code: 
  
  
  import importlib
  import inspect
  import json
  import os
  from collections import OrderedDict
  
  # Build the list of all feature extractors
  from ...configuration_utils import PretrainedConfig
  from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...image_processing_utils import ImageProcessingMixin
  from ...tokenization_utils import TOKENIZER_CONFIG_FILE
  from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
  from .auto_factory import _LazyAutoMapping
  from .configuration_auto import (
      CONFIG_MAPPING_NAMES,
      AutoConfig,
      model_type_to_module_name,
      replace_list_option_in_docstrings,
  )
  from .feature_extraction_auto import AutoFeatureExtractor
  from .image_processing_auto import AutoImageProcessor
  from .tokenization_auto import AutoTokenizer
  
  
  __a   				=      logging.get_logger(__name__)
  
  __a   				=      OrderedDict(
      [
          ('''align''', '''AlignProcessor'''),
          ('''altclip''', '''AltCLIPProcessor'''),
          ('''bark''', '''BarkProcessor'''),
          ('''blip''', '''BlipProcessor'''),
          ('''blip-2''', '''Blip2Processor'''),
          ('''bridgetower''', '''BridgeTowerProcessor'''),
          ('''chinese_clip''', '''ChineseCLIPProcessor'''),
          ('''clap''', '''ClapProcessor'''),
          ('''clip''', '''CLIPProcessor'''),
          ('''clipseg''', '''CLIPSegProcessor'''),
          ('''flava''', '''FlavaProcessor'''),
          ('''git''', '''GitProcessor'''),
          ('''groupvit''', '''CLIPProcessor'''),
          ('''hubert''', '''Wav2Vec2Processor'''),
          ('''instructblip''', '''InstructBlipProcessor'''),
          ('''layoutlmv2''', '''LayoutLMv2Processor'''),
          ('''layoutlmv3''', '''LayoutLMv3Processor'''),
          ('''markuplm''', '''MarkupLMProcessor'''),
          ('''mctct''', '''MCTCTProcessor'''),
          ('''mgp-str''', '''MgpstrProcessor'''),
          ('''oneformer''', '''OneFormerProcessor'''),
          ('''owlvit''', '''OwlViTProcessor'''),
          ('''pix2struct''', '''Pix2StructProcessor'''),
          ('''sam''', '''SamProcessor'''),
          ('''sew''', '''Wav2Vec2Processor'''),
          ('''sew-d''', '''Wav2Vec2Processor'''),
          ('''speech_to_text''', '''Speech2TextProcessor'''),
          ('''speech_to_text_2''', '''Speech2Text2Processor'''),
          ('''speecht5''', '''SpeechT5Processor'''),
          ('''trocr''', '''TrOCRProcessor'''),
          ('''tvlt''', '''TvltProcessor'''),
          ('''unispeech''', '''Wav2Vec2Processor'''),
          ('''unispeech-sat''', '''Wav2Vec2Processor'''),
          ('''vilt''', '''ViltProcessor'''),
          ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
          ('''wav2vec2''', '''Wav2Vec2Processor'''),
          ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
          ('''wavlm''', '''Wav2Vec2Processor'''),
          ('''whisper''', '''WhisperProcessor'''),
          ('''xclip''', '''XCLIPProcessor'''),
      ]
  )
  
  __a   				=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                    lowercase			:   Tuple					   =		model_type_to_module_name(_UpperCamelCase     )
  
                    lowercase			:   Optional[Any]					   =		importlib.import_module(f""".{module_name}""",    '''transformers.models'''     )
                    try:
                          return getattr(_UpperCamelCase,    _UpperCamelCase     )
                    except AttributeError:
                          continue
  
        for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(_UpperCamelCase,    '''__name__''',    _UpperCamelCase     ) == class_name:
                    return processor
  
      # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
      # init and we return the proper dummy to get an appropriate error message.
        lowercase			:   Dict					   =		importlib.import_module('''transformers'''     )
        if hasattr(_UpperCamelCase,    _UpperCamelCase     ):
              return getattr(_UpperCamelCase,    _UpperCamelCase     )
  
        return None
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self		):
              raise EnvironmentError(
                  '''AutoProcessor is designed to be instantiated '''
                  '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''		)
  
  
        @classmethod
        @replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE__		)
        def        __lowerCamelCase			(							cls							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		):
              lowercase			:   Dict					   =		kwargs.pop('''config'''							,						SCREAMING_SNAKE_CASE__		)
              lowercase			:   Any					   =		kwargs.pop('''trust_remote_code'''							,						SCREAMING_SNAKE_CASE__		)
              lowercase			:   Dict					   =		True
  
              lowercase			:   Dict					   =		None
              lowercase			:   List[Any]					   =		None
  
