





from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

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


try:
							from fastapi import Body, FastAPI, HTTPException
							from fastapi.routing import APIRoute
							from pydantic import BaseModel
							from starlette.responses import JSONResponse
							from uvicorn import run

							UpperCAmelCase_							=				True
except (ImportError, AttributeError):
							UpperCAmelCase_							=				object





							def  __magic_name__  (				*lowercase				,       **lowercase				)				-> Any:

														"""simple docstring"""



														pass




							UpperCAmelCase_							=				False


UpperCAmelCase_							=				logging.get_logger("""transformers-cli/serving""")





def  __magic_name__  (				lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   str  				=     pipeline(
							    task=args.task				,       model=args.model if args.model else None				,       config=args.config				,       tokenizer=args.tokenizer				,       device=args.device				,       )
							return ServeCommand(lowerCAmelCase__				,       args.host				,       args.port				,       args.workers				)
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       dict




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[str]
		__a       :       Optional[List[int]]




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		@staticmethod
		def 						snake_case__				(							snake_case__  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Any  				=     parser.add_parser(
									    """serve""",   help="""CLI tool to run inference requests through REST and GraphQL endpoints."""  )
									serve_parser.add_argument(
									    """--task""",   type=__lowerCAmelCase,   choices=get_supported_tasks(),   help="""The task to run the pipeline on""",   )
									serve_parser.add_argument("""--host""",   type=__lowerCAmelCase,   default="""localhost""",   help="""Interface the server will listen on."""  )
									serve_parser.add_argument("""--port""",   type=__lowerCAmelCase,   default=88_88,   help="""Port the serving will listen to."""  )
									serve_parser.add_argument("""--workers""",   type=__lowerCAmelCase,   default=1,   help="""Number of http workers"""  )
									serve_parser.add_argument("""--model""",   type=__lowerCAmelCase,   help="""Model's name or path to stored model."""  )
									serve_parser.add_argument("""--config""",   type=__lowerCAmelCase,   help="""Model's config name or path to stored model."""  )
									serve_parser.add_argument("""--tokenizer""",   type=__lowerCAmelCase,   help="""Tokenizer name to use."""  )
									serve_parser.add_argument(
									    """--device""",   type=__lowerCAmelCase,   default=-1,   help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""",   )
									serve_parser.set_defaults(func=__lowerCAmelCase  )






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   int  				=     pipeline

									lowercase_	:   Tuple  				=     host
									lowercase_	:   Tuple  				=     port
									lowercase_	:   Tuple  				=     workers

									if not _serve_dependencies_installed:
																raise RuntimeError(
																    """Using serve command requires FastAPI and uvicorn. """
																    """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
																    """Or install FastAPI and uvicorn separately."""  )
									else:
																logger.info(f"""Serving model over {host}:{port}"""  )
																lowercase_	:   int  				=     FastAPI(
																    routes=[
																        APIRoute(
																            """/""",   self.model_info,   response_model=__lowerCAmelCase,   response_class=__lowerCAmelCase,   methods=["""GET"""],   ),
																        APIRoute(
																            """/tokenize""",   self.tokenize,   response_model=__lowerCAmelCase,   response_class=__lowerCAmelCase,   methods=["""POST"""],   ),
																        APIRoute(
																            """/detokenize""",   self.detokenize,   response_model=__lowerCAmelCase,   response_class=__lowerCAmelCase,   methods=["""POST"""],   ),
																        APIRoute(
																            """/forward""",   self.forward,   response_model=__lowerCAmelCase,   response_class=__lowerCAmelCase,   methods=["""POST"""],   ),
																    ],   timeout=6_00,   )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									run(self._app,   host=self.host,   port=self.port,   workers=self.workers  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )






		def 						snake_case__				(							self,   snake_case__ = Body(__lowerCAmelCase,   embed=__lowerCAmelCase  ),   snake_case__ = Body(__lowerCAmelCase,   embed=__lowerCAmelCase  )  )       -> Union[str, Any]:






									"""simple docstring"""



									try:
																lowercase_	:   List[str]  				=     self._pipeline.tokenizer.tokenize(__lowerCAmelCase  )

																if return_ids:
																							lowercase_	:   List[str]  				=     self._pipeline.tokenizer.convert_tokens_to_ids(__lowerCAmelCase  )
																							return ServeTokenizeResult(tokens=__lowerCAmelCase,   tokens_ids=__lowerCAmelCase  )
																else:
																							return ServeTokenizeResult(tokens=__lowerCAmelCase  )

									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(__lowerCAmelCase  )}  )






		def 						snake_case__				(							self,   snake_case__ = Body(__lowerCAmelCase,   embed=__lowerCAmelCase  ),   snake_case__ = Body(__lowerCAmelCase,   embed=__lowerCAmelCase  ),   snake_case__ = Body(__lowerCAmelCase,   embed=__lowerCAmelCase  ),   )       -> str:






