
import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_UpperCamelCase:					List[Any]     =logging.get_logger(__name__)

_UpperCamelCase:					int     ={
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_UpperCamelCase:					Dict     =[
    'lm_head',
]



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  for attribute in key.split('.'    ):
    _lowerCAmelCase						=		getattr(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  if weight_type is not None:
    _lowerCAmelCase						=		getattr(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ).shape
  else:
    _lowerCAmelCase						=		hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
        f''' {value.shape} for {full_name}'''    )

  if weight_type == "weight":
    _lowerCAmelCase						=		value
  elif weight_type == "weight_g":
    _lowerCAmelCase						=		value
  elif weight_type == "weight_v":
    _lowerCAmelCase						=		value
  elif weight_type == "bias":
    _lowerCAmelCase						=		value
  else:
    _lowerCAmelCase						=		value

  logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		[]
  _lowerCAmelCase						=		fairseq_model.state_dict()

  if not is_headless:
    _lowerCAmelCase						=		hf_model.dataavec_audio.feature_extractor
    _lowerCAmelCase						=		hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    _lowerCAmelCase						=		hf_model.feature_extractor
    _lowerCAmelCase						=		hf_model.encoder.pos_conv_embed

  for name, value in fairseq_dict.items():
    _lowerCAmelCase						=		False
    if "conv_layers" in name:
      load_conv_layer(
          __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				)
      _lowerCAmelCase						=		True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				)
      _lowerCAmelCase						=		True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          _lowerCAmelCase						=		'data2vec_audio.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('w2v_model.'    )[-1] == name.split('.'    )[0]:
          _lowerCAmelCase						=		True
          if "*" in mapped_key:
            _lowerCAmelCase						=		name.split(__SCREAMING_SNAKE_CASE    )[0].split('.'    )[-2]
            _lowerCAmelCase						=		mapped_key.replace('*'		,				__SCREAMING_SNAKE_CASE    )
          if "weight_g" in name:
            _lowerCAmelCase						=		'weight_g'
          elif "weight_v" in name:
            _lowerCAmelCase						=		'weight_v'
          elif "bias" in name:
            _lowerCAmelCase						=		'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _lowerCAmelCase						=		'weight'
          else:
            _lowerCAmelCase						=		None
          set_recursively(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
        continue
    if not is_used:
      unused_weights.append(__SCREAMING_SNAKE_CASE    )

  logger.warning(f'''Unused weights: {unused_weights}'''    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		path.split('.'    )
  return reduce(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




  _lowerCAmelCase						=		access_by_string(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		hf_weight.data

  if fsq_value.shape != hf_value.shape:
    raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''    )
  _lowerCAmelCase						=		fsq_value
  logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		full_name.split('conv_layers.'    )[-1]
  _lowerCAmelCase						=		name.split('.'    )
  _lowerCAmelCase						=		int(items[0]    )
  _lowerCAmelCase						=		int(items[1]    )

  _lowerCAmelCase						=		name.split('.'    )[-1]
  if type_id == 0:
    _lowerCAmelCase						=		'conv'
  elif type_id == 2:
    _lowerCAmelCase						=		'layer_norm'
  else:
    unused_weights.append(__SCREAMING_SNAKE_CASE    )
    return

  set_weights(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Dict    ):






  """simple docstring"""




  _lowerCAmelCase						=		full_name.split('pos_conv.'    )[-1]
  _lowerCAmelCase						=		name.split('.'    )
  _lowerCAmelCase						=		int(items[0]    )
  _lowerCAmelCase						=		int(items[1]    )

  _lowerCAmelCase						=		name.split('.'    )[-1]
  if type_id != 0:
    unused_weights.append(__SCREAMING_SNAKE_CASE    )
    return
  else:
    _lowerCAmelCase						=		'conv'

  set_weights(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				f'''layers.{layer_id}.{layer_type}.{weight_type}'''    )



@torch.no_grad()
def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]=None		,				__SCREAMING_SNAKE_CASE					:  Tuple=None		,				__SCREAMING_SNAKE_CASE					:  Tuple=True    ):






  """simple docstring"""




  if config_path is not None:
    _lowerCAmelCase						=		DataaVecAudioConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
  else:
    _lowerCAmelCase						=		DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    _lowerCAmelCase						=		DataaVecAudioModel(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		os.path.dirname(__SCREAMING_SNAKE_CASE    )

    _lowerCAmelCase						=		torch.load(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		state_dict['model'].pop('final_proj.0.weight'    )
    _lowerCAmelCase						=		state_dict['model'].pop('final_proj.0.bias'    )
    _lowerCAmelCase						=		os.path.join(__SCREAMING_SNAKE_CASE		,				'converted.pt'    )
    torch.save(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  else:
    _lowerCAmelCase						=		DataaVecAudioForCTC(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		checkpoint_path

  def load_dataavec(__SCREAMING_SNAKE_CASE					:  Dict    ):
    _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
    return model[0].eval()

  _lowerCAmelCase						=		load_dataavec(__SCREAMING_SNAKE_CASE    )

  recursively_load_weights(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				not is_finetuned    )

  _lowerCAmelCase						=		WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'    )

  _lowerCAmelCase						=		load_dataset('patrickvonplaten/librispeech_asr_dummy'		,				'clean'		,				split='validation'    )
  _lowerCAmelCase						=		[x['array'] for x in ds[:4]['audio']]

  _lowerCAmelCase						=		processor(__SCREAMING_SNAKE_CASE		,				return_tensors='pt'		,				padding=__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		inputs.input_values
  _lowerCAmelCase						=		inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    _lowerCAmelCase						=		model(source=__SCREAMING_SNAKE_CASE		,				padding_mask=(1 - attention_mask)		,				mask=__SCREAMING_SNAKE_CASE		,				features_only=__SCREAMING_SNAKE_CASE    )[
        'encoder_out'
    ].transpose(0		,				1    )
    _lowerCAmelCase						=		hf_wavavec(__SCREAMING_SNAKE_CASE		,				attention_mask=__SCREAMING_SNAKE_CASE    )['logits']

    _lowerCAmelCase						=		torch.argmax(__SCREAMING_SNAKE_CASE		,				dim=-1    )
    _lowerCAmelCase						=		processor.batch_decode(__SCREAMING_SNAKE_CASE    )

    print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''    )
  else:
    _lowerCAmelCase						=		model(source=__SCREAMING_SNAKE_CASE		,				padding_mask=(1 - attention_mask)		,				mask=__SCREAMING_SNAKE_CASE		,				features_only=__SCREAMING_SNAKE_CASE    )[
        'layer_results'
    ][-1][0].transpose(0		,				1    )
    _lowerCAmelCase						=		hf_wavavec(__SCREAMING_SNAKE_CASE		,				attention_mask=__SCREAMING_SNAKE_CASE    )['last_hidden_state']

  print(our_output.shape		,				their_output.shape    )
  _lowerCAmelCase						=		torch.max(torch.abs(our_output - their_output    )    ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''    )  # ~ 1e-7
  _lowerCAmelCase						=		torch.allclose(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				atol=1E-3    )
  print('Do both models output the same tensors?'		,				'🔥' if success else '💩'    )
  if not success:
    raise Exception('Something went wRoNg'    )

  hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE    )

  if is_finetuned:
    processor.save_pretrained(__SCREAMING_SNAKE_CASE    )
  else:
    processor.feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       _UpperCamelCase:					List[Any]     =argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       _UpperCamelCase:					Tuple     =parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
