
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()

_A  =	{
    "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__							(							__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    int=False       ,				__lowerCAmelCase    :    str=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_         =      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   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    Any=None       ,				__lowerCAmelCase    :    Optional[int]=None       ,				__lowerCAmelCase    :    Optional[Any]=False       ,				__lowerCAmelCase    :    Optional[int]=False       ,				__lowerCAmelCase    :    List[Any]=False       ,				__lowerCAmelCase    :    int=False       ,				):




			"""simple docstring"""


			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__":
			_A  =	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.")
			_A  =	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,
			)





from math import loga






def   lowerCamelCase__							(							__lowerCAmelCase    :    int							):




			"""simple docstring"""


			if a < 0:
						raise ValueError("Input value must be a positive integer"							)
			elif isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						raise TypeError("Input value must be a 'int' type"							)
			return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



