






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_a      	=  logging.get_logger(__name__)

_a      	=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
_a      	=  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						for attribute in key.split('.'	):
												_UpperCAmelCase									=			getattr(__lowerCAmelCase ,		__lowerCAmelCase	)

						if weight_type is not None:
												_UpperCAmelCase									=			getattr(__lowerCAmelCase ,		__lowerCAmelCase	).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    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						_UpperCAmelCase									=			fairseq_model.state_dict()

						_UpperCAmelCase									=			hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase									=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		hf_model.config.feat_extract_norm == 'group' ,		)
																		_UpperCAmelCase									=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase									=			'unispeech_sat.' + 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]:
																														if "layer_norm_for_extract" in name and (".".join(name.split('.'	)[:-1]	) != key):
																																				# special case since naming is very similar
																																				continue
																														_UpperCAmelCase									=			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase									=			name.split(__lowerCAmelCase	)[0].split('.'	)[-2]
																																				_UpperCAmelCase									=			mapped_key.replace('*' ,		__lowerCAmelCase	)
																														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(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
																								continue
												if not is_used:
																		unused_weights.append(__lowerCAmelCase	)

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			full_name.split('conv_layers.'	)[-1]
						_UpperCAmelCase									=			name.split('.'	)
						_UpperCAmelCase									=			int(items[0]	)
						_UpperCAmelCase									=			int(items[1]	)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
						else:
												unused_weights.append(__lowerCAmelCase	)


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		__lowerCAmelCase=True	)->  int:





						"""simple docstring"""



						if config_path is not None:
												_UpperCAmelCase									=			UniSpeechSatConfig.from_pretrained(__lowerCAmelCase	)
						else:
												_UpperCAmelCase									=			UniSpeechSatConfig()

						_UpperCAmelCase									=			''

						if is_finetuned:
												_UpperCAmelCase									=			UniSpeechSatForCTC(__lowerCAmelCase	)
						else:
												_UpperCAmelCase									=			UniSpeechSatForPreTraining(__lowerCAmelCase	)

						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path] ,		arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
						_UpperCAmelCase									=			model[0].eval()

						recursively_load_weights(__lowerCAmelCase ,		__lowerCAmelCase	)

						hf_wavavec.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
		_a      	=  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      	=  parser.parse_args()
		convert_unispeech_sat_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)








import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


_a      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
						assert len(__lowerCAmelCase	) == len(__lowerCAmelCase	), F"""{len(__lowerCAmelCase	)} != {len(__lowerCAmelCase	)}"""
						dest_layers.load_state_dict(layers_to_copy.state_dict()	)


_a      	=  {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_a      	=  {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						try:
												_UpperCAmelCase									=			LAYERS_TO_COPY[n_teacher][n_student]
												return val
						except KeyError:
												if n_student != n_teacher:
																		warnings.warn(
																		    F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
																		    F""" {n_student}"""	)
												return list(range(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  List[int]:





						"""simple docstring"""



						if n_student > n_teacher:
												raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""	)
						elif n_teacher == n_student:
												return list(range(__lowerCAmelCase	)	)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = "student" ,		__lowerCAmelCase = None ,		__lowerCAmelCase = None ,		__lowerCAmelCase=False ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		**__lowerCAmelCase ,		)->  Tuple[PreTrainedModel, List[int], List[int]]:





						"""simple docstring"""



						_UpperCAmelCase									=			'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
						assert (e is not None) or (d is not None), _msg
						if isinstance(__lowerCAmelCase ,		__lowerCAmelCase	):
												AutoTokenizer.from_pretrained(__lowerCAmelCase	).save_pretrained(__lowerCAmelCase	)  # purely for convenience
												_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase	).eval()
						else:
												assert isinstance(__lowerCAmelCase ,		__lowerCAmelCase	), F"""teacher must be a model or string got type {type(__lowerCAmelCase	)}"""
						_UpperCAmelCase									=			teacher.config.to_diff_dict()

						try:
												_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.encoder_layers, teacher.config.decoder_layers
												if e is None:
																		_UpperCAmelCase									=			teacher_e
												if d is None:
																		_UpperCAmelCase									=			teacher_d
												init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}	)
						except AttributeError:  # T5
												if hasattr(teacher.config ,		'num_encoder_layers'	):
																		_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
												else:
																		_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.num_layers, teacher.config.num_decoder_layers
												if e is None:
																		_UpperCAmelCase									=			teacher_e
												if d is None:
																		_UpperCAmelCase									=			teacher_d
												if hasattr(teacher.config ,		'num_encoder_layers'	):
																		init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}	)
												else:
																		init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}	)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
						init_kwargs.update(__lowerCAmelCase	)

						# Copy weights
						_UpperCAmelCase									=			teacher.config_class(**__lowerCAmelCase	)
						_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase	)
						# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
						_UpperCAmelCase									=			student.load_state_dict(teacher.state_dict() ,		strict=__lowerCAmelCase	)
						assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

						if copy_first_teacher_layers:  # Our copying is done. We just log and save
												_UpperCAmelCase					, _UpperCAmelCase									=			list(range(__lowerCAmelCase	)	), list(range(__lowerCAmelCase	)	)
												logger.info(
												    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
												    F""" {save_path}"""	)
												student.save_pretrained(__lowerCAmelCase	)
												return student, e_layers_to_copy, d_layers_to_copy

						# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
						if e_layers_to_copy is None:
												_UpperCAmelCase									=			pick_layers_to_copy(__lowerCAmelCase ,		__lowerCAmelCase	)
						if d_layers_to_copy is None:
												_UpperCAmelCase									=			pick_layers_to_copy(__lowerCAmelCase ,		__lowerCAmelCase	)

						try:
												if hasattr(
												    __lowerCAmelCase ,		'prophetnet'	):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																		copy_layers(teacher.prophetnet.encoder.layers ,		student.prophetnet.encoder.layers ,		__lowerCAmelCase	)
																		copy_layers(teacher.prophetnet.decoder.layers ,		student.prophetnet.decoder.layers ,		__lowerCAmelCase	)
												else:
																		copy_layers(teacher.model.encoder.layers ,		student.model.encoder.layers ,		__lowerCAmelCase	)
																		copy_layers(teacher.model.decoder.layers ,		student.model.decoder.layers ,		__lowerCAmelCase	)
						except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
												copy_layers(teacher.encoder.block ,		student.encoder.block ,		__lowerCAmelCase	)
												copy_layers(teacher.decoder.block ,		student.decoder.block ,		__lowerCAmelCase	)
						logger.info(
						    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""	)
						_UpperCAmelCase									=			{
						    'teacher_type': teacher.config.model_type,
						    'copied_encoder_layers': e_layers_to_copy,
						    'copied_decoder_layers': d_layers_to_copy,
						}
						student.save_pretrained(__lowerCAmelCase	)
						# Save information about copying for easier reproducibility

						return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
		fire.Fire(create_student_by_copying_alternating_layers)
