





'''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()

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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=False				,						__UpperCAmelCase=True   )    ->       List[str]:

   '''simple docstring'''







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

   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      __SCREAMING_SNAKE_CASE   				=							cached_file(__UpperCAmelCase				,						__UpperCAmelCase				,						force_download=not use_cached_models   )
   __SCREAMING_SNAKE_CASE   				=							config_class.from_json_file(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							True
   __SCREAMING_SNAKE_CASE   				=							True
   print(f"""Building TensorFlow model from configuration: {config}"""   )
   __SCREAMING_SNAKE_CASE   				=							model_class(__UpperCAmelCase   )

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

   if compare_with_pt_model:
      __SCREAMING_SNAKE_CASE   				=							tf_model(tf_model.dummy_inputs				,						training=__UpperCAmelCase   )  # build the network

      __SCREAMING_SNAKE_CASE   				=							torch.load(__UpperCAmelCase				,						map_location="""cpu"""   )
      __SCREAMING_SNAKE_CASE   				=							pt_model_class.from_pretrained(
          pretrained_model_name_or_path=__UpperCAmelCase				,						config=__UpperCAmelCase				,						state_dict=__UpperCAmelCase   )

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

      __SCREAMING_SNAKE_CASE   				=							pto[0].numpy()
      __SCREAMING_SNAKE_CASE   				=							tfo[0].numpy()
      __SCREAMING_SNAKE_CASE   				=							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(__UpperCAmelCase				,						save_format="""h5"""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=False				,						__UpperCAmelCase=False				,						__UpperCAmelCase=False				,						__UpperCAmelCase=False				,						)    ->       Any:

   '''simple docstring'''







   if args_model_type is None:
      __SCREAMING_SNAKE_CASE   				=							list(MODEL_CLASSES.keys()   )
   else:
      __SCREAMING_SNAKE_CASE   				=							[args_model_type]

   for j, model_type in enumerate(__UpperCAmelCase				,						start=1   ):
      print("""=""" * 100   )
      print(f""" Converting model type {j}/{len(__UpperCAmelCase   )}: {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()   )}."""   )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							MODEL_CLASSES[model_type]

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

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

         if config_shortcut_name in aws_config_map:
            __SCREAMING_SNAKE_CASE   				=							cached_file(__UpperCAmelCase				,						__UpperCAmelCase				,						force_download=not use_cached_models   )
         else:
            __SCREAMING_SNAKE_CASE   				=							config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            __SCREAMING_SNAKE_CASE   				=							cached_file(__UpperCAmelCase				,						__UpperCAmelCase				,						force_download=not use_cached_models   )
         else:
            __SCREAMING_SNAKE_CASE   				=							model_shortcut_name

         if os.path.isfile(__UpperCAmelCase   ):
            __SCREAMING_SNAKE_CASE   				=							"""converted_model"""

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


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,
    )













# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase      :       Dict    =						abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_terminal_summary_main

     __lowerCAmelCase             =       terminalreporter.config.getoption("--make-reports"							)
     if make_reports:
          pytest_terminal_summary_main(__snake_case       ,			id=__snake_case							)






