
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A  :			Any     				=				logging.get_logger(__name__)

A  :			int     				=				{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__						) ->						Tuple:
				for attribute in key.split('''.'''						):
								__a    							=					getattr(__snake_case		,					__snake_case						)

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

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


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						Dict:
				__a    							=					[]
				__a    							=					fairseq_model.state_dict()

				__a    							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__a    							=					False
								if "conv_layers" in name:
												load_conv_layer(
												    __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					hf_model.config.feat_extract_norm == '''group'''		,					)
												__a    							=					True
								else:
												for key, mapped_key in MAPPING.items():
																__a    							=					'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
																				__a    							=					True
																				if "*" in mapped_key:
																								__a    							=					name.split(__snake_case						)[0].split('''.'''						)[-2]
																								if not layer_index.isnumeric():
																												continue
																								__a    							=					mapped_key.replace('''*'''		,					__snake_case						)
																				if "weight_g" in name:
																								__a    							=					'''weight_g'''
																				elif "weight_v" in name:
																								__a    							=					'''weight_v'''
																				elif "weight" in name:
																								__a    							=					'''weight'''
																				elif "bias" in name:
																								__a    							=					'''bias'''
																				else:
																								__a    							=					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 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__						) ->						int:
				__a    							=					full_name.split('''conv_layers.'''						)[-1]
				__a    							=					name.split('''.'''						)
				__a    							=					int(items[0]						)
				__a    							=					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."""
												)
												__a    							=					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."""
												)
												__a    							=					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."
												)
												__a    							=					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."""
												)
												__a    							=					value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
				else:
								unused_weights.append(__snake_case						)


def 					__lowerCAmelCase    (    a__		,					a__						) ->						List[Any]:
				__a    							=					SEWDConfig()
				if is_finetuned:
								__a    							=					model.wav_encoder.wav_model.cfg
				else:
								__a    							=					model.cfg

				__a    							=					fs_config.conv_bias
				__a    							=					eval(fs_config.conv_feature_layers						)
				__a    							=					[x[0] for x in conv_layers]
				__a    							=					[x[1] for x in conv_layers]
				__a    							=					[x[2] for x in conv_layers]
				__a    							=					'''gelu'''
				__a    							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				__a    							=					0.0
				__a    							=					fs_config.activation_fn.name
				__a    							=					fs_config.encoder_embed_dim
				__a    							=					0.02
				__a    							=					fs_config.encoder_ffn_embed_dim
				__a    							=					1e-5
				__a    							=					fs_config.encoder_layerdrop
				__a    							=					fs_config.encoder_attention_heads
				__a    							=					fs_config.conv_pos_groups
				__a    							=					fs_config.conv_pos
				__a    							=					len(__snake_case						)
				__a    							=					fs_config.encoder_layers
				__a    							=					fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				__a    							=					fs_config.max_position_embeddings
				__a    							=					fs_config.position_buckets
				__a    							=					fs_config.share_att_key
				__a    							=					fs_config.relative_attention
				__a    							=					fs_config.position_biased_input
				__a    							=					tuple(fs_config.pos_att_type.split('''|'''						)						)
				__a    							=					fs_config.norm_rel_ebd

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								__a    							=					model.cfg
								__a    							=					fs_config.final_dropout
								__a    							=					fs_config.layerdrop
				__a    							=					fs_config.activation_dropout
				__a    							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				__a    							=					fs_config.attention_dropout
				__a    							=					fs_config.dropout_input
				__a    							=					fs_config.dropout
				__a    							=					fs_config.mask_channel_length
				__a    							=					fs_config.mask_channel_prob
				__a    							=					fs_config.mask_length
				__a    							=					fs_config.mask_prob

				__a    							=					'''Wav2Vec2FeatureExtractor'''
				__a    							=					'''Wav2Vec2CTCTokenizer'''

				return config


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__		,					a__=None		,					a__=None		,					a__=True						) ->						List[Any]:

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

				if config_path is not None:
								__a    							=					SEWDConfig.from_pretrained(__snake_case						)
				else:
								__a    							=					convert_config(model[0]		,					__snake_case						)
				__a    							=					model[0].eval()

