





from argparse import ArgumentParser

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


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

def       A__(		__lowerCAmelCase ):
				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 ):
				_snake_case :			Optional[Any]  						=   pipeline(
				    task=args.task	,			model=args.model if args.model else None	,			config=args.config	,			tokenizer=args.tokenizer	,			device=args.device	,			)
				_snake_case :			int  						=   try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				_snake_case :			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 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Pipeline ,				lowerCamelCase_			:		PipelineDataFormat					):





										'''simple docstring'''







										_snake_case :			str  						=   nlp
										_snake_case :			str  						=   reader







						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		ArgumentParser					):





										'''simple docstring'''







										_snake_case :			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=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			:		List[str]					):





										'''simple docstring'''







										_snake_case						,			_snake_case :			List[str]  						=   self._nlp, []

										for entry in self._reader:
														_snake_case :			Optional[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:
														_snake_case :			Union[str, Any]  						=   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_					)













import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowercase_		:				Optional[int]				=						object()

# For specifying empty leaf dict `{}`
lowercase_		:				List[Any]				=						object()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   tuple((re.compile(x + '$' ) for x in qs) )
				for i in range(len(__lowerCAmelCase ) - len(__lowerCAmelCase ) + 1 ):
								_snake_case :			Tuple  						=   [x.match(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase	,			ks[i:] )]
								if matches and all(__lowerCAmelCase ):
												return True
				return False

def       A__(		__lowerCAmelCase ):
				def replace(__lowerCAmelCase	,			__lowerCAmelCase ):
								for rule, replacement in rules:
												if _match(__lowerCAmelCase	,			__lowerCAmelCase ):
																return replacement
								return val

				return replace

def       A__(		):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    (("transformer", "wte", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("attention", "out_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("mlp", "c_fc", "bias"), P('mp' )),
				    (("mlp", "c_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]

def       A__(		__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   _get_partition_rules()
				_snake_case :			Optional[Any]  						=   _replacement_rules(__lowerCAmelCase )
				_snake_case :			str  						=   {k: _unmatched for k in flatten_dict(__lowerCAmelCase )}
				_snake_case :			str  						=   {k: replace(__lowerCAmelCase	,			__lowerCAmelCase ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__lowerCAmelCase ) )






