




						@staticmethod
						def 						lowercase_ (				__lowercase							)					->    Optional[int]:
										lowerCAmelCase_ :      Union[str, Any]    				=				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=__lowercase   ,						help='''Path to the file to use for inference'''							)
										run_parser.add_argument('''--output'''   ,						type=__lowercase   ,						help='''Path to the file that will be used post to write results.'''							)
										run_parser.add_argument('''--model'''   ,						type=__lowercase   ,						help='''Name or path to the model to instantiate.'''							)
										run_parser.add_argument('''--config'''   ,						type=__lowercase   ,						help='''Name or path to the model\'s config to instantiate.'''							)
										run_parser.add_argument(
										    '''--tokenizer'''   ,						type=__lowercase   ,						help='''Name of the tokenizer to use. (default: same as the model name)'''							)
										run_parser.add_argument(
										    '''--column'''   ,						type=__lowercase   ,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''   ,						)
										run_parser.add_argument(
										    '''--format'''   ,						type=__lowercase   ,						default='''infer'''   ,						choices=PipelineDataFormat.SUPPORTED_FORMATS   ,						help='''Input format to read from'''   ,						)
										run_parser.add_argument(
										    '''--device'''   ,						type=__lowercase   ,						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=__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_				,  lowerCAmelCase_ :      int    				=				self._nlp, []

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

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



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase			:  Any     ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase			:  Optional[Any]     =direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCAmelCase			:  List[str]     =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_UpperCAmelCase			:  Dict     =re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_UpperCAmelCase			:  Any     ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Any    				=				None

				# source code of `config_class`
				lowerCAmelCase_ :      Optional[int]    				=				inspect.getsource(lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				_re_checkpoint.findall(lowerCAmelCase_ )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith('''/''' ):
												lowerCAmelCase_ :      Optional[Any]    				=				ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								lowerCAmelCase_ :      Tuple    				=				f"""https://huggingface.co/{ckpt_name}"""
								if ckpt_link == ckpt_link_from_name:
												lowerCAmelCase_ :      List[str]    				=				ckpt_name
												break

				return checkpoint



def 			lowerCAmelCase (		)-> Optional[Any]:
				lowerCAmelCase_ :      Tuple    				=				[]

				for config_class in list(CONFIG_MAPPING.values() ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								lowerCAmelCase_ :      int    				=				get_checkpoint_from_config_class(lowerCAmelCase_ )

								lowerCAmelCase_ :      Tuple    				=				config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase_ )

				if len(lowerCAmelCase_ ) > 0:
								lowerCAmelCase_ :      List[Any]    				=				'''\n'''.join(sorted(lowerCAmelCase_ ) )
								raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()