              # First, let's see if we have a preprocessor config.
              # Filter the kwargs for `get_file_from_repo`.
              lowercase			:   Union[str, Any]					   =		{
                  key: kwargs[key] for key in inspect.signature(SCREAMING_SNAKE_CASE__		).parameters.keys() if key in kwargs
              }
              # Let's start by checking whether the processor class is saved in an image processor
              lowercase			:   int					   =		get_file_from_repo(
                  SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
              if preprocessor_config_file is not None:
                    lowercase			:   Optional[int]					   =		ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    lowercase			:   Dict					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                    if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                          lowercase			:   Any					   =		config_dict['''auto_map''']['''AutoProcessor''']
  
          # If not found, let's check whether the processor class is saved in a feature extractor config
              if preprocessor_config_file is not None and processor_class is None:
                    lowercase			:   Dict					   =		FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    lowercase			:   Optional[int]					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                    if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                          lowercase			:   List[str]					   =		config_dict['''auto_map''']['''AutoProcessor''']
  
              if processor_class is None:
                    # Next, let's check whether the processor class is saved in a tokenizer
                    lowercase			:   Optional[Any]					   =		get_file_from_repo(
                        SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    if tokenizer_config_file is not None:
                          with open(SCREAMING_SNAKE_CASE__							,						encoding='''utf-8'''		) as reader:
                                lowercase			:   str					   =		json.load(SCREAMING_SNAKE_CASE__		)
  
                          lowercase			:   List[Any]					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                          if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                                lowercase			:   Any					   =		config_dict['''auto_map''']['''AutoProcessor''']
  
              if processor_class is None:
                    # Otherwise, load config, if it can be loaded.
                    if not isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                          lowercase			:   str					   =		AutoConfig.from_pretrained(
                              SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
                    # And check if the config contains the processor class.
                    lowercase			:   List[Any]					   =		getattr(SCREAMING_SNAKE_CASE__							,						'''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                    if hasattr(SCREAMING_SNAKE_CASE__							,						'''auto_map'''		) and "AutoProcessor" in config.auto_map:
                          lowercase			:   str					   =		config.auto_map['''AutoProcessor''']
  
              if processor_class is not None:
                    lowercase			:   List[str]					   =		processor_class_from_name(SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   Optional[int]					   =		processor_auto_map is not None
              lowercase			:   int					   =		processor_class is not None or type(SCREAMING_SNAKE_CASE__		) in PROCESSOR_MAPPING
              lowercase			:   Tuple					   =		resolve_trust_remote_code(
                  SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
  
              if has_remote_code and trust_remote_code:
                    lowercase			:   Tuple					   =		get_class_from_dynamic_module(
                        SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    lowercase			:   List[str]					   =		kwargs.pop('''code_revision'''							,						SCREAMING_SNAKE_CASE__		)
                    if os.path.isdir(SCREAMING_SNAKE_CASE__		):
                          processor_class.register_for_auto_class()
                    return processor_class.from_pretrained(
                        SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
              elif processor_class is not None:
                    return processor_class.from_pretrained(
                        SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
              # Last try: we use the PROCESSOR_MAPPING.
              elif type(SCREAMING_SNAKE_CASE__		) in PROCESSOR_MAPPING:
                    return PROCESSOR_MAPPING[type(SCREAMING_SNAKE_CASE__		)].from_pretrained(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
              # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
              # tokenizer.
              try:
                    return AutoTokenizer.from_pretrained(
                        SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
              except Exception:
                    try:
                          return AutoImageProcessor.from_pretrained(
                              SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    except Exception:
                          pass
  
                    try:
                          return AutoFeatureExtractor.from_pretrained(
                              SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                    except Exception:
                          pass
  
              raise ValueError(
                  f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
                  '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
                  '''the files of at least one of those processing classes.'''		)
  
  
  
  
  
  
        @staticmethod
        def        __lowerCamelCase			(							SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
  
  
style_context: 
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
      datefmt='''%m/%d/%Y %H:%M:%S''',
      level=logging.INFO,
  )
  __a   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        np.random.seed(args.seed     )
        torch.manual_seed(args.seed     )
        if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
        parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
        parser.add_argument(
            '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()
  
        logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
        set_seed(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )
  
        # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
        # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
        model.to(args.device     )
  
        if args.fpaa:
              model.half()
  
        logger.info(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]
  
              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  