"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from . import (AlbertConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, DPRConfig, ElectraConfig, FlaubertConfig, GPT2Config, LayoutLMConfig,
LxmertConfig, OpenAIGPTConfig, RobertaConfig, T5Config, TFAlbertForPreTraining, TFBartForConditionalGeneration, TFBartForSequenceClassification, TFBertForPreTraining,
TFBertForQuestionAnswering, TFBertForSequenceClassification, TFCamembertForMaskedLM, TFCTRLLMHeadModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering,
TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, TFElectraForPreTraining, TFFlaubertWithLMHeadModel, TFGPT2LMHeadModel, TFLayoutLMForMaskedLM, TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder, TFOpenAIGPTLMHeadModel, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFT5ForConditionalGeneration,
TFTransfoXLLMHeadModel, TFWav2Vec2Model, TFXLMRobertaForMaskedLM, TFXLMWithLMHeadModel, TFXLNetLMHeadModel, TransfoXLConfig, Wav2Vec2Config, Wav2Vec2Model, XLMConfig,
XLMRobertaConfig, XLNetConfig, is_torch_available, load_pytorch_checkpoint_in_tf2_model)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
import argparse
import os
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,
    GPT2LMHeadModel, LayoutLMForMaskedLM, LxmertForPreTraining, LxmertVisualFeatureEncoder, OpenAIGPTLMHeadModel, RobertaForMaskedLM, RobertaForSequenceClassification,
    T5ForConditionalGeneration, TransfoXLLMHeadModel, XLMRobertaForMaskedLM, XLMWithLMHeadModel, XLNetLMHeadModel)
    from .pytorch_utils import is_torch_greater_or_equal_than_1_13
logging.set_verbosity_info()
MODEL_CLASSES = {"bart": (BartConfig, TFBartForConditionalGeneration, TFBartForSequenceClassification, BartForConditionalGeneration),
"bert": (BertConfig, TFBertForPreTraining, BertForPreTraining),
"google-bert/bert-large-uncased-whole-word-masking-finetuned-squad": (BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering),
"google-bert/bert-large-cased-whole-word-masking-finetuned-squad": (BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering),
"google-bert/bert-base-cased-finetuned-mrpc": (BertConfig, TFBertForSequenceClassification, BertForSequenceClassification),
"dpr": (DPRConfig, TFDPRQuestionEncoder, TFDPRContextEncoder, TFDPRReader, DPRQuestionEncoder, DPRContextEncoder, DPRReader),
"openai-community/gpt2": (GPT2Config, TFGPT2LMHeadModel, GPT2LMHeadModel),
"xlnet": (XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel),
"xlm": (XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel),
"xlm-roberta": (XLMRobertaConfig, TFXLMRobertaForMaskedLM, XLMRobertaForMaskedLM),
"transfo-xl": (TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel),
"openai-community/openai-gpt": (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel),
"roberta": (RobertaConfig, TFRobertaForCausalLM, TFRobertaForMaskedLM, RobertaForMaskedLM),
"layoutlm": (LayoutLMConfig, TFLayoutLMForMaskedLM, LayoutLMForMaskedLM),
"FacebookAI/roberta-large-mnli": (RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification),
"camembert": (CamembertConfig, TFCamembertForMaskedLM, CamembertForMaskedLM),
"flaubert": (FlaubertConfig, TFFlaubertWithLMHeadModel, FlaubertWithLMHeadModel),
"distilbert": (DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM),
"distilbert-base-distilled-squad": (DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering),
"lxmert": (LxmertConfig, TFLxmertForPreTraining, LxmertForPreTraining),
"lxmert-visual-feature-encoder": (LxmertConfig, TFLxmertVisualFeatureEncoder, LxmertVisualFeatureEncoder),
"Salesforce/ctrl": (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel),
"albert": (AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining),
"t5": (T5Config, TFT5ForConditionalGeneration, T5ForConditionalGeneration),
"electra": (ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining),
"wav2vec2": (Wav2Vec2Config, TFWav2Vec2Model, Wav2Vec2Model)}
def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True):
    if model_type not in MODEL_CLASSES: raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.")
    config_class, model_class, pt_model_class, aws_config_map = MODEL_CLASSES[model_type]
    if config_file in aws_config_map: config_file = cached_file(config_file, CONFIG_NAME, force_download=not use_cached_models)
    config = config_class.from_json_file(config_file)
    config.output_hidden_states = True
    config.output_attentions = True
    print(f"Building TensorFlow model from configuration: {config}")
    tf_model = model_class(config)
    if pytorch_checkpoint_path in aws_config_map.keys(): pytorch_checkpoint_path = cached_file(pytorch_checkpoint_path, WEIGHTS_NAME, force_download=not use_cached_models)
    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
    if compare_with_pt_model:
        tfo = tf_model(tf_model.dummy_inputs, training=False)
        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
        state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu", **weights_only_kwarg)
        pt_model = pt_model_class.from_pretrained(pretrained_model_name_or_path=None, config=config, state_dict=state_dict)
        with torch.no_grad(): pto = pt_model(**pt_model.dummy_inputs)
        np_pt = pto[0].numpy()
        np_tf = tfo[0].numpy()
        diff = 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}"
    print(f"Save TensorFlow model to {tf_dump_path}")
    tf_model.save_weights(tf_dump_path, save_format="h5")
def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None, compare_with_pt_model=False, use_cached_models=False, remove_cached_files=False, only_convert_finetuned_models=False):
    if args_model_type is None: model_types = list(MODEL_CLASSES.keys())
    else: model_types = [args_model_type]
    for j, model_type in enumerate(model_types, start=1):
        print("=" * 100)
        print(f" Converting model type {j}/{len(model_types)}: {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())}.")
        config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
        if model_shortcut_names_or_path is None: model_shortcut_names_or_path = list(aws_model_maps.keys())
        if config_shortcut_names_or_path is None: config_shortcut_names_or_path = model_shortcut_names_or_path
        for i, (model_shortcut_name, config_shortcut_name) in enumerate(zip(model_shortcut_names_or_path, config_shortcut_names_or_path), 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
                model_type = model_shortcut_name
            elif only_convert_finetuned_models:
                print(f"    Skipping not finetuned checkpoint {model_shortcut_name}")
                continue
            print(f"    Converting checkpoint {i}/{len(aws_config_map)}: {model_shortcut_name} - model_type {model_type}")
            print("-" * 100)
            if config_shortcut_name in aws_config_map: config_file = cached_file(config_shortcut_name, CONFIG_NAME, force_download=not use_cached_models)
            else: config_file = config_shortcut_name
            if model_shortcut_name in aws_model_maps: model_file = cached_file(model_shortcut_name, WEIGHTS_NAME, force_download=not use_cached_models)
            else: model_file = model_shortcut_name
            if os.path.isfile(model_shortcut_name): model_shortcut_name = "converted_model"
            convert_pt_checkpoint_to_tf(model_type=model_type, pytorch_checkpoint_path=model_file, config_file=config_file, tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"), compare_with_pt_model=compare_with_pt_model)
            if remove_cached_files:
                os.remove(config_file)
                os.remove(model_file)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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. \nThis 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.")
    args = parser.parse_args()
    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)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
