






"""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 :				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""",
}
a :				Tuple      					=		[
    """lm_head""",
]





def 						lowercase__(A					,		A					,		A					,		A					,		A						)				->List[Any]:




						"""simple docstring"""



						for attribute in key.split("."						):
												lowercase__				:      List[Any]=       getattr(A					,		A						)

						if weight_type is not None:
												lowercase__				:      Dict=       getattr(A					,		A						).shape
						else:
												lowercase__				:      Union[str, Any]=       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__				:      List[Any]=       value
						elif weight_type == "weight_g":
												lowercase__				:      Optional[int]=       value
						elif weight_type == "weight_v":
												lowercase__				:      Union[str, Any]=       value
						elif weight_type == "bias":
												lowercase__				:      Any=       value
						else:
												lowercase__				:      List[str]=       value

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





def 						lowercase__(A					,		A					,		A						)				->str:




						"""simple docstring"""



						lowercase__				:      Union[str, Any]=       []
						lowercase__				:      Dict=       fairseq_model.state_dict()

						if not is_headless:
												lowercase__				:      Optional[Any]=       hf_model.dataavec_audio.feature_extractor
												lowercase__				:      Any=       hf_model.dataavec_audio.encoder.pos_conv_embed

						else:
												lowercase__				:      int=       hf_model.feature_extractor
												lowercase__				:      Tuple=       hf_model.encoder.pos_conv_embed

						for name, value in fairseq_dict.items():
												lowercase__				:      Optional[Any]=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    A					,		A					,		A					,		A					,		)
																		lowercase__				:      List[str]=       True
												elif "pos_conv" in name:
																		load_pos_conv_layer(
																		    A					,		A					,		A					,		A					,		)
																		lowercase__				:      Tuple=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								if not is_headless:
																														lowercase__				:      Optional[int]=       "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__				:      str=       True
																														if "*" in mapped_key:
																																				lowercase__				:      Any=       name.split(A						)[0].split("."						)[-2]
																																				lowercase__				:      Union[str, Any]=       mapped_key.replace("*"					,		A						)
																														if "weight_g" in name:
																																				lowercase__				:      Union[str, Any]=       "weight_g"
																														elif "weight_v" in name:
																																				lowercase__				:      Union[str, Any]=       "weight_v"
																														elif "bias" in name:
																																				lowercase__				:      Tuple=       "bias"
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase__				:      Union[str, Any]=       "weight"
																														else:
																																				lowercase__				:      str=       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 						lowercase__(A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[Any]=       path.split("."						)
						return reduce(A					,		A					,		A						)





def 						lowercase__(A					,		A					,		A					,		A						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      int=       access_by_string(A					,		A						)
						lowercase__				:      List[Any]=       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__				:      List[str]=       fsq_value
						logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''						)





def 						lowercase__(A					,		A					,		A					,		A						)				->Tuple:




						"""simple docstring"""



						lowercase__				:      int=       full_name.split("conv_layers."						)[-1]
						lowercase__				:      List[Any]=       name.split("."						)
						lowercase__				:      Dict=       int(items[0]						)
						lowercase__				:      str=       int(items[1]						)

						lowercase__				:      List[str]=       name.split("."						)[-1]
						if type_id == 0:
												lowercase__				:      Optional[int]=       "conv"
						elif type_id == 2:
												lowercase__				:      List[Any]=       "layer_norm"
						else:
												unused_weights.append(A						)
												return

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





def 						lowercase__(A					,		A					,		A					,		A						)				->List[Any]:




						"""simple docstring"""



						lowercase__				:      int=       full_name.split("pos_conv."						)[-1]
						lowercase__				:      List[str]=       name.split("."						)
						lowercase__				:      int=       int(items[0]						)
						lowercase__				:      List[Any]=       int(items[1]						)

						lowercase__				:      int=       name.split("."						)[-1]
						if type_id != 0:
												unused_weights.append(A						)
												return
						else:
												lowercase__				:      Tuple=       "conv"

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





@torch.no_grad()
def 						lowercase__(A					,		A					,		A=None					,		A=None					,		A=True						)				->Dict:




						"""simple docstring"""



						if config_path is not None:
												lowercase__				:      List[str]=       DataaVecAudioConfig.from_pretrained(A						)
						else:
												lowercase__				:      Any=       DataaVecAudioConfig()

						if not is_finetuned:
												# Modify final_proj layer name
												lowercase__				:      Tuple=       DataaVecAudioModel(A						)
												lowercase__				:      Dict=       os.path.dirname(A						)

												lowercase__				:      Optional[int]=       torch.load(A						)
												lowercase__				:      str=       state_dict["model"].pop("final_proj.0.weight"						)
												lowercase__				:      Tuple=       state_dict["model"].pop("final_proj.0.bias"						)
												lowercase__				:      str=       os.path.join(A					,		"converted.pt"						)
												torch.save(A					,		A						)
						else:
												lowercase__				:      Dict=       DataaVecAudioForCTC(A						)
												lowercase__				:      List[Any]=       checkpoint_path

						def load_dataavec(A						):
												lowercase__,	lowercase__,	lowercase__				:      int=       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]						)
												return model[0].eval()

						lowercase__				:      Optional[Any]=       load_dataavec(A						)

						recursively_load_weights(A					,		A					,		not is_finetuned						)

						lowercase__				:      List[Any]=       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"						)

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

						lowercase__				:      Optional[int]=       processor(A					,		return_tensors="pt"					,		padding=A						)

						lowercase__				:      Optional[Any]=       inputs.input_values
						lowercase__				:      Optional[Any]=       inputs.attention_mask
						#    input_values = inputs.input_values[:, :-1]
						#    attention_mask = inputs.attention_mask[:, :-1]

						hf_wavavec.eval()
						model.eval()
						if is_finetuned:
												lowercase__				:      List[str]=       model(source=A					,		padding_mask=(1 - attention_mask)					,		mask=A					,		features_only=A						)[
												    "encoder_out"
												].transpose(0					,		1						)
												lowercase__				:      Tuple=       hf_wavavec(A					,		attention_mask=A						)["logits"]

												lowercase__				:      Optional[Any]=       torch.argmax(A					,		dim=-1						)
												lowercase__				:      List[Any]=       processor.batch_decode(A						)

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

						print(our_output.shape					,		their_output.shape						)
						lowercase__				:      Tuple=       torch.max(torch.abs(our_output - their_output						)						).item()
						print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
						lowercase__				:      Optional[int]=       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__":
			a :				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"""
			)
			a :				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"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






