
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()

_SCREAMING_SNAKE_CASE	       =      {
    """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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False							,    SCREAMING_SNAKE_CASE=True )  ->     int:







							"""simple docstring"""


							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     			=       MODEL_CLASSES[model_type]

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

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

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

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

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

														__snake_case     			=       pto[0].numpy()
														__snake_case     			=       tfo[0].numpy()
														__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(SCREAMING_SNAKE_CASE							,    save_format="h5" )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=False							,    SCREAMING_SNAKE_CASE=False							,    SCREAMING_SNAKE_CASE=False							,    SCREAMING_SNAKE_CASE=False							,    )  ->     str:







							"""simple docstring"""


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

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

														__snake_case			,   __snake_case			,   __snake_case			,   __snake_case			,   __snake_case     			=       MODEL_CLASSES[model_type]

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

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(SCREAMING_SNAKE_CASE							,    SCREAMING_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
																												__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(SCREAMING_SNAKE_CASE )}: {model_shortcut_name} - model_type {model_type}''' )
																					print("-" * 1_00 )

																					if config_shortcut_name in aws_config_map:
																												__snake_case     			=       cached_file(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    force_download=not use_cached_models )
																					else:
																												__snake_case     			=       config_shortcut_name

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

																					if os.path.isfile(SCREAMING_SNAKE_CASE ):
																												__snake_case     			=       "converted_model"

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      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.""")
		_SCREAMING_SNAKE_CASE	       =      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 typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
