



"""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()

lowercase__       =	{
    "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__     ( _lowerCamelCase					:			str	,							_lowerCamelCase					:			Optional[Any]	,							_lowerCamelCase					:			Optional[int]	,							_lowerCamelCase					:			Union[str, Any]	,							_lowerCamelCase					:			Optional[Any]=False	,							_lowerCamelCase					:			str=True       ):






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

							__a		:		Dict						=							MODEL_CLASSES[model_type]

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

							# Load weights from tf checkpoint
							if pytorch_checkpoint_path in aws_config_map.keys():
														__a		:		int						=							cached_file(
														    snake_case__	,							snake_case__	,							force_download=not use_cached_models       )
							# Load PyTorch checkpoint in tf2 model:
							__a		:		Union[str, Any]						=							load_pytorch_checkpoint_in_tfa_model(snake_case__	,							snake_case__       )

							if compare_with_pt_model:
														__a		:		Tuple						=							tf_model(tf_model.dummy_inputs	,							training=snake_case__       )  # build the network

														__a		:		Optional[Any]						=							torch.load(snake_case__	,							map_location="""cpu"""       )
														__a		:		List[Any]						=							pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=snake_case__	,							config=snake_case__	,							state_dict=snake_case__       )

														with torch.no_grad():
																					__a		:		Optional[Any]						=							pt_model(**pt_model.dummy_inputs       )

														__a		:		str						=							pto[0].numpy()
														__a		:		Optional[int]						=							tfo[0].numpy()
														__a		:		int						=							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(snake_case__	,							save_format="""h5"""       )





def    __magic_name__     ( _lowerCamelCase					:			Dict	,							_lowerCamelCase					:			Optional[Any]	,							_lowerCamelCase					:			int=None	,							_lowerCamelCase					:			Dict=None	,							_lowerCamelCase					:			Tuple=False	,							_lowerCamelCase					:			Optional[Any]=False	,							_lowerCamelCase					:			Dict=False	,							_lowerCamelCase					:			Any=False	,							):






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

							for j, model_type in enumerate(snake_case__	,							start=1       ):
														print("""=""" * 1_0_0       )
														print(F''' Converting model type {j}/{len(snake_case__       )}: {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()       )}.'''       )

														__a		:		Tuple						=							MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					__a		:		Optional[Any]						=							list(aws_model_maps.keys()       )
														if config_shortcut_names_or_path is None:
																					__a		:		Tuple						=							model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(snake_case__	,							snake_case__       )	,							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
																												__a		:		Tuple						=							model_shortcut_name
																					elif only_convert_finetuned_models:
																												print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''       )
																												continue
																					print(
																					    F'''    Converting checkpoint {i}/{len(snake_case__       )}: {model_shortcut_name} - model_type {model_type}'''       )
																					print("""-""" * 1_0_0       )

																					if config_shortcut_name in aws_config_map:
																												__a		:		Dict						=							cached_file(snake_case__	,							snake_case__	,							force_download=not use_cached_models       )
																					else:
																												__a		:		List[Any]						=							config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												__a		:		str						=							cached_file(snake_case__	,							snake_case__	,							force_download=not use_cached_models       )
																					else:
																												__a		:		Union[str, Any]						=							model_shortcut_name

																					if os.path.isfile(snake_case__       ):
																												__a		:		str						=							"""converted_model"""

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


if __name__ == "__main__":
	lowercase__       =	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.")
	lowercase__       =	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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=lowercase_)
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			field(default="audio-classification"						,     metadata={"include_in_asdict_even_if_is_default": True})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"audio": Audio()})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"labels": ClassLabel})
       lowerCamelCase				:					str								=			"audio"
       lowerCamelCase				:					str								=			"labels"



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''     )
            if not isinstance(features[self.label_column]      ,    a__     ):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''     )
            __snake_case      :Any 							=    copy.deepcopy(self     )
            __snake_case      :List[Any] 							=    self.label_schema.copy()
            __snake_case      :List[Any] 							=    features[self.label_column]
            __snake_case      :Optional[Any] 							=    label_schema
            return task_template



       @property
       def        __lowercase    (				self     )			->   Dict[str, str]:






            '''simple docstring'''





            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }


