
'''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()

lowercase			:				str    		=			{
    '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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False		,				snake_case__=True						):




    '''simple docstring'''



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

    A,  A,  A,  A       :   List[Any]					=						MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        A       :   Any					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
    A       :   List[Any]					=						config_class.from_json_file(snake_case__						)
    A       :   int					=						True
    A       :   str					=						True
    print(F'Building TensorFlow model from configuration: {config}'						)
    A       :   List[str]					=						model_class(snake_case__						)

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

    if compare_with_pt_model:
        A       :   Dict					=						tf_model(tf_model.dummy_inputs		,				training=snake_case__						)  # build the network

        A       :   List[Any]					=						torch.load(snake_case__		,				map_location='''cpu'''						)
        A       :   Dict					=						pt_model_class.from_pretrained(
            pretrained_model_name_or_path=snake_case__		,				config=snake_case__		,				state_dict=snake_case__						)

        with torch.no_grad():
            A       :   Union[str, Any]					=						pt_model(**pt_model.dummy_inputs						)

        A       :   Tuple					=						pto[0].numpy()
        A       :   List[str]					=						tfo[0].numpy()
        A       :   Tuple					=						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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=False		,				snake_case__=False		,				snake_case__=False		,				):




    '''simple docstring'''



    if args_model_type is None:
        A       :   int					=						list(MODEL_CLASSES.keys()						)
    else:
        A       :   Any					=						[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()						)}.'						)

        A,  A,  A,  A,  A       :   Optional[int]					=						MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            A       :   Any					=						list(aws_model_maps.keys()						)
        if config_shortcut_names_or_path is None:
            A       :   List[Any]					=						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
                A       :   Tuple					=						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:
                A       :   int					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
            else:
                A       :   Union[str, Any]					=						config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                A       :   Optional[int]					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
            else:
                A       :   List[Any]					=						model_shortcut_name

            if os.path.isfile(snake_case__						):
                A       :   Any					=						'''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__":
      lowercase			:				Dict    		=			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.')
      lowercase			:				Optional[Any]    		=			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,
      )






import itertools
import math




def 				a						(							snake_case__:					int					):


				'''simple docstring'''

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5						,   int(math.sqrt(snake_case__					) + 1					)						,   6					):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					2
				while True:
								if is_prime(snake_case__					):
												yield num
								num += 1




def 				a						(							snake_case__:					int = 10_001					):


				'''simple docstring'''
				return next(itertools.islice(prime_generator()						,   nth - 1						,   snake_case__					)					)


if __name__ == "__main__":
						print(f"{solution() = }")




