"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =	{
    "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",
}
SCREAMING_SNAKE_CASE__       =	[
    "lm_head",
]





def  lowerCAmelCase__     (      _UpperCamelCase							:					Union[str, Any]			,      _UpperCamelCase							:					Tuple			,      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					int       )       ->					Any:





						"""simple docstring"""






						for attribute in key.split('.'       ):
												snake_case             =      getattr(__lowercase			,      __lowercase       )

						if weight_type is not None:
												snake_case             =      getattr(__lowercase			,      __lowercase       ).shape
						else:
												snake_case             =      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":
												snake_case             =      value
						elif weight_type == "weight_g":
												snake_case             =      value
						elif weight_type == "weight_v":
												snake_case             =      value
						elif weight_type == "bias":
												snake_case             =      value
						else:
												snake_case             =      value

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





def  lowerCAmelCase__     (      _UpperCamelCase							:					Union[str, Any]			,      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					List[str]       )       ->					Dict:





						"""simple docstring"""






						snake_case             =      []
						snake_case             =      fairseq_model.state_dict()

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

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

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

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





def  lowerCAmelCase__     (      _UpperCamelCase							:					Dict			,      _UpperCamelCase							:					str       )       ->					str:





						"""simple docstring"""






						snake_case             =      path.split('.'       )
						return reduce(__lowercase			,      __lowercase			,      __lowercase       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					List[Any]			,      _UpperCamelCase							:					List[Any]			,      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					Any       )       ->					Optional[Any]:





						"""simple docstring"""






						snake_case             =      access_by_string(__lowercase			,      __lowercase       )
						snake_case             =      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."""       )
						snake_case             =      fsq_value
						logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					List[Any]			,      _UpperCamelCase							:					Tuple			,      _UpperCamelCase							:					List[str]       )       ->					List[Any]:





						"""simple docstring"""






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

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

						set_weights(__lowercase			,      __lowercase			,      __lowercase			,      f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					int			,      _UpperCamelCase							:					Union[str, Any]			,      _UpperCamelCase							:					Optional[int]       )       ->					Optional[Any]:





						"""simple docstring"""






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

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

						set_weights(__lowercase			,      __lowercase			,      __lowercase			,      f"""layers.{layer_id}.{layer_type}.{weight_type}"""       )





@torch.no_grad()
def  lowerCAmelCase__     (      _UpperCamelCase							:					int			,      _UpperCamelCase							:					Union[str, Any]			,      _UpperCamelCase							:					Union[str, Any]=None			,      _UpperCamelCase							:					Optional[Any]=None			,      _UpperCamelCase							:					Optional[Any]=True       )       ->					Optional[Any]:





						"""simple docstring"""






						if config_path is not None:
												snake_case             =      DataaVecAudioConfig.from_pretrained(__lowercase       )
						else:
												snake_case             =      DataaVecAudioConfig()

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

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

						def load_dataavec(_UpperCamelCase							:					Union[str, Any]       ):
												snake_case             =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
												return model[0].eval()

						snake_case             =      load_dataavec(__lowercase       )

						recursively_load_weights(__lowercase			,      __lowercase			,      not is_finetuned       )

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

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

						snake_case             =      processor(__lowercase			,      return_tensors='pt'			,      padding=__lowercase       )

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

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

												snake_case             =      torch.argmax(__lowercase			,      dim=-1       )
												snake_case             =      processor.batch_decode(__lowercase       )

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

						print(our_output.shape			,      their_output.shape       )
						snake_case             =      torch.max(torch.abs(our_output - their_output       )       ).item()
						print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
						snake_case             =      torch.allclose(__lowercase			,      __lowercase			,      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(__lowercase       )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__       =	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"
					)
					SCREAMING_SNAKE_CASE__       =	parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)








'''simple docstring'''


def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> int:
     if not isinstance(__lowercase		,				__lowercase	):
          raise ValueError('''double_factorial() only accepts integral values'''	)
     if n < 0:
          raise ValueError('''double_factorial() not defined for negative values'''	)
     return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






