
'''simple docstring'''





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


a__						:      Optional[int]   				=				logging.get_logger(__name__)

a__						:      Dict   				=				OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

a__						:      Any   				=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 				snake_case							(						UpperCAmelCase		)->	Union[str, Any]:







      """simple docstring"""
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  __A						=	model_type_to_module_name(lowerCAmelCase_		)

                  __A						=	importlib.import_module(f'.{module_name}'					,     'transformers.models'		)
                  try:
                        return getattr(lowerCAmelCase_					,     lowerCAmelCase_		)
                  except AttributeError:
                        continue

      for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
            if getattr(lowerCAmelCase_					,     '__name__'					,     lowerCAmelCase_		) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      __A						=	importlib.import_module('transformers'		)
      if hasattr(lowerCAmelCase_					,     lowerCAmelCase_		):
            return getattr(lowerCAmelCase_					,     lowerCAmelCase_		)

      return None





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase = None					,     UpperCAmelCase = False					,     UpperCAmelCase = False					,     UpperCAmelCase = None					,     UpperCAmelCase = None					,     UpperCAmelCase = None					,     UpperCAmelCase = False					,     **UpperCAmelCase					,     )->	Dict:







      """simple docstring"""
      __A						=	get_file_from_repo(
          lowerCAmelCase_					,     lowerCAmelCase_					,     cache_dir=lowerCAmelCase_					,     force_download=lowerCAmelCase_					,     resume_download=lowerCAmelCase_					,     proxies=lowerCAmelCase_					,     use_auth_token=lowerCAmelCase_					,     revision=lowerCAmelCase_					,     local_files_only=lowerCAmelCase_					,     )
      if resolved_config_file is None:
            logger.info(
                'Could not locate the feature extractor configuration file, will try to use the model config instead.'		)
            return {}

      with open(lowerCAmelCase_					,     encoding='utf-8'		) as reader:
            return json.load(lowerCAmelCase_		)


class        UpperCamelCase__     :






 def __init__(	self     :int	) ->	Dict:







       '''simple docstring'''



       raise EnvironmentError(
           'AutoFeatureExtractor is designed to be instantiated '
           'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'	)






 @classmethod
 @replace_list_option_in_docstrings(__a	)
 def 		lowercase_  (	cls     :str		, _A     :Tuple		, **_A     :Optional[int]	) ->	Any:







       '''simple docstring'''



       __A						=	kwargs.pop('config'		, __a	)
       __A						=	kwargs.pop('trust_remote_code'		, __a	)
       __A						=	True

       __A						=	FeatureExtractionMixin.get_feature_extractor_dict(__a		, **__a	)
       __A						=	config_dict.get('feature_extractor_type'		, __a	)
       __A						=	None
       if "AutoFeatureExtractor" in config_dict.get('auto_map'		, {}	):
             __A						=	config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

       # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
       if feature_extractor_class is None and feature_extractor_auto_map is None:
             if not isinstance(__a		, __a	):
                   __A						=	AutoConfig.from_pretrained(__a		, **__a	)
             # It could be in `config.feature_extractor_type``
             __A						=	getattr(__a		, 'feature_extractor_type'		, __a	)
             if hasattr(__a		, 'auto_map'	) and "AutoFeatureExtractor" in config.auto_map:
                   __A						=	config.auto_map["""AutoFeatureExtractor"""]

       if feature_extractor_class is not None:
             __A						=	feature_extractor_class_from_name(__a	)

       __A						=	feature_extractor_auto_map is not None
       __A						=	feature_extractor_class is not None or type(__a	) in FEATURE_EXTRACTOR_MAPPING
       __A						=	resolve_trust_remote_code(
           __a		, __a		, __a		, __a	)

       if has_remote_code and trust_remote_code:
             __A						=	get_class_from_dynamic_module(
                 __a		, __a		, **__a	)
             __A						=	kwargs.pop('code_revision'		, __a	)
             if os.path.isdir(__a	):
                   feature_extractor_class.register_for_auto_class()
             return feature_extractor_class.from_dict(__a		, **__a	)
       elif feature_extractor_class is not None:
             return feature_extractor_class.from_dict(__a		, **__a	)
       # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
       elif type(__a	) in FEATURE_EXTRACTOR_MAPPING:
             __A						=	FEATURE_EXTRACTOR_MAPPING[type(__a	)]
             return feature_extractor_class.from_dict(__a		, **__a	)

       raise ValueError(
           F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
           F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
           F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()	)}'	)






 @staticmethod
 def 		lowercase_  (	_A     :Any		, _A     :Union[str, Any]	) ->	Dict:







       '''simple docstring'''



       FEATURE_EXTRACTOR_MAPPING.register(__a		, __a	)














import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							else:
														raise ValueError(F"Task {task} not supported."					)

							print(F"Building PyTorch model from configuration: {config}"					)
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

							# Save pytorch-model (weights and configuration)
							print(F"Save PyTorch model to {pytorch_dump_path}"					)
							model.save_pretrained(lowerCAmelCase_					)

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

							print("""Used relative position embeddings:"""				,				model.config.reset_position_index_per_cell					)


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
	)
	parser.add_argument(
	    '''--reset_position_index_per_cell''',
	    default=False,
	    action='''store_true''',
	    help='''Whether to use relative position embeddings or not. Defaults to True.''',
	)
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--tapas_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCamelCase       :       int        =				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)