

"""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()
a__ :	List[Any]						= logging.get_logger(__name__)

a__ :	Dict						= {
    """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""",
}
a__ :	Optional[int]						= [
    """lm_head""",
]





def 				__UpperCamelCase   (    a__: Any ,	a__: Union[str, Any] ,	a__: List[str] ,	a__: List[str] ,	a__: int			)				->			Any:




     """simple docstring"""



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

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

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





def 				__UpperCamelCase   (    a__: Optional[Any] ,	a__: Optional[Any] ,	a__: List[Any]			)				->			Dict:




     """simple docstring"""



     __lowercase												=			[]
     __lowercase												=			fairseq_model.state_dict()

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

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

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

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





def 				__UpperCamelCase   (    a__: Optional[int] ,	a__: Optional[Any]			)				->			str:




     """simple docstring"""



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





def 				__UpperCamelCase   (    a__: Optional[int] ,	a__: List[str] ,	a__: Optional[int] ,	a__: Optional[int]			)				->			Dict:




     """simple docstring"""



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





def 				__UpperCamelCase   (    a__: str ,	a__: Optional[int] ,	a__: Optional[Any] ,	a__: List[Any]			)				->			Any:




     """simple docstring"""



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

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

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





def 				__UpperCamelCase   (    a__: int ,	a__: Union[str, Any] ,	a__: List[Any] ,	a__: List[str]			)				->			Optional[Any]:




     """simple docstring"""



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

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

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





@torch.no_grad()
def 				__UpperCamelCase   (    a__: Optional[Any] ,	a__: Optional[Any] ,	a__: List[Any]=None ,	a__: str=None ,	a__: str=True			)				->			Optional[int]:




     """simple docstring"""



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

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

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

     def load_dataavec(a__: int			):
          __lowercase												=			fairseq.checkpoint_utils.load_model_ensemble_and_task([path]			)
          return model[0].eval()

     __lowercase												=			load_dataavec(lowerCAmelCase__			)

     recursively_load_weights(lowerCAmelCase__ ,	lowerCAmelCase__ ,	not is_finetuned			)

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
       a__ :	Union[str, 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"""
       )
       a__ :	Dict						= 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"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


