






'''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    :      Optional[Any]  			=		{
    '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 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Optional[Any]=False   ,    __lowerCAmelCase		:			Union[str, Any]=True						):
						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__         =  MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												lowerCamelCase__         =  cached_file(__lowerCAmelCase   ,    __lowerCAmelCase   ,    force_download=not use_cached_models						)
						lowerCamelCase__         =  config_class.from_json_file(__lowerCAmelCase						)
						lowerCamelCase__         =  True
						lowerCamelCase__         =  True
						print(F'''Building TensorFlow model from configuration: {config}'''						)
						lowerCamelCase__         =  model_class(__lowerCAmelCase						)

						# Load weights from tf checkpoint
						if pytorch_checkpoint_path in aws_config_map.keys():
												lowerCamelCase__         =  cached_file(
												    __lowerCAmelCase   ,    __lowerCAmelCase   ,    force_download=not use_cached_models						)
						# Load PyTorch checkpoint in tf2 model:
						lowerCamelCase__         =  load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase   ,    __lowerCAmelCase						)

						if compare_with_pt_model:
												lowerCamelCase__         =  tf_model(tf_model.dummy_inputs   ,    training=__lowerCAmelCase						)  # build the network

												lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)
												lowerCamelCase__         =  pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=__lowerCAmelCase   ,    config=__lowerCAmelCase   ,    state_dict=__lowerCAmelCase						)

												with torch.no_grad():
																		lowerCamelCase__         =  pt_model(**pt_model.dummy_inputs						)

												lowerCamelCase__         =  pto[0].numpy()
												lowerCamelCase__         =  tfo[0].numpy()
												lowerCamelCase__         =  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(__lowerCAmelCase   ,    save_format="""h5"""						)
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Any=None   ,    __lowerCAmelCase		:			Any=None   ,    __lowerCAmelCase		:			Optional[int]=False   ,    __lowerCAmelCase		:			Any=False   ,    __lowerCAmelCase		:			List[str]=False   ,    __lowerCAmelCase		:			List[Any]=False   ,    ):
						if args_model_type is None:
												lowerCamelCase__         =  list(MODEL_CLASSES.keys()						)
						else:
												lowerCamelCase__         =  [args_model_type]

						for j, model_type in enumerate(__lowerCAmelCase   ,    start=1						):
												print("""=""" * 100						)
												print(F''' Converting model type {j}/{len(__lowerCAmelCase						)}: {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__         =  MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		lowerCamelCase__         =  list(aws_model_maps.keys()						)
												if config_shortcut_names_or_path is None:
																		lowerCamelCase__         =  model_shortcut_names_or_path

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(__lowerCAmelCase   ,    __lowerCAmelCase						)   ,    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__         =  model_shortcut_name
																		elif only_convert_finetuned_models:
																								print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''						)
																								continue
																		print(
																		    F'''    Converting checkpoint {i}/{len(__lowerCAmelCase						)}: {model_shortcut_name} - model_type {model_type}'''						)
																		print("""-""" * 100						)

																		if config_shortcut_name in aws_config_map:
																								lowerCamelCase__         =  cached_file(__lowerCAmelCase   ,    __lowerCAmelCase   ,    force_download=not use_cached_models						)
																		else:
																								lowerCamelCase__         =  config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								lowerCamelCase__         =  cached_file(__lowerCAmelCase   ,    __lowerCAmelCase   ,    force_download=not use_cached_models						)
																		else:
																								lowerCamelCase__         =  model_shortcut_name

																		if os.path.isfile(__lowerCAmelCase						):
																								lowerCamelCase__         =  """converted_model"""

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


if __name__ == "__main__":
							UpperCamelCase    :      str  			=		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    :      str  			=		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 json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase = "▁"   ,_lowerCAmelCase = True   ,_lowerCAmelCase = "<unk>"   ,_lowerCAmelCase = "</s>"   ,_lowerCAmelCase = "<pad>"   ,):
								lowerCamelCase__         =  {
								    """pad""": {"""id""": 0, """token""": pad_token},
								    """eos""": {"""id""": 1, """token""": eos_token},
								    """unk""": {"""id""": 2, """token""": unk_token},
								}

								lowerCamelCase__         =  [None] * len(self.special_tokens       )
								for token_dict in self.special_tokens.values():
														lowerCamelCase__         =  token_dict["""token"""]

								lowerCamelCase__         =  Tokenizer(Unigram()       )

								lowerCamelCase__         =  normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(""" {2,}"""       )   ,""" """       ),
								        normalizers.Lowercase(),
								    ]       )
								lowerCamelCase__         =  pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       ),
								        pre_tokenizers.Digits(individual_digits=_lowerCAmelCase       ),
								        pre_tokenizers.Punctuation(),
								    ]       )
								lowerCamelCase__         =  decoders.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       )

								lowerCamelCase__         =  TemplateProcessing(
								    single=F'''$A {self.special_tokens["eos"]["token"]}'''   ,special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]   ,)

								lowerCamelCase__         =  {
								    """model""": """SentencePieceUnigram""",
								    """replacement""": replacement,
								    """add_prefix_space""": add_prefix_space,
								}

								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  [files]
								self._tokenizer.train(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								self._tokenizer.train_from_iterator(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  json.loads(self._tokenizer.to_str()       )

								lowerCamelCase__         =  self.special_tokens["""unk"""]["""id"""]

								lowerCamelCase__         =  Tokenizer.from_str(json.dumps(_lowerCAmelCase       )       )

