






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


__lowerCamelCase      :    List[str]   		=   logging.get_logger(__name__)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							nn.ModuleList  ,						snake_case_      :							nn.ModuleList  ,						snake_case_      :							List[int]     ):
	snake_case__				:   str     =						nn.ModuleList([src_layers[i] for i in layers_to_copy]     )
	assert len(snake_case_     ) == len(snake_case_     ), F'''{len(snake_case_     )} != {len(snake_case_     )}'''
	dest_layers.load_state_dict(layers_to_copy.state_dict()     )


__lowerCamelCase      :    Dict   		=   {
    # 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))},
}
__lowerCamelCase      :    Dict   		=   {
    # 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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Optional[Any]     ):
	try:
		snake_case__				:   Optional[Any]     =						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(snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							str     ):
	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(snake_case_     )     )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, PreTrainedModel]  ,						snake_case_      :							Union[str, Path] = "student"  ,						snake_case_      :							Union[int, None] = None  ,						snake_case_      :							Union[int, None] = None  ,						snake_case_      :							Optional[Any]=False  ,						snake_case_      :							Union[str, Any]=None  ,						snake_case_      :							Optional[Any]=None  ,						**snake_case_      :							Tuple  ,						):
	snake_case__				:   str     =						"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(snake_case_  ,						snake_case_     ):
		AutoTokenizer.from_pretrained(snake_case_     ).save_pretrained(snake_case_     )  # purely for convenience
		snake_case__				:   str     =						AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).eval()
	else:
		assert isinstance(snake_case_  ,						snake_case_     ), F'''teacher must be a model or string got type {type(snake_case_     )}'''
	snake_case__				:   Dict     =						teacher.config.to_diff_dict()

	try:
		snake_case__,		snake_case__				:   Union[str, Any]     =						teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			snake_case__				:   str     =						teacher_e
		if d is None:
			snake_case__				:   Optional[Any]     =						teacher_d
		init_kwargs.update({"encoder_layers": e, "decoder_layers": d}     )
	except AttributeError:  # T5
		if hasattr(teacher.config  ,						"num_encoder_layers"     ):
			snake_case__,		snake_case__				:   Tuple     =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			snake_case__,		snake_case__				:   List[Any]     =						teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			snake_case__				:   Any     =						teacher_e
		if d is None:
			snake_case__				:   str     =						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(snake_case_     )

	# Copy weights
	snake_case__				:   Any     =						teacher.config_class(**snake_case_     )
	snake_case__				:   Optional[int]     =						AutoModelForSeqaSeqLM.from_config(snake_case_     )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	snake_case__				:   Tuple     =						student.load_state_dict(teacher.state_dict()  ,						strict=snake_case_     )
	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
		snake_case__,		snake_case__				:   List[Any]     =						list(range(snake_case_     )     ), list(range(snake_case_     )     )
		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(snake_case_     )
		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:
		snake_case__				:   List[int]     =						pick_layers_to_copy(snake_case_  ,						snake_case_     )
	if d_layers_to_copy is None:
		snake_case__				:   List[int]     =						pick_layers_to_copy(snake_case_  ,						snake_case_     )

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











import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowerCamelCase      :    int   		=   logging.get_logger(__name__)

__lowerCamelCase      :    int   		=   {
    """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""",
}
__lowerCamelCase      :    Tuple   		=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   int     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Optional[Any]     =						getattr(snake_case_  ,						snake_case_     ).shape
	else:
		snake_case__				:   List[str]     =						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":
		snake_case__				:   str     =						value
	elif weight_type == "weight_g":
		snake_case__				:   Union[str, Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "bias":
		snake_case__				:   str     =						value
	else:
		snake_case__				:   Union[str, Any]     =						value

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   str     =						[]
	snake_case__				:   Optional[int]     =						fairseq_model.state_dict()

	snake_case__				:   int     =						hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case__				:   Dict     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   str     =						True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case__				:   Optional[int]     =						"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
					snake_case__				:   int     =						True
					if "*" in mapped_key:
						snake_case__				:   Any     =						name.split(snake_case_     )[0].split("."     )[-2]
						snake_case__				:   Any     =						mapped_key.replace("*"  ,						snake_case_     )
					if "weight_g" in name:
						snake_case__				:   List[Any]     =						"weight_g"
					elif "weight_v" in name:
						snake_case__				:   Optional[Any]     =						"weight_v"
					elif "bias" in name:
						snake_case__				:   Optional[Any]     =						"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case__				:   Optional[Any]     =						"weight"
					else:
						snake_case__				:   Optional[Any]     =						None
					set_recursively(snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_     )
				continue
		if not is_used:
			unused_weights.append(snake_case_     )

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   Tuple     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Union[str, Any]     =						name.split("."     )
	snake_case__				:   str     =						int(items[0]     )
	snake_case__				:   str     =						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.'''     )
			snake_case__				:   Any     =						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.'''     )
			snake_case__				:   Any     =						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.'''     )
			snake_case__				:   Optional[Any]     =						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.'''     )
			snake_case__				:   int     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
	else:
		unused_weights.append(snake_case_     )







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Any=True     ):
	if config_path is not None:
		snake_case__				:   Tuple     =						UniSpeechSatConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   Tuple     =						UniSpeechSatConfig()

	snake_case__				:   str     =						""

	if is_finetuned:
		snake_case__				:   Tuple     =						UniSpeechSatForCTC(snake_case_     )
	else:
		snake_case__				:   Any     =						UniSpeechSatForPreTraining(snake_case_     )

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

	recursively_load_weights(snake_case_  ,						snake_case_     )

	hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    int   		=   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      :    List[Any]   		=   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
	)



