
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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				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_						=			"lm_head"

								lowerCamelCase_						=			getattr(lowerCamelCase__	, lowerCamelCase__    )

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

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[]
				lowerCamelCase_						=			fairseq_model.state_dict()

				lowerCamelCase_						=			hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCamelCase_						=			False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, hf_model.config.feat_extract_norm == "group"	, )
												lowerCamelCase_						=			True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCamelCase_						=			"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_						=			True
																				if "*" in mapped_key:
																								lowerCamelCase_						=			name.split(lowerCamelCase__    )[0].split("."    )[-2]
																								lowerCamelCase_						=			mapped_key.replace("*"	, lowerCamelCase__    )
																				if "weight_g" in name:
																								lowerCamelCase_						=			"weight_g"
																				elif "weight_v" in name:
																								lowerCamelCase_						=			"weight_v"
																				elif "bias" in name:
																								lowerCamelCase_						=			"bias"
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCamelCase_						=			"weight"
																				else:
																								lowerCamelCase_						=			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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			full_name.split("conv_layers."    )[-1]
				lowerCamelCase_						=			name.split("."    )
				lowerCamelCase_						=			int(items[0]    )
				lowerCamelCase_						=			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_						=			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_						=			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_						=			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_						=			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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=True    ):
				if config_path is not None:
								lowerCamelCase_						=			UniSpeechConfig.from_pretrained(lowerCamelCase__    )
				else:
								lowerCamelCase_						=			UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowerCamelCase_						=			Dictionary.load_from_json(lowerCamelCase__    )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCamelCase_						=			target_dict.pad_index
												lowerCamelCase_						=			target_dict.bos_index
												lowerCamelCase_						=			target_dict.eos_index
												lowerCamelCase_						=			len(target_dict.symbols    )
												lowerCamelCase_						=			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_						=			target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCamelCase_						=			4_2
												lowerCamelCase_						=			4_3
												with open(lowerCamelCase__	, "w"	, encoding="utf-8"    ) as vocab_handle:
																json.dump(lowerCamelCase__	, lowerCamelCase__    )
												lowerCamelCase_						=			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_						=			True if config.feat_extract_norm == "layer" else False
												lowerCamelCase_						=			WavaVecaFeatureExtractor(
												    feature_size=1	, sampling_rate=1_6_0_0_0	, padding_value=0	, do_normalize=lowerCamelCase__	, return_attention_mask=lowerCamelCase__	, )
												lowerCamelCase_						=			WavaVecaProcessor(feature_extractor=lowerCamelCase__	, tokenizer=lowerCamelCase__    )
												processor.save_pretrained(lowerCamelCase__    )

								lowerCamelCase_						=			UniSpeechForCTC(lowerCamelCase__    )
				else:
								lowerCamelCase_						=			UniSpeechForPreTraining(lowerCamelCase__    )

				if is_finetuned:
								lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			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_						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

				lowerCamelCase_						=			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 importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				):
				# Get the sagemaker specific mp parameters from smp_options variable.
				lowerCamelCase_						=			os.getenv("SM_HP_MP_PARAMETERS"	, "{}"    )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				lowerCamelCase_						=			os.getenv("SM_FRAMEWORK_PARAMS"	, "{}"    )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if not mpi_options.get("sagemaker_mpi_enabled"	, lowerCamelCase__    ):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec("smdistributed"    ) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()





@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = field(
	    default=''			,       metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,       )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					super().__post_init__()
					warnings.warn(
					    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
					    "`TrainingArguments` instead."						,  lowercase						,  )



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       "torch.device":
					logger.info("PyTorch: setting up devices" )
					if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
									logger.warning(
									    "torch.distributed process group is initialized, but local_rank == -1. "
									    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
					if self.no_cuda:
									lowerCamelCase_						=			torch.device("cpu" )
									lowerCamelCase_						=			0
					elif is_sagemaker_model_parallel_available():
									lowerCamelCase_						=			smp.local_rank()
									lowerCamelCase_						=			torch.device("cuda"						,  lowercase )
									lowerCamelCase_						=			1
					elif is_sagemaker_dp_enabled():
									import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

									torch.distributed.init_process_group(backend="smddp"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1
					elif self.local_rank == -1:
									# if n_gpu is > 1 we'll use nn.DataParallel.
									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
									# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
									# trigger an error that a device index is missing. Index 0 takes into account the
									# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
									# will use the first GPU in that env, i.e. GPU#1
									lowerCamelCase_						=			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
									# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
									# the default value.
									lowerCamelCase_						=			torch.cuda.device_count()
					else:
									# Here, we'll use torch.distributed.
									# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
									if not torch.distributed.is_initialized():
													torch.distributed.init_process_group(backend="nccl"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1

					if device.type == "cuda":
									torch.cuda.set_device(lowercase )

					return device



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return not is_sagemaker_model_parallel_available()



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return False


