import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					SCREAMING_SNAKE_CASE							:Union[str, Any]											=       True
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       ''
except Exception as e:
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       False
					SCREAMING_SNAKE_CASE							:int											=       (
					    f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
					)


from PIL import Image  # noqa: E402





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "mel_config.json"






	@register_to_config
	def __init__( self      :				List[Any]							,A      :				int = 2_56							,A      :				int = 2_56							,A      :				int = 2_20_50							,A      :				int = 20_48							,A      :				int = 5_12							,A      :				int = 80							,A      :				int = 32							,):
					__A         =				hop_length
					__A         =				sample_rate
					__A         =				n_fft
					__A         =				top_db
					__A         =				n_iter
					self.set_resolution(A							,A				)
					__A         =				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				int							,A      :				int				):
					__A         =				x_res
					__A         =				y_res
					__A         =				self.y_res
					__A         =				self.x_res * self.hop_length - 1






	def        UpperCamelCase_						( self      :				str							,A      :				str = None							,A      :				np.ndarray = None				):
					if audio_file is not None:
									__A    ,							__A         =				librosa.load(A							,mono=A							,sr=self.sr				)
					else:
									__A         =				raw_audio

					# Pad with silence if necessary.
					if len(self.audio				) < self.x_res * self.hop_length:
									__A         =				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio				),)				)]				)






	def        UpperCamelCase_						( self      :				int				):
					return len(self.audio				) // self.slice_size






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int = 0				):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					return self.sr






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				int				):
					__A         =				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(A				)							,sr=self.sr							,n_fft=self.n_fft							,hop_length=self.hop_length							,n_mels=self.n_mels				)
					__A         =				librosa.power_to_db(A							,ref=np.max							,top_db=self.top_db				)
					__A         =				(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,2_55				) + 0.5).astype(np.uinta				)
					__A         =				Image.fromarray(A				)
					return image







	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Image.Image				):
					__A         =				np.frombuffer(image.tobytes()							,dtype="uint8"				).reshape((image.height, image.width)				)
					__A         =				bytedata.astype("float"				) * self.top_db / 2_55 - self.top_db
					__A         =				librosa.db_to_power(A				)
					__A         =				librosa.feature.inverse.mel_to_audio(
					    A							,sr=self.sr							,n_fft=self.n_fft							,hop_length=self.hop_length							,n_iter=self.n_iter				)
					return audio


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
