



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_		:  Any										=  logging.get_logger(__name__)

A_		:  List[str]										=  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_		:  Dict										=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:str       ):






 '''simple docstring'''

 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   a							=		model_type_to_module_name(_a       )

   a							=		importlib.import_module(F""".{module_name}"""			,       "transformers.models"       )
   try:
    return getattr(_a			,       _a       )
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(_a			,       "__name__"			,       _a       ) == 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.
 a							=		importlib.import_module("transformers"       )
 if hasattr(_a			,       _a       ):
  return getattr(_a			,       _a       )

 return None




class 		_lowercase		:

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


    """simple docstring"""






    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(__lowerCAmelCase				)
   def 		A				(   cls      :							Optional[int]				, __lowerCAmelCase      :							Optional[Any]				, **__lowerCAmelCase      :							Tuple				)							->					List[Any]:


    """simple docstring"""






    a							=		kwargs.pop("config"				, __lowerCAmelCase				)
    a							=		kwargs.pop("trust_remote_code"				, __lowerCAmelCase				)
    a							=		True

    a							=		None
    a							=		None

    # First, let's see if we have a preprocessor config.
    # Filter the kwargs for `get_file_from_repo`.
    a							=		{
        key: kwargs[key] for key in inspect.signature(__lowerCAmelCase				).parameters.keys() if key in kwargs
    }
    # Let's start by checking whether the processor class is saved in an image processor
    a							=		get_file_from_repo(
        __lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)
    if preprocessor_config_file is not None:
     a		,	a							=		ImageProcessingMixin.get_image_processor_dict(__lowerCAmelCase				, **__lowerCAmelCase				)
     a							=		config_dict.get("processor_class"				, __lowerCAmelCase				)
     if "AutoProcessor" in config_dict.get("auto_map"				, {}				):
      a							=		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:
     a		,	a							=		FeatureExtractionMixin.get_feature_extractor_dict(__lowerCAmelCase				, **__lowerCAmelCase				)
     a							=		config_dict.get("processor_class"				, __lowerCAmelCase				)
     if "AutoProcessor" in config_dict.get("auto_map"				, {}				):
      a							=		config_dict["auto_map"]["AutoProcessor"]

    if processor_class is None:
     # Next, let's check whether the processor class is saved in a tokenizer
     a							=		get_file_from_repo(
         __lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)
     if tokenizer_config_file is not None:
      with open(__lowerCAmelCase				, encoding="utf-8"				) as reader:
       a							=		json.load(__lowerCAmelCase				)

      a							=		config_dict.get("processor_class"				, __lowerCAmelCase				)
      if "AutoProcessor" in config_dict.get("auto_map"				, {}				):
       a							=		config_dict["auto_map"]["AutoProcessor"]

    if processor_class is None:
     # Otherwise, load config, if it can be loaded.
     if not isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
      a							=		AutoConfig.from_pretrained(
          __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)

     # And check if the config contains the processor class.
     a							=		getattr(__lowerCAmelCase				, "processor_class"				, __lowerCAmelCase				)
     if hasattr(__lowerCAmelCase				, "auto_map"				) and "AutoProcessor" in config.auto_map:
      a							=		config.auto_map["AutoProcessor"]

    if processor_class is not None:
     a							=		processor_class_from_name(__lowerCAmelCase				)

    a							=		processor_auto_map is not None
    a							=		processor_class is not None or type(__lowerCAmelCase				) in PROCESSOR_MAPPING
    a							=		resolve_trust_remote_code(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

    if has_remote_code and trust_remote_code:
     a							=		get_class_from_dynamic_module(
         __lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)
     a							=		kwargs.pop("code_revision"				, __lowerCAmelCase				)
     if os.path.isdir(__lowerCAmelCase				):
      processor_class.register_for_auto_class()
     return processor_class.from_pretrained(
         __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)
    elif processor_class is not None:
     return processor_class.from_pretrained(
         __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)
    # Last try: we use the PROCESSOR_MAPPING.
    elif type(__lowerCAmelCase				) in PROCESSOR_MAPPING:
     return PROCESSOR_MAPPING[type(__lowerCAmelCase				)].from_pretrained(__lowerCAmelCase				, **__lowerCAmelCase				)

    # 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(
         __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)
    except Exception:
     try:
      return AutoImageProcessor.from_pretrained(
          __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)
     except Exception:
      pass

     try:
      return AutoFeatureExtractor.from_pretrained(
          __lowerCAmelCase				, trust_remote_code=__lowerCAmelCase				, **__lowerCAmelCase				)
     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 		A				(   __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Union[str, Any]				)							->					Dict:


    """simple docstring"""






    PROCESSOR_MAPPING.register(__lowerCAmelCase				, __lowerCAmelCase				)







'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   A					       =argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument('--epochs', type=int, default=1)
   parser.add_argument('--per_device_train_batch_size', type=int, default=16)
   parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
   parser.add_argument('--model_name_or_path', type=str)
   parser.add_argument('--learning_rate', type=str, default=5E-5)
   parser.add_argument('--do_train', type=bool, default=True)
   parser.add_argument('--do_eval', type=bool, default=True)
   parser.add_argument('--output_dir', type=str)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # Set up logging
   A					       =logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName('INFO'),
       handlers=[logging.StreamHandler(sys.stdout)],
       format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
   )

   # Load model and tokenizer
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A					       =train_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   A					       =test_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
       args.per_device_eval_batch_size
   )

   # fine optimizer and loss
   A					       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   A					       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   A					       =[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =time.time() - start_train_time

   logger.info('*** Train ***')
   logger.info(f"""train_runtime = {end_train_time}""")
   for key, value in train_results.history.items():
      logger.info(f"""  {key} = {value}""")




