




"""simple docstring"""


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Tuple		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  WavaVecaForSequenceClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		List[Any]								=  downstream_dict["""projector.weight"""]
  _snake_case							:		Optional[Any]								=  downstream_dict["""projector.bias"""]
  _snake_case							:		int								=  downstream_dict["""model.post_net.linear.weight"""]
  _snake_case							:		List[str]								=  downstream_dict["""model.post_net.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  WavaVecaForAudioFrameClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[int]								=  downstream_dict["""model.linear.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  WavaVecaForXVector.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[Any]								=  downstream_dict["""connector.weight"""]
  _snake_case							:		Optional[int]								=  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
    _snake_case							:		Tuple								=  downstream_dict[
        F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
    ]
    _snake_case							:		str								=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

  _snake_case							:		Any								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  _snake_case							:		Union[str, Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  _snake_case							:		List[Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  _snake_case							:		List[str]								=  downstream_dict["""objective.W"""]
  return model


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  torch.load(snake_case__		,   map_location="""cpu"""    )

  _snake_case							:		Union[str, Any]								=  checkpoint["""Downstream"""]

  _snake_case							:		Optional[Any]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		Optional[int]								=  WavaVecaFeatureExtractor.from_pretrained(
      snake_case__		,   return_attention_mask=snake_case__		,   do_normalize=snake_case__    )

  _snake_case							:		Optional[int]								=  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""    ):
    _snake_case							:		Optional[Any]								=  convert_classification(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForAudioFrameClassification"""    ):
    _snake_case							:		Dict								=  convert_diarization(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForXVector"""    ):
    _snake_case							:		List[str]								=  convert_xvector(snake_case__		,   snake_case__		,   snake_case__    )
  else:
    raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"    )

  if hf_config.use_weighted_layer_sum:
    _snake_case							:		Optional[int]								=  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(snake_case__    )
  hf_model.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     A_						=				parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''







import copy
import os
from typing import Union

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


_lowercase :  Optional[Any]    							=       logging.get_logger(__name__)

_lowercase :  List[str]    							=       {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_text_model'''
					lowerCAmelCase_        =     ['''past_key_values''']
					lowerCAmelCase_        =     {
					    '''hidden_size''': '''hidden_size''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=5_02_44				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       vocab_size
											lowercase_	:     Tuple													=       hidden_size
											lowercase_	:     Optional[Any]													=       d_kv
											lowercase_	:     List[str]													=       d_ff
											lowercase_	:     List[str]													=       num_layers
											lowercase_	:     Optional[Any]													=       num_heads
											lowercase_	:     Union[str, Any]													=       relative_attention_num_buckets
											lowercase_	:     Optional[int]													=       relative_attention_max_distance
											lowercase_	:     Union[str, Any]													=       dropout_rate
											lowercase_	:     Dict													=       layer_norm_epsilon
											lowercase_	:     Dict													=       initializer_factor
											lowercase_	:     List[Any]													=       use_cache

											lowercase_	:     Optional[int]													=       eos_token_id
											lowercase_	:     Optional[int]													=       decoder_start_token_id

											# for backwards compatibility
											lowercase_	:     Any													=       dense_act_fn

											super().__init__(
											    pad_token_id=__SCREAMING_SNAKE_CASE				,							eos_token_id=__SCREAMING_SNAKE_CASE				,							decoder_start_token_id=__SCREAMING_SNAKE_CASE				,							tie_word_embeddings=__SCREAMING_SNAKE_CASE				,							is_decoder=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				,							)






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Optional[int]													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											# get the text config dict if we are loading from Pix2StructConfig
											if config_dict.get('''model_type'''		) == "pix2struct":
																	lowercase_	:     List[Any]													=       config_dict['''text_config''']

											if "model_type" in config_dict and hasattr(cls				,							'''model_type'''		) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																	    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

											return cls.from_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_vision_model'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=1E-1_0				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=40_96				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       hidden_size
											lowercase_	:     Any													=       patch_embed_hidden_size
											lowercase_	:     List[Any]													=       d_ff
											lowercase_	:     Dict													=       dropout_rate
											lowercase_	:     Any													=       num_hidden_layers
											lowercase_	:     Any													=       num_attention_heads
											lowercase_	:     int													=       initializer_range
											lowercase_	:     Dict													=       initializer_factor
											lowercase_	:     Dict													=       attention_dropout
											lowercase_	:     Optional[Any]													=       layer_norm_eps
											lowercase_	:     str													=       dense_act_fn
											lowercase_	:     Dict													=       seq_len
											lowercase_	:     List[Any]													=       relative_attention_num_buckets
											lowercase_	:     int													=       relative_attention_max_distance
											lowercase_	:     Optional[int]													=       d_kv






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     str													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get('''model_type'''		) == "pix2struct":
																	lowercase_	:     Optional[int]													=       config_dict['''vision_config''']

											if "model_type" in config_dict and hasattr(cls				,							'''model_type'''		) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																	    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

											return cls.from_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct'''
					lowerCAmelCase_        =     True






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE				,							is_encoder_decoder=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											if text_config is None:
																	lowercase_	:     Optional[Any]													=       {}
																	logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''		)

											if vision_config is None:
																	lowercase_	:     Dict													=       {}
																	logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''		)

											lowercase_	:     str													=       PixaStructTextConfig(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       PixaStructVisionConfig(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       self.text_config.decoder_start_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.pad_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.eos_token_id

											lowercase_	:     int													=       initializer_factor
											lowercase_	:     Any													=       initializer_range

											lowercase_	:     str													=       self.initializer_range
											lowercase_	:     str													=       self.initializer_range

											lowercase_	:     int													=       is_vqa






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(text_config=text_config.to_dict()				,							vision_config=vision_config.to_dict()				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       copy.deepcopy(self.__dict__		)
											lowercase_	:     Any													=       self.text_config.to_dict()
											lowercase_	:     Optional[Any]													=       self.vision_config.to_dict()
											lowercase_	:     Optional[int]													=       self.__class__.model_type
											return output



