


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()

_snake_case	:						Dict         =							{
    "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				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=False				,      __lowerCamelCase=True      ):
							if model_type not in MODEL_CLASSES:
														raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

							__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Optional[Any]									=  MODEL_CLASSES[model_type]

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

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

							if compare_with_pt_model:
														__snake_case						:     Any									=  tf_model(tf_model.dummy_inputs				,      training=__lowerCamelCase      )  # build the network

														__snake_case						:     Tuple									=  torch.load(__lowerCamelCase				,      map_location="cpu"      )
														__snake_case						:     Tuple									=  pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=__lowerCamelCase				,      config=__lowerCamelCase				,      state_dict=__lowerCamelCase      )

														with torch.no_grad():
																					__snake_case						:     List[Any]									=  pt_model(**pt_model.dummy_inputs      )

														__snake_case						:     Optional[int]									=  pto[0].numpy()
														__snake_case						:     Union[str, Any]									=  tfo[0].numpy()
														__snake_case						:     Dict									=  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				,      __lowerCamelCase				,      __lowerCamelCase=None				,      __lowerCamelCase=None				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      ):
							if args_model_type is None:
														__snake_case						:     Any									=  list(MODEL_CLASSES.keys()      )
							else:
														__snake_case						:     Optional[int]									=  [args_model_type]

							for j, model_type in enumerate(__lowerCamelCase				,      start=1      ):
														print("=" * 1_0_0      )
														print(F' Converting model type {j}/{len(__lowerCamelCase      )}: {model_type}'      )
														print("=" * 1_0_0      )
														if model_type not in MODEL_CLASSES:
																					raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Any									=  MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					__snake_case						:     Optional[int]									=  list(aws_model_maps.keys()      )
														if config_shortcut_names_or_path is None:
																					__snake_case						:     List[str]									=  model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(__lowerCamelCase				,      __lowerCamelCase      )				,      start=1      ):
																					print("-" * 1_0_0      )
																					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
																												__snake_case						:     Dict									=  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("-" * 1_0_0      )

																					if config_shortcut_name in aws_config_map:
																												__snake_case						:     List[str]									=  cached_file(__lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
																					else:
																												__snake_case						:     List[Any]									=  config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												__snake_case						:     str									=  cached_file(__lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
																					else:
																												__snake_case						:     str									=  model_shortcut_name

																					if os.path.isfile(__lowerCamelCase      ):
																												__snake_case						:     List[Any]									=  "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__":
			_snake_case	:						int         =							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.")
			_snake_case	:						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,
			)




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__		 =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




       output_path.parent.mkdir(parents=__A	,exist_ok=__A		)
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,use_external_data_format=__A	,enable_onnx_checker=__A	,opset_version=__A	,)
       else:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,opset_version=__A	,)







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __snake_case       	=							"""cuda"""
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
       else:
              __snake_case       	=							"""cpu"""
       __snake_case       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               False,
           )	,output_path=output_path / """vae_decoder""" / """model.onnx"""	,ordered_input_names=["""latent_sample""", """return_dict"""]	,output_names=["""sample"""]	,dynamic_axes={
               """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
           }	,opset=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()

    parser.add_argument(
        '''--model_path''',
        type=str,
        required=True,
        help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
    )

    parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--opset''',
        default=14,
        type=int,
        help='''The version of the ONNX operator set to use.''',
    )
    parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

    UpperCamelCase__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

