from argparse import ArgumentParser

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


__lowerCamelCase      :			Union[str, Any]							       =		logging.get_logger(__name__)  # pylint: disable=invalid-name



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> Optional[Any]:







  """simple docstring"""





  if not path:
    return "pipe"

  for ext in PipelineDataFormat.SUPPORTED_FORMATS:
    if path.endswith(__UpperCamelCase     ):
      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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple     )							-> List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				pipeline(
      task=args.task			,					model=args.model if args.model else None			,					config=args.config			,					tokenizer=args.tokenizer			,					device=args.device			,					)
  SCREAMING_SNAKE_CASE__      							=				try_infer_format_from_ext(args.input     ) if args.format == """infer""" else args.format
  SCREAMING_SNAKE_CASE__      							=				PipelineDataFormat.from_str(
      format=__UpperCamelCase			,					output_path=args.output			,					input_path=args.input			,					column=args.column if args.column else nlp.default_input_names			,					overwrite=args.overwrite			,					)
  return RunCommand(__UpperCamelCase			,					__UpperCamelCase     )







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    int	,     _lowercase      :    Pipeline	,     _lowercase      :    PipelineDataFormat				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				nlp
     SCREAMING_SNAKE_CASE__      							=				reader




   @staticmethod
   def      __a						(   _lowercase      :    ArgumentParser				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._nlp, []

     for entry in self._reader:
       SCREAMING_SNAKE_CASE__      							=				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:
       SCREAMING_SNAKE_CASE__      							=				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 json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowerCamelCase      :			List[Any]							       =		get_tests_dir('''fixtures''')
__lowerCamelCase      :			Optional[int]							       =		get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowerCamelCase      :			Any							       =		get_tests_dir('''fixtures/dummy-config.json''')







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				0




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE__      							=				WavaVecaConfig()

       # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				).to_dict()

       config_dict.pop("""feature_extractor_type"""				)
       SCREAMING_SNAKE_CASE__      							=				WavaVecaFeatureExtractor(**_lowercase				)

       # save in new folder
       model_config.save_pretrained(_lowercase				)
       config.save_pretrained(_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)

       # make sure private variable is not incorrectly saved
       SCREAMING_SNAKE_CASE__      							=				json.loads(config.to_json_string()				)
       self.assertTrue("""_processor_class""" not in dict_as_saved				)

     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """bert-base is not a local folder and is not a valid model identifier"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""bert-base"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     revision="""aaaaaa"""				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""	,     ):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
     self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     trust_remote_code=_lowercase				)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(_lowercase				):
         AutoFeatureExtractor.register(_lowercase	,     _lowercase				)

       # Now that the config is registered, it can be used as any other config with the auto-API
       SCREAMING_SNAKE_CASE__      							=				CustomFeatureExtractor.from_pretrained(_lowercase				)
       with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
         self.assertIsInstance(_lowercase	,     _lowercase				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




   def      __a						(   self      :    Any				):



     """simple docstring"""






     class __snake_case    (						lowerCamelCase_		):
        lowerCAmelCase_														=							True

     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # If remote code is not set, the default is to use local
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote code is disabled, we load the local one.
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote is enabled, we load from the Hub
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(not hasattr(_lowercase	,     """is_local"""				)				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
