
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowercase  :					List[Any]													=       {
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowercase  :					Dict													=       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			Optional[Any])					->    int:






							'''simple docstring'''





							for attribute in key.split("."):
														__UpperCamelCase	:   Dict			   =					getattr(_lowerCamelCase						,  _lowerCamelCase)

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

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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[]
							__UpperCamelCase	:   Tuple			   =					fairseq_model.state_dict()

							__UpperCamelCase	:   Union[str, Any]			   =					hf_model.feature_extractor
							__UpperCamelCase	:   Optional[int]			   =					hf_model.adapter

							for name, value in fairseq_dict.items():
														__UpperCamelCase	:   List[Any]			   =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  hf_model.config.feat_extract_norm == "group"						,  )
																					__UpperCamelCase	:   Optional[Any]			   =					True
														elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]):
																					load_adapter(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
																					__UpperCamelCase	:   str			   =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																																			__UpperCamelCase	:   List[Any]			   =					True
																																			if "*" in mapped_key:
																																										__UpperCamelCase	:   List[Any]			   =					name.split(_lowerCamelCase)[0].split(".")[-2]
																																										__UpperCamelCase	:   Any			   =					mapped_key.replace("*"						,  _lowerCamelCase)
																																			if "weight_g" in name:
																																										__UpperCamelCase	:   Union[str, Any]			   =					"weight_g"
																																			elif "weight_v" in name:
																																										__UpperCamelCase	:   Optional[Any]			   =					"weight_v"
																																			elif "bias" in name:
																																										__UpperCamelCase	:   Tuple			   =					"bias"
																																			elif "weight" in name:
																																										__UpperCamelCase	:   Optional[int]			   =					"weight"
																																			else:
																																										__UpperCamelCase	:   Union[str, Any]			   =					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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			int						,  _lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			Dict)					->    List[Any]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Optional[int])					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					full_name.split("adaptor.")[-1]
							__UpperCamelCase	:   Tuple			   =					name.split(".")

							if items[1].isdigit():
														__UpperCamelCase	:   str			   =					int(items[1])
							else:
														__UpperCamelCase	:   Tuple			   =					None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																												__UpperCamelCase	:   int			   =					value
																												logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.')
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																												__UpperCamelCase	:   Union[str, Any]			   =					value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																												__UpperCamelCase	:   Any			   =					value
																												logger.info(F'Adapter proj layer bias was initialized from {full_name}.')
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																												__UpperCamelCase	:   Optional[int]			   =					value
																												logger.info(F'Adapter proj layer weight was initialized from {full_name}.')
							elif isinstance(_lowerCamelCase						,  _lowerCamelCase):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																					__UpperCamelCase	:   Optional[Any]			   =					value
																					logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																					__UpperCamelCase	:   Any			   =					value
																					logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
							else:
														unused_weights.append(_lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Any:






							'''simple docstring'''





							__UpperCamelCase   ,						__UpperCamelCase	:   Any			   =					emb.weight.shape
							__UpperCamelCase	:   Union[str, Any]			   =					nn.Linear(_lowerCamelCase						,  _lowerCamelCase						,  bias=_lowerCamelCase)
							__UpperCamelCase	:   int			   =					emb.weight.data
							return lin_layer




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			int						,  _lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			str						,  _lowerCamelCase :			int						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			Tuple						,  )					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					WavaVecaConfig.from_pretrained(
							    _lowerCamelCase						,  add_adapter=_lowerCamelCase						,  adapter_stride=_lowerCamelCase						,  adapter_kernel_size=_lowerCamelCase						,  use_auth_token=_lowerCamelCase						,  output_hidden_size=_lowerCamelCase						,  )
							__UpperCamelCase	:   Optional[Any]			   =					MBartConfig.from_pretrained(_lowerCamelCase)

							# load model
							__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]						,  arg_overrides={
							        "config_yaml": config_yaml_path,
							        "data": "/".join(dict_path.split("/")[:-1]),
							        "w2v_path": checkpoint_path,
							        "load_pretrained_decoder_from": None,
							    }						,  )
							__UpperCamelCase	:   List[str]			   =					model[0].eval()

							# load feature extractor
							__UpperCamelCase	:   Union[str, Any]			   =					WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase						,  use_auth_token=_lowerCamelCase)

							# set weights for wav2vec2 encoder
							__UpperCamelCase	:   Optional[Any]			   =					WavaVecaModel(_lowerCamelCase)

							recursively_load_weights_wavaveca(model.encoder						,  _lowerCamelCase)

							# load decoder weights
							__UpperCamelCase	:   int			   =					MBartForCausalLM(_lowerCamelCase)
							__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,  strict=_lowerCamelCase)
							logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}')
							logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}')

							__UpperCamelCase	:   Optional[int]			   =					SpeechEncoderDecoderModel(encoder=_lowerCamelCase						,  decoder=_lowerCamelCase)
							__UpperCamelCase	:   Dict			   =					False

							__UpperCamelCase	:   Any			   =					MBartaaTokenizer(_lowerCamelCase)
							tokenizer.save_pretrained(_lowerCamelCase)

							__UpperCamelCase	:   Optional[int]			   =					hf_wavavec.config.to_dict()
							__UpperCamelCase	:   str			   =					tokenizer.pad_token_id
							__UpperCamelCase	:   Dict			   =					tokenizer.bos_token_id
							__UpperCamelCase	:   int			   =					tokenizer.eos_token_id
							__UpperCamelCase	:   int			   =					"mbart50"
							__UpperCamelCase	:   int			   =					"wav2vec2"

							__UpperCamelCase	:   Tuple			   =					tokenizer.eos_token_id
							__UpperCamelCase	:   Any			   =					250_004
							__UpperCamelCase	:   Tuple			   =					tokenizer.eos_token_id

							__UpperCamelCase	:   Tuple			   =					SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase)

							hf_wavavec.save_pretrained(_lowerCamelCase)
							feature_extractor.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		lowercase  :					List[str]													=       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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
		parser.add_argument(
		    '--encoder_config_path',
		    default='facebook/wav2vec2-xls-r-1b',
		    type=str,
		    help='Path to hf encoder wav2vec2 checkpoint config',
		)
		parser.add_argument(
		    '--decoder_config_path',
		    default='facebook/mbart-large-50-one-to-many-mmt',
		    type=str,
		    help='Path to hf decoder checkpoint config',
		)
		parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
		parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
		parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
		parser.add_argument('--encoder_output_dim', default=1024, type=int, help='encoder output dim')
		parser.add_argument('--start_token_id', default=250004, type=int, help='`decoder_start_token_id` of model config')

		lowercase  :					Union[str, Any]													=       parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    args.config_yaml_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    add_adapter=args.add_adapter,
		    adapter_kernel_size=args.adapter_kernel_size,
		    adapter_stride=args.adapter_stride,
		    decoder_start_token_id=args.start_token_id,
		    encoder_output_dim=args.encoder_output_dim,
		)

# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# XXX: run_asr is premature and doesn't save any results
								# so all we check for now is that the process didn't fail
								pass







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

								if fpaa:
															args.extend(["--fp16"]						)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__UpperCamelCase	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

								__UpperCamelCase	:   Optional[int]			   =					launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(a     ,   env=self.get_env()						)

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
								# - it won't be able to handle that
								# 2. for now testing with just 2 gpus max (since some quality tests may give different
								# results with mode gpus because we use very little data)
								__UpperCamelCase	:   List[Any]			   =					min(2     ,   get_gpu_count()						) if distributed else 1
								return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()