




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()

lowerCAmelCase							:    Optional[Any]  							=							{
    """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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__=False ,				snake_case__=True       )		->      List[str]:







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

			lowerCamelCase       =      MODEL_CLASSES[model_type]

			# Initialise TF model
			if config_file in aws_config_map:
						lowerCamelCase       =      cached_file(snake_case__ ,				snake_case__ ,				force_download=not use_cached_models       )
			lowerCamelCase       =      config_class.from_json_file(snake_case__       )
			lowerCamelCase       =      True
			lowerCamelCase       =      True
			print(F'Building TensorFlow model from configuration: {config}'       )
			lowerCamelCase       =      model_class(snake_case__       )

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

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

						lowerCamelCase       =      torch.load(snake_case__ ,				map_location="""cpu"""       )
						lowerCamelCase       =      pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=snake_case__ ,				config=snake_case__ ,				state_dict=snake_case__       )

						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(snake_case__ ,				save_format="""h5"""       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=None ,				snake_case__=False ,				snake_case__=False ,				snake_case__=False ,				snake_case__=False ,				)		->      Optional[int]:







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

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

									if config_shortcut_name in aws_config_map:
												lowerCamelCase       =      cached_file(snake_case__ ,				snake_case__ ,				force_download=not use_cached_models       )
									else:
												lowerCamelCase       =      config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												lowerCamelCase       =      cached_file(snake_case__ ,				snake_case__ ,				force_download=not use_cached_models       )
									else:
												lowerCamelCase       =      model_shortcut_name

									if os.path.isfile(snake_case__       ):
												lowerCamelCase       =      "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__":
					lowerCAmelCase							:    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.""")
					lowerCAmelCase							:    Dict  							=							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,
					)









"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:    Dict  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Optional[Any]  							=							{
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		"audio-spectrogram-transformer"





				def __init__( self	,				_a=768	,				_a=12	,				_a=12	,				_a=3_072	,				_a="gelu"	,				_a=0.0	,				_a=0.0	,				_a=0.02	,				_a=1e-1_2	,				_a=16	,				_a=True	,				_a=10	,				_a=10	,				_a=1_024	,				_a=128	,				**_a	,				):





							"""simple docstring"""
							super().__init__(**_a				)

							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_hidden_layers
							lowerCamelCase       =      num_attention_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      hidden_act
							lowerCamelCase       =      hidden_dropout_prob
							lowerCamelCase       =      attention_probs_dropout_prob
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      layer_norm_eps
							lowerCamelCase       =      patch_size
							lowerCamelCase       =      qkv_bias
							lowerCamelCase       =      frequency_stride
							lowerCamelCase       =      time_stride
							lowerCamelCase       =      max_length
							lowerCamelCase       =      num_mel_bins