									"""simple docstring"""



									try:
																lowercase_	:   int  				=     self._pipeline.tokenizer.decode(__lowerCAmelCase,   __lowerCAmelCase,   __lowerCAmelCase  )
																return ServeDeTokenizeResult(model="""""",   text=__lowerCAmelCase  )
									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(__lowerCAmelCase  )}  )






		async def 						snake_case__				(							self,   snake_case__=Body(__lowerCAmelCase,   embed=__lowerCAmelCase  )  )       -> List[Any]:






									"""simple docstring"""



									if len(__lowerCAmelCase  ) == 0:
																return ServeForwardResult(output=[],   attention=[]  )

									try:
																# Forward through the model
																lowercase_	:   Dict  				=     self._pipeline(__lowerCAmelCase  )
																return ServeForwardResult(output=__lowerCAmelCase  )
									except Exception as e:
																raise HTTPException(5_00,   {"""error""": str(__lowerCAmelCase  )}  )
"""simple docstring"""






import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__lowerCAmelCase       :			str   	=get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures/vocab.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures""")





class 					_A		(			unittest.TestCase      ):
      snake_case__     :  List[Any]				    =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      0

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)
            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig()
                  lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  # save in new folder
                  model_config.save_pretrained(__lowerCAmelCase		)
                  processor.save_pretrained(__lowerCAmelCase		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)		)
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

                  # drop `processor_class` in tokenizer
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""r"""		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                        config_dict.pop("""processor_class"""		)

                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write(json.dumps(__lowerCAmelCase		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

                  # drop `processor_class` in feature extractor
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""r"""		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                        config_dict.pop("""processor_class"""		)

                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write(json.dumps(__lowerCAmelCase		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig(processor_class="""Wav2Vec2Processor"""		)
                  model_config.save_pretrained(__lowerCAmelCase		)
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)
                  # create emtpy sample processor
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write("""{}"""		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)

            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
            self.assertTrue(processor.special_attribute_present		)
            self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)

            lowercase								=      processor.feature_extractor
            self.assertTrue(feature_extractor.special_attribute_present		)
            self.assertEqual(feature_extractor.__class__.__name__						,			"""NewFeatureExtractor"""		)

            lowercase								=      processor.tokenizer
            self.assertTrue(tokenizer.special_attribute_present		)
            if is_tokenizers_available():
                  self.assertEqual(tokenizer.__class__.__name__						,			"""NewTokenizerFast"""		)

                  # Test we can also load the slow version
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase						,			use_fast=__lowerCAmelCase		)
                  lowercase								=      new_processor.tokenizer
                  self.assertTrue(new_tokenizer.special_attribute_present		)
                  self.assertEqual(new_tokenizer.__class__.__name__						,			"""NewTokenizer"""		)
            else:
                  self.assertEqual(tokenizer.__class__.__name__						,			"""NewTokenizer"""		)

      def 							A__			(		self		):







            """simple docstring"""



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

                  # Now that the config is registered, it can be used as any other config with the auto-API
                  lowercase								=      CustomFeatureExtractor.from_pretrained(__lowerCAmelCase		)

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        lowercase								=      os.path.join(__lowerCAmelCase						,			"""vocab.txt"""		)
                        with open(__lowerCAmelCase						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                              vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]		)		)
                        lowercase								=      CustomTokenizer(__lowerCAmelCase		)

                  lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        processor.save_pretrained(__lowerCAmelCase		)
                        lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)
                        self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

            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]
                  if CustomConfig in TOKENIZER_MAPPING._extra_content:
                        del TOKENIZER_MAPPING._extra_content[CustomConfig]
                  if CustomConfig in PROCESSOR_MAPPING._extra_content:
                        del PROCESSOR_MAPPING._extra_content[CustomConfig]

      def 							A__			(		self		):







            """simple docstring"""



            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  Optional[Any]				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  int				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  List[Any]				    =      'AutoFeatureExtractor'
                  snake_case__     :  List[str]				    =      'AutoTokenizer'
                  snake_case__     :  List[str]				    =      False

            try:
                  AutoConfig.register("""custom"""						,			__lowerCAmelCase		)
                  AutoFeatureExtractor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  AutoTokenizer.register(__lowerCAmelCase						,			slow_tokenizer_class=__lowerCAmelCase		)
                  AutoProcessor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  # If remote code is not set, the default is to use local classes.
                  lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertFalse(processor.special_attribute_present		)
                  self.assertFalse(processor.feature_extractor.special_attribute_present		)
                  self.assertFalse(processor.tokenizer.special_attribute_present		)

                  # If remote code is disabled, we load the local ones.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertFalse(processor.special_attribute_present		)
                  self.assertFalse(processor.feature_extractor.special_attribute_present		)
                  self.assertFalse(processor.tokenizer.special_attribute_present		)

                  # If remote is enabled, we load from the Hub.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertTrue(processor.special_attribute_present		)
                  self.assertTrue(processor.feature_extractor.special_attribute_present		)
                  self.assertTrue(processor.tokenizer.special_attribute_present		)

            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]
                  if CustomConfig in TOKENIZER_MAPPING._extra_content:
                        del TOKENIZER_MAPPING._extra_content[CustomConfig]
                  if CustomConfig in PROCESSOR_MAPPING._extra_content:
                        del PROCESSOR_MAPPING._extra_content[CustomConfig]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
            self.assertEqual(processor.__class__.__name__						,			"""BertTokenizerFast"""		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""		)
            self.assertEqual(processor.__class__.__name__						,			"""ConvNextImageProcessor"""		)






@is_staging_test
class 					_A		(			unittest.TestCase      ):
      snake_case__     :  Dict				    =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            lowercase								=      TOKEN
            HfFolder.save_token(__lowerCAmelCase		)

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            try:
                  delete_repo(token=cls._token						,			repo_id="""test-processor"""		)
            except HTTPError:
                  pass

            try:
                  delete_repo(token=cls._token						,			repo_id="""valid_org/test-processor-org"""		)
            except HTTPError:
                  pass

            try:
                  delete_repo(token=cls._token						,			repo_id="""test-dynamic-processor"""		)
            except HTTPError:
                  pass

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)
            with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(
                      os.path.join(__lowerCAmelCase						,			"""test-processor"""		)						,			push_to_hub=__lowerCAmelCase						,			use_auth_token=self._token		)

                  lowercase								=      WavaVecaProcessor.from_pretrained(f'{USER}/test-processor'		)
                  for k, v in processor.feature_extractor.__dict__.items():
                        self.assertEqual(__lowerCAmelCase						,			getattr(new_processor.feature_extractor						,			__lowerCAmelCase		)		)
                  self.assertDictEqual(new_processor.tokenizer.get_vocab()						,			processor.tokenizer.get_vocab()		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(
                      os.path.join(__lowerCAmelCase						,			"""test-processor-org"""		)						,			push_to_hub=__lowerCAmelCase						,			use_auth_token=self._token						,			organization="""valid_org"""						,			)

                  lowercase								=      WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""		)
                  for k, v in processor.feature_extractor.__dict__.items():
                        self.assertEqual(__lowerCAmelCase						,			getattr(new_processor.feature_extractor						,			__lowerCAmelCase		)		)
                  self.assertDictEqual(new_processor.tokenizer.get_vocab()						,			processor.tokenizer.get_vocab()		)







      def 							A__			(		self		):







            """simple docstring"""



            CustomFeatureExtractor.register_for_auto_class()
            CustomTokenizer.register_for_auto_class()
            CustomProcessor.register_for_auto_class()

            lowercase								=      CustomFeatureExtractor.from_pretrained(__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase								=      os.path.join(__lowerCAmelCase						,			"""vocab.txt"""		)
                  with open(__lowerCAmelCase						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                        vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]		)		)
                  lowercase								=      CustomTokenizer(__lowerCAmelCase		)

            lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  create_repo(f'{USER}/test-dynamic-processor'						,			token=self._token		)
                  lowercase								=      Repository(__lowerCAmelCase						,			clone_from=f'{USER}/test-dynamic-processor'						,			token=self._token		)
                  processor.save_pretrained(__lowerCAmelCase		)

                  # This has added the proper auto_map field to the feature extractor config
                  self.assertDictEqual(
                      processor.feature_extractor.auto_map						,			{
                          """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
                          """AutoProcessor""": """custom_processing.CustomProcessor""",
                      }						,			)

                  # This has added the proper auto_map field to the tokenizer config
                  with open(os.path.join(__lowerCAmelCase						,			"""tokenizer_config.json"""		)		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                  self.assertDictEqual(
                      tokenizer_config["""auto_map"""]						,			{
                          """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
                          """AutoProcessor""": """custom_processing.CustomProcessor""",
                      }						,			)

                  # The code has been copied from fixtures
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_feature_extraction.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_tokenization.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_processing.py"""		)		)		)

                  repo.push_to_hub()

            lowercase								=      AutoProcessor.from_pretrained(f'{USER}/test-dynamic-processor'						,			trust_remote_code=__lowerCAmelCase		)
            # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
            self.assertEqual(new_processor.__class__.__name__						,			"""CustomProcessor"""		)



