






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			:		int   				=    {
    "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_		(lowercase__						:       Dict     ,					lowercase__						:       int     ,					lowercase__						:       Any     ,					lowercase__						:       str     ,					lowercase__						:       str=False     ,					lowercase__						:       Any=True       )		->    Dict:


				'''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(lowercase__     ,					lowercase__     ,					force_download=not use_cached_models       )
				lowerCAmelCase__      			=						config_class.from_json_file(lowercase__       )
				lowerCAmelCase__      			=						True
				lowerCAmelCase__      			=						True
				print(f'Building TensorFlow model from configuration: {config}'       )
				lowerCAmelCase__      			=						model_class(lowercase__       )

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

				if compare_with_pt_model:
								lowerCAmelCase__      			=						tf_model(tf_model.dummy_inputs     ,					training=lowercase__       )  # build the network

								lowerCAmelCase__      			=						torch.load(lowercase__     ,					map_location='''cpu'''       )
								lowerCAmelCase__      			=						pt_model_class.from_pretrained(
								    pretrained_model_name_or_path=lowercase__     ,					config=lowercase__     ,					state_dict=lowercase__       )

								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(lowercase__     ,					save_format='''h5'''       )





def 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       Tuple=None     ,					lowercase__						:       Optional[Any]=None     ,					lowercase__						:       int=False     ,					lowercase__						:       str=False     ,					lowercase__						:       Optional[Any]=False     ,					lowercase__						:       str=False     ,					)		->    List[str]:


				'''simple docstring'''




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

				for j, model_type in enumerate(lowercase__     ,					start=1       ):
								print('''=''' * 1_00       )
								print(f' Converting model type {j}/{len(lowercase__       )}: {model_type}'       )
								print('''=''' * 1_00       )
								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(lowercase__     ,					lowercase__       )     ,					start=1       ):
												print('''-''' * 1_00       )
												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(lowercase__       )}: {model_shortcut_name} - model_type {model_type}'       )
												print('''-''' * 1_00       )

												if config_shortcut_name in aws_config_map:
																lowerCAmelCase__      			=						cached_file(lowercase__     ,					lowercase__     ,					force_download=not use_cached_models       )
												else:
																lowerCAmelCase__      			=						config_shortcut_name

												if model_shortcut_name in aws_model_maps:
																lowerCAmelCase__      			=						cached_file(lowercase__     ,					lowercase__     ,					force_download=not use_cached_models       )
												else:
																lowerCAmelCase__      			=						model_shortcut_name

												if os.path.isfile(lowercase__       ):
																lowerCAmelCase__      			=						'''converted_model'''

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


if __name__ == "__main__":
			_UpperCAmelCase			:		Optional[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.")
			_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,
			)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





