





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_UpperCAmelCase       : Tuple			     =       {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=None				)    ->  str:
    # Initialise PyTorch model
    lowerCamelCase__					:   int      				=	XLNetConfig.from_json_file(_lowerCAmelCase				)

    lowerCamelCase__					:   List[str]      				=	finetuning_task.lower() if finetuning_task is not None else ""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""				)
        lowerCamelCase__					:   Dict      				=	finetuning_task
        lowerCamelCase__					:   Any      				=	GLUE_TASKS_NUM_LABELS[finetuning_task]
        lowerCamelCase__					:   List[str]      				=	XLNetForSequenceClassification(_lowerCAmelCase				)
    elif "squad" in finetuning_task:
        lowerCamelCase__					:   List[Any]      				=	finetuning_task
        lowerCamelCase__					:   Optional[Any]      				=	XLNetForQuestionAnswering(_lowerCAmelCase				)
    else:
        lowerCamelCase__					:   Optional[int]      				=	XLNetLMHeadModel(_lowerCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(_lowerCAmelCase		,			_lowerCAmelCase		,			_lowerCAmelCase				)

    # Save pytorch-model
    lowerCamelCase__					:   List[str]      				=	os.path.join(_lowerCAmelCase		,			_lowerCAmelCase				)
    lowerCamelCase__					:   str      				=	os.path.join(_lowerCAmelCase		,			_lowerCAmelCase				)
    print(F"""Save PyTorch model to {os.path.abspath(_lowerCAmelCase				)}"""				)
    torch.save(model.state_dict()		,			_lowerCAmelCase				)
    print(F"""Save configuration file to {os.path.abspath(_lowerCAmelCase				)}"""				)
    with open(_lowerCAmelCase		,			'w'		,			encoding='utf-8'				) as f:
        f.write(config.to_json_string()				)


if __name__ == "__main__":
 _UpperCAmelCase       : List[Any]			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--xlnet_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained XLNet model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=str,
     required=True,
     help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
 )
 parser.add_argument(
     """--finetuning_task""",
     default=None,
     type=str,
     help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
 )
 _UpperCAmelCase       : Dict			     =       parser.parse_args()
 print(args)

 convert_xlnet_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
 )












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase       : Optional[Any]			     =       {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : int			     =       ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Optional[int]			     =       ["""CLIPFeatureExtractor"""]
    _UpperCAmelCase       : Union[str, Any]			     =       ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : int			     =       [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Tuple			     =       [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Optional[int]			     =       [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

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





