


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

snake_case_  :	str							  =							{
    """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""",
}
snake_case_  :	Tuple							  =							[
    """lm_head""",
]







def   lowerCamelCase(      a__		,a__		,a__		,a__		,a__):



				for attribute in key.split('''.'''):
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,a__)

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

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







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =fairseq_model.state_dict()

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

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

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

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







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =path.split('''.''')
				return reduce(a__		,a__		,a__)







def   lowerCamelCase(      a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =access_by_string(a__		,a__)
				_SCREAMING_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.")
				_SCREAMING_SNAKE_CASE	     =fsq_value
				logger.info(f"{full_name} was correctly initialized from {hf_weight_path}.")







def   lowerCamelCase(      a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =full_name.split('''conv_layers.''')[-1]
				_SCREAMING_SNAKE_CASE	     =name.split('''.''')
				_SCREAMING_SNAKE_CASE	     =int(items[0])
				_SCREAMING_SNAKE_CASE	     =int(items[1])

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

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







def   lowerCamelCase(      a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =full_name.split('''pos_conv.''')[-1]
				_SCREAMING_SNAKE_CASE	     =name.split('''.''')
				_SCREAMING_SNAKE_CASE	     =int(items[0])
				_SCREAMING_SNAKE_CASE	     =int(items[1])

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

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







@torch.no_grad()
def   lowerCamelCase(      a__		,a__		,a__=None		,a__=None		,a__=True):



				if config_path is not None:
								_SCREAMING_SNAKE_CASE	     =DataaVecAudioConfig.from_pretrained(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =DataaVecAudioConfig()

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

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

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

				_SCREAMING_SNAKE_CASE	     =load_dataavec(a__)

				recursively_load_weights(a__		,a__		,not is_finetuned)

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

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

				_SCREAMING_SNAKE_CASE	     =processor(a__		,return_tensors='''pt'''		,padding=a__)

				_SCREAMING_SNAKE_CASE	     =inputs.input_values
				_SCREAMING_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:
								_SCREAMING_SNAKE_CASE	     =model(source=a__		,padding_mask=(1 - attention_mask)		,mask=a__		,features_only=a__)[
								    "encoder_out"
								].transpose(0		,1)
								_SCREAMING_SNAKE_CASE	     =hf_wavavec(a__		,attention_mask=a__)["logits"]

								_SCREAMING_SNAKE_CASE	     =torch.argmax(a__		,dim=-1)
								_SCREAMING_SNAKE_CASE	     =processor.batch_decode(a__)

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

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

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


if __name__ == "__main__":
							snake_case_  :	List[str]							  =							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'''
							)
							snake_case_  :	List[Any]							  =							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 						lowercase__(A						)				->int:




						"""simple docstring"""



						lowercase__				:      str=       0
						for plain_chr in plain_text:
												lowercase__				:      List[str]=       (
												    ord(A						) + (hash_value << 6) + (hash_value << 16) - hash_value
												)
						return hash_value






