from argparse import ArgumentParser

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


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






def _lowerCamelCase  (		snake_case  ):
							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(snake_case  ):
																					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  (		snake_case  ):
							_lowerCAmelCase							=							pipeline(
							    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
							_lowerCAmelCase							=							try_infer_format_from_ext(args.input  ) if args.format == 'infer' else args.format
							_lowerCAmelCase							=							PipelineDataFormat.from_str(
							    format=snake_case     , output_path=args.output     , input_path=args.input     , column=args.column if args.column else nlp.default_input_names     , overwrite=args.overwrite     , )
							return RunCommand(snake_case     , snake_case  )



class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Tuple  ,  lowercase__	:	Pipeline  ,  lowercase__	:	PipelineDataFormat						):
										_lowerCAmelCase							=							nlp
										_lowerCAmelCase							=							reader





			@staticmethod
			def   SCREAMING_SNAKE_CASE__     (       lowercase__	:	ArgumentParser						):
										_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self._nlp, []

										for entry in self._reader:
																	_lowerCAmelCase							=							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							=							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 tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowercase:	str       			=		'''sshleifer/bart-tiny-random'''
_lowercase:	Union[str, Any]       			=		'''patrickvonplaten/t5-tiny-random'''



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return AutoConfig.from_pretrained(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.num_hidden_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  self.teacher_config.encoder_layers						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										with self.assertRaises(lowercase__						):
																	create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=lowercase__  ,  d=lowercase__						)






