


"""simple docstring"""
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

UpperCAmelCase				=						{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}

def 				_snake_case      (   __snake_case							:      List[str]						, __snake_case							:      Dict						, __snake_case							:      int						, __snake_case							:      Optional[int]						, __snake_case							:      List[Any]=False						, __snake_case							:      Any=True   ):




   """simple docstring"""







   if model_type not in MODEL_CLASSES:
      raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()   )}.'   )

   _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      _lowerCamelCase	:					Tuple   =		cached_file(__snake_case						, __snake_case						, force_download=not use_cached_models   )
   _lowerCamelCase	:					Dict   =		config_class.from_json_file(__snake_case   )
   _lowerCamelCase	:					Tuple   =		True
   _lowerCamelCase	:					Union[str, Any]   =		True
   print(F'Building TensorFlow model from configuration: {config}'   )
   _lowerCamelCase	:					Optional[Any]   =		model_class(__snake_case   )

   # Load weights from tf checkpoint
   if pytorch_checkpoint_path in aws_config_map.keys():
      _lowerCamelCase	:					Tuple   =		cached_file(
          __snake_case						, __snake_case						, force_download=not use_cached_models   )
   # Load PyTorch checkpoint in tf2 model:
   _lowerCamelCase	:					List[str]   =		load_pytorch_checkpoint_in_tfa_model(__snake_case						, __snake_case   )

   if compare_with_pt_model:
      _lowerCamelCase	:					int   =		tf_model(tf_model.dummy_inputs						, training=__snake_case   )  # build the network

      _lowerCamelCase	:					Optional[Any]   =		torch.load(__snake_case						, map_location="""cpu"""   )
      _lowerCamelCase	:					str   =		pt_model_class.from_pretrained(
          pretrained_model_name_or_path=__snake_case						, config=__snake_case						, state_dict=__snake_case   )

      with torch.no_grad():
         _lowerCamelCase	:					Dict   =		pt_model(**pt_model.dummy_inputs   )

      _lowerCamelCase	:					str   =		pto[0].numpy()
      _lowerCamelCase	:					Any   =		tfo[0].numpy()
      _lowerCamelCase	:					Dict   =		np.amax(np.abs(np_pt - np_tf   )   )
      print(F'Max absolute difference between models outputs {diff}'   )
      assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

   # Save pytorch-model
   print(F'Save TensorFlow model to {tf_dump_path}'   )
   tf_model.save_weights(__snake_case						, save_format="""h5"""   )

def 				_snake_case      (   __snake_case							:      Union[str, Any]						, __snake_case							:      Union[str, Any]						, __snake_case							:      Optional[Any]=None						, __snake_case							:      int=None						, __snake_case							:      str=False						, __snake_case							:      Optional[Any]=False						, __snake_case							:      Optional[Any]=False						, __snake_case							:      Tuple=False						, ):




   """simple docstring"""







   if args_model_type is None:
      _lowerCamelCase	:					Union[str, Any]   =		list(MODEL_CLASSES.keys()   )
   else:
      _lowerCamelCase	:					Tuple   =		[args_model_type]

   for j, model_type in enumerate(__snake_case						, start=1   ):
      print("""=""" * 100   )
      print(F' Converting model type {j}/{len(__snake_case   )}: {model_type}'   )
      print("""=""" * 100   )
      if model_type not in MODEL_CLASSES:
         raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()   )}.'   )

      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         _lowerCamelCase	:					Union[str, Any]   =		list(aws_model_maps.keys()   )
      if config_shortcut_names_or_path is None:
         _lowerCamelCase	:					Optional[int]   =		model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(__snake_case						, __snake_case   )						, start=1   ):
         print("""-""" * 100   )
         if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
            if not only_convert_finetuned_models:
               print(F'    Skipping finetuned checkpoint {model_shortcut_name}'   )
               continue
            _lowerCamelCase	:					int   =		model_shortcut_name
         elif only_convert_finetuned_models:
            print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'   )
            continue
         print(
             F'    Converting checkpoint {i}/{len(__snake_case   )}: {model_shortcut_name} - model_type {model_type}'   )
         print("""-""" * 100   )

         if config_shortcut_name in aws_config_map:
            _lowerCamelCase	:					Optional[Any]   =		cached_file(__snake_case						, __snake_case						, force_download=not use_cached_models   )
         else:
            _lowerCamelCase	:					Dict   =		config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            _lowerCamelCase	:					int   =		cached_file(__snake_case						, __snake_case						, force_download=not use_cached_models   )
         else:
            _lowerCamelCase	:					int   =		model_shortcut_name

         if os.path.isfile(__snake_case   ):
            _lowerCamelCase	:					Optional[int]   =		"""converted_model"""

         convert_pt_checkpoint_to_tf(
             model_type=__snake_case						, pytorch_checkpoint_path=__snake_case						, config_file=__snake_case						, tf_dump_path=os.path.join(__snake_case						, model_shortcut_name + """-tf_model.h5"""   )						, compare_with_pt_model=__snake_case						, )
         if remove_cached_files:
            os.remove(__snake_case   )
            os.remove(__snake_case   )


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
 )
 parser.add_argument(
     """--model_type""",
     default=None,
     type=str,
     help=(
         f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
         """convert all the models from AWS."""
     ),
 )
 parser.add_argument(
     """--pytorch_checkpoint_path""",
     default=None,
     type=str,
     help=(
         """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
         """If not given, will download and convert all the checkpoints from AWS."""
     ),
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     help=(
         """The config json file corresponding to the pre-trained model. \n"""
         """This specifies the model architecture. If not given and """
         """--pytorch_checkpoint_path is not given or is a shortcut name """
         """use the configuration associated to the shortcut name on the AWS"""
     ),
 )
 parser.add_argument(
     """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
 )
 parser.add_argument(
     """--use_cached_models""",
     action="""store_true""",
     help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
 )
 parser.add_argument(
     """--remove_cached_files""",
     action="""store_true""",
     help="""Remove pytorch models after conversion (save memory when converting in batches).""",
 )
 parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
 UpperCAmelCase				=						parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )





"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

