

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
a		=[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	for attribute in key.split('.'      ):
		if is_finetuned:
			if attribute in ["quantizer", "project_q", "project_hid"]:
				# those layers are only relevant for pretraining and should be dropped
				return

			if attribute == "ctc_proj":
				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
				__lowerCamelCase    :    Tuple 	=			'lm_head'

		__lowerCamelCase    :    Dict 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      )

	if weight_type is not None:
		__lowerCamelCase    :    int 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      ).shape
	else:
		__lowerCamelCase    :    Union[str, Any] 	=			hf_pointer.shape

	assert hf_shape == value.shape, (
	    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":
		__lowerCamelCase    :    Any 	=			value
	elif weight_type == "weight_g":
		__lowerCamelCase    :    Optional[Any] 	=			value
	elif weight_type == "weight_v":
		__lowerCamelCase    :    Dict 	=			value
	elif weight_type == "bias":
		__lowerCamelCase    :    List[Any] 	=			value
	else:
		__lowerCamelCase    :    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__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    str 	=			[]
	__lowerCamelCase    :    Any 	=			fairseq_model.state_dict()

	__lowerCamelCase    :    List[Any] 	=			hf_model.unispeech.feature_extractor

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

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    str 	=			full_name.split('conv_layers.'      )[-1]
	__lowerCamelCase    :    int 	=			name.split('.'      )
	__lowerCamelCase    :    Union[str, Any] 	=			int(items[0]      )
	__lowerCamelCase    :    Tuple 	=			int(items[1]      )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F"{full_name} has size {value.shape}, but"
			    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
			)
			__lowerCamelCase    :    Tuple 	=			value
			logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F"{full_name} has size {value.shape}, but"
			    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
			)
			__lowerCamelCase    :    List[str] 	=			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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
			    " found."
			)
			__lowerCamelCase    :    int 	=			value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"{full_name} has size {value.shape}, but"
			    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
			)
			__lowerCamelCase    :    List[str] 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=True      )  ->					str:
	if config_path is not None:
		__lowerCamelCase    :    int 	=			UniSpeechConfig.from_pretrained(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    Tuple 	=			UniSpeechConfig()

	if is_finetuned:
		if dict_path:
			__lowerCamelCase    :    Tuple 	=			Dictionary.load_from_json(lowerCamelCase__      )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__lowerCamelCase    :    str 	=			target_dict.pad_index
			__lowerCamelCase    :    int 	=			target_dict.bos_index
			__lowerCamelCase    :    int 	=			target_dict.eos_index
			__lowerCamelCase    :    Dict 	=			len(target_dict.symbols      )
			__lowerCamelCase    :    Optional[Any] 	=			os.path.join(lowerCamelCase__     ,			'vocab.json'      )
			if not os.path.isdir(lowerCamelCase__      ):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCamelCase__      )      )
				return
			os.makedirs(lowerCamelCase__     ,			exist_ok=lowerCamelCase__      )
			__lowerCamelCase    :    Optional[int] 	=			target_dict.indices

			# fairseq has the <pad> and <s> switched
			__lowerCamelCase    :    Dict 	=			4_2
			__lowerCamelCase    :    str 	=			4_3
			with open(lowerCamelCase__     ,			'w'     ,			encoding='utf-8'      ) as vocab_handle:
				json.dump(lowerCamelCase__     ,			lowerCamelCase__      )
			__lowerCamelCase    :    Tuple 	=			WavaVecaPhonemeCTCTokenizer(
			    lowerCamelCase__     ,			unk_token=target_dict.unk_word     ,			pad_token=target_dict.pad_word     ,			bos_token=target_dict.bos_word     ,			eos_token=target_dict.eos_word     ,			word_delimiter_token='|'     ,			do_lower_case=lowerCamelCase__     ,			)
			__lowerCamelCase    :    Tuple 	=			True if config.feat_extract_norm == 'layer' else False
			__lowerCamelCase    :    str 	=			WavaVecaFeatureExtractor(
			    feature_size=1     ,			sampling_rate=1_6_0_0_0     ,			padding_value=0     ,			do_normalize=lowerCamelCase__     ,			return_attention_mask=lowerCamelCase__     ,			)
			__lowerCamelCase    :    List[str] 	=			WavaVecaProcessor(feature_extractor=lowerCamelCase__     ,			tokenizer=lowerCamelCase__      )
			processor.save_pretrained(lowerCamelCase__      )

		__lowerCamelCase    :    Dict 	=			UniSpeechForCTC(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    Tuple 	=			UniSpeechForPreTraining(lowerCamelCase__      )

	if is_finetuned:
		__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,			arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      ), 'w2v_path': checkpoint_path}      )
	else:
		__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

	__lowerCamelCase    :    List[Any] 	=			model[0].eval()

	recursively_load_weights(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	hf_unispeech.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_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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    List[str] 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[int]:
	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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = "student"     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			lowerCamelCase__=False     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			**lowerCamelCase__     ,			)  ->					Tuple[PreTrainedModel, List[int], List[int]]:
	__lowerCamelCase    :    int 	=			'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
		__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__      ).eval()
	else:
		assert isinstance(lowerCamelCase__     ,			lowerCamelCase__      ), F"teacher must be a model or string got type {type(lowerCamelCase__      )}"
	__lowerCamelCase    :    str 	=			teacher.config.to_diff_dict()

	try:
		__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			__lowerCamelCase    :    Optional[int] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Optional[Any] 	=			teacher_d
		init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}      )
	except AttributeError:  # T5
		if hasattr(teacher.config     ,			'num_encoder_layers'      ):
			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			__lowerCamelCase    :    Union[str, Any] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Any 	=			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
	__lowerCamelCase    :    str 	=			teacher.config_class(**lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__      )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	__lowerCamelCase    :    Tuple 	=			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
		__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			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:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )
	if d_layers_to_copy is None:
		__lowerCamelCase    :    List[int] 	=			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}"      )
	__lowerCamelCase    :    Dict 	=			{
	    '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)




