





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()
_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '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',
}
_lowerCamelCase       =	[
    'lm_head',
]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							str    ,      UpperCamelCase__:							int			):
		for attribute in key.split("""."""			):
				SCREAMING_SNAKE_CASE__								=					getattr(UpperCamelCase__    ,      UpperCamelCase__			)

		if weight_type is not None:
				SCREAMING_SNAKE_CASE__								=					getattr(UpperCamelCase__    ,      UpperCamelCase__			).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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Any			):
		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(
						    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      )
						SCREAMING_SNAKE_CASE__								=					True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      )
						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(UpperCamelCase__			)[0].split("""."""			)[-2]
												SCREAMING_SNAKE_CASE__								=					mapped_key.replace("""*"""    ,      UpperCamelCase__			)
										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(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)
								continue
				if not is_used:
						unused_weights.append(UpperCamelCase__			)

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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[Any]    ,      UpperCamelCase__:							Any			):
		SCREAMING_SNAKE_CASE__								=					path.split("""."""			)
		return reduce(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							List[Any]    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							List[Any]			):
		SCREAMING_SNAKE_CASE__								=					access_by_string(UpperCamelCase__    ,      UpperCamelCase__			)
		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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple    ,      UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Union[str, Any]			):
		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(UpperCamelCase__			)
				return

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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Tuple    ,      UpperCamelCase__:							Dict			):
		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(UpperCamelCase__			)
				return
		else:
				SCREAMING_SNAKE_CASE__								=					"""conv"""

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






@torch.no_grad()
def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Tuple=None    ,      UpperCamelCase__:							int=None    ,      UpperCamelCase__:							List[str]=True			):
		if config_path is not None:
				SCREAMING_SNAKE_CASE__								=					DataaVecAudioConfig.from_pretrained(UpperCamelCase__			)
		else:
				SCREAMING_SNAKE_CASE__								=					DataaVecAudioConfig()

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

				SCREAMING_SNAKE_CASE__								=					torch.load(UpperCamelCase__			)
				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(UpperCamelCase__    ,      """converted.pt"""			)
				torch.save(UpperCamelCase__    ,      UpperCamelCase__			)
		else:
				SCREAMING_SNAKE_CASE__								=					DataaVecAudioForCTC(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					checkpoint_path

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

		SCREAMING_SNAKE_CASE__								=					load_dataavec(UpperCamelCase__			)

		recursively_load_weights(UpperCamelCase__    ,      UpperCamelCase__    ,      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(UpperCamelCase__    ,      return_tensors="""pt"""    ,      padding=UpperCamelCase__			)

		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=UpperCamelCase__    ,      padding_mask=(1 - attention_mask)    ,      mask=UpperCamelCase__    ,      features_only=UpperCamelCase__			)[
				    """encoder_out"""
				].transpose(0    ,      1			)
				SCREAMING_SNAKE_CASE__								=					hf_wavavec(UpperCamelCase__    ,      attention_mask=UpperCamelCase__			)["""logits"""]

				SCREAMING_SNAKE_CASE__								=					torch.argmax(UpperCamelCase__    ,      dim=-1			)
				SCREAMING_SNAKE_CASE__								=					processor.batch_decode(UpperCamelCase__			)

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

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


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





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

