"""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()
UpperCamelCase_    :    Any											=						logging.get_logger(__name__)

UpperCamelCase_    :    List[Any]											=						{
    """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""",
}
UpperCamelCase_    :    Any											=						[
    """lm_head""",
]


def 					a__ (						UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     Dict     )->		Optional[Any]:
						for attribute in key.split("""."""     ):
												UpperCAmelCase__   		=						getattr(UpperCAmelCase__						,     UpperCAmelCase__     )

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

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


def 					a__ (						UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     int     )->		Tuple:
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						fairseq_model.state_dict()

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

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

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


def 					a__ (						UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     Any     )->		Dict:
						UpperCAmelCase__   		=						access_by_string(UpperCAmelCase__						,     UpperCAmelCase__     )
						UpperCAmelCase__   		=						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.'     )
						UpperCAmelCase__   		=						fsq_value
						logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'     )


def 					a__ (						UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Optional[Any]     )->		int:
						UpperCAmelCase__   		=						full_name.split("""conv_layers."""     )[-1]
						UpperCAmelCase__   		=						name.split("""."""     )
						UpperCAmelCase__   		=						int(items[0]     )
						UpperCAmelCase__   		=						int(items[1]     )

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

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


def 					a__ (						UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     Optional[int]     )->		List[str]:
						UpperCAmelCase__   		=						full_name.split("""pos_conv."""     )[-1]
						UpperCAmelCase__   		=						name.split("""."""     )
						UpperCAmelCase__   		=						int(items[0]     )
						UpperCAmelCase__   		=						int(items[1]     )

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

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


@torch.no_grad()
def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     Optional[Any]=None						,     UpperCAmelCase__    :     Any=None						,     UpperCAmelCase__    :     Dict=True     )->		Optional[Any]:
						if config_path is not None:
												UpperCAmelCase__   		=						DataaVecAudioConfig.from_pretrained(UpperCAmelCase__     )
						else:
												UpperCAmelCase__   		=						DataaVecAudioConfig()

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

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

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

						UpperCAmelCase__   		=						load_dataavec(UpperCAmelCase__     )

						recursively_load_weights(UpperCAmelCase__						,     UpperCAmelCase__						,     not is_finetuned     )

						UpperCAmelCase__   		=						WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""     )

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

						UpperCAmelCase__   		=						processor(UpperCAmelCase__						,     return_tensors="""pt"""						,     padding=UpperCAmelCase__     )

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

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

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

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

						print(our_output.shape						,     their_output.shape     )
						UpperCAmelCase__   		=						torch.max(torch.abs(our_output - their_output     )     ).item()
						print(f'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
						UpperCAmelCase__   		=						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__":
						UpperCamelCase_    :    Dict											=						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"""
						)
						UpperCamelCase_    :    Tuple											=						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 numpy as np  # noqa: E402

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


try:
						import librosa  # noqa: E402

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    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      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

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

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