				__a    							=					True if config.feat_extract_norm == '''layer''' else False
				__a    							=					WavaVecaFeatureExtractor(
				    feature_size=1		,					sampling_rate=1_6000		,					padding_value=0		,					do_normalize=__snake_case		,					return_attention_mask=__snake_case		,					)

				if is_finetuned:
								if dict_path:
												__a    							=					Dictionary.load(__snake_case						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__a    							=					target_dict.pad_index
												__a    							=					target_dict.bos_index
												__a    							=					target_dict.pad_index
												__a    							=					target_dict.bos_index
												__a    							=					target_dict.eos_index
												__a    							=					len(target_dict.symbols						)
												__a    							=					os.path.join(__snake_case		,					'''vocab.json'''						)
												if not os.path.isdir(__snake_case						):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case						)						)
																return
												os.makedirs(__snake_case		,					exist_ok=__snake_case						)
												with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''						) as vocab_handle:
																json.dump(target_dict.indices		,					__snake_case						)
												__a    							=					WavaVecaCTCTokenizer(
												    __snake_case		,					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=__snake_case		,					)
												__a    							=					WavaVecaProcessor(feature_extractor=__snake_case		,					tokenizer=__snake_case						)
												processor.save_pretrained(__snake_case						)

								__a    							=					SEWDForCTC(__snake_case						)
				else:
								__a    							=					SEWDModel(__snake_case						)
								feature_extractor.save_pretrained(__snake_case						)

				recursively_load_weights(__snake_case		,					__snake_case		,					__snake_case						)

				hf_model.save_pretrained(__snake_case						)


if __name__ == "__main__":
					A  :			Dict     				=				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(
					    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					A  :			Any     				=				parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
					import torch_xla.core.xla_model as xm  # noqa: F401

					A  :			List[str]     				=				True
except ImportError:
					A  :			List[Any]     				=				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
A  :			str     				=				torch.distributed.is_available()


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
				__a    							=					importlib.util.find_spec(a__						) is not None
				if package_exists:
								try:
												__a    							=					importlib.metadata.metadata(a__						)
												return True
								except importlib.metadata.PackageNotFoundError:
												return False


def 					__lowerCAmelCase    (    ) ->						bool:
				return _torch_distributed_available


def 					__lowerCAmelCase    (    ) ->						Tuple:
				try:
								pass
				except ImportError:
								print(
								    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
								    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
								    ''' Bindings for PyTorch*.'''						)
				return (
				    importlib.util.find_spec('''torch_ccl'''						) is not None
				    or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''						) is not None
				)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return importlib.metadata.version('''oneccl_bind_pt'''						)


def 					__lowerCAmelCase    (    ) ->						Dict:
				return _is_package_available('''transformer_engine'''						)


@lru_cache
def 					__lowerCAmelCase    (    a__=True						) ->						Union[str, Any]:
				# Due to bugs on the amp series GPUs, we disable torch-xla on them
				if torch.cuda.is_available():
								return False
				if _tpu_available and check_device:
								try:
												# Will raise a RuntimeError if no XLA configuration is found
												__a    							=					xm.xla_device()
												return True
								except RuntimeError:
												return False
				return _tpu_available


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''deepspeed'''						)


def 					__lowerCAmelCase    (    a__=False						) ->						Optional[Any]:
				if is_tpu_available():
								return not ignore_tpu
				if torch.cuda.is_available():
								return torch.cuda.is_bfaa_supported()
				if is_npu_available():
								return False
				return True


def 					__lowerCAmelCase    (    ) ->						List[str]:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.39.0'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.37.2'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''bitsandbytes'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''		,					'''False'''						)						) == 1:
								__a    							=					importlib.util.find_spec('''megatron'''						) is not None
								if package_exists:
												try:
																__a    							=					parse(importlib.metadata.version('''megatron-lm'''						)						)
																return compare_versions(a__		,					'''>='''		,					'''2.2.0'''						)
												except Exception as e:
																warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""						)
																return False


def 					__lowerCAmelCase    (    ) ->						List[str]:
				return _is_package_available('''safetensors'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return _is_package_available('''transformers'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''datasets'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''aim'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''wandb'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''comet_ml'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				return _is_package_available('''boto3'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				if _is_package_available('''rich'''						):
								if "ACCELERATE_DISABLE_RICH" in os.environ:
												warnings.warn(
												    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''						)
												return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''		,					a__						)
								return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''		,					a__						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''sagemaker'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''tqdm'''						)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return _is_package_available('''mlflow'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return is_torch_version('''>='''		,					'''1.12'''						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 					__lowerCAmelCase    (    ) ->						str:
				def get_major_and_minor_from_version(a__						):
								return str(version.parse(a__						).major						) + "." + str(version.parse(a__						).minor						)

				__a    							=					importlib.metadata.version('''torch'''						)
				if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
								return False
				__a    							=					'''N/A'''
				try:
								__a    							=					importlib.metadata.version('''intel_extension_for_pytorch'''						)
				except importlib.metadata.PackageNotFoundError:
								return False
				__a    							=					get_major_and_minor_from_version(_torch_version						)
				__a    							=					get_major_and_minor_from_version(_ipex_version						)
				if torch_major_and_minor != ipex_major_and_minor:
								warnings.warn(
								    F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
								    F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
								return False
				return True


@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						List[Any]:
				if importlib.util.find_spec('''torch'''						) is None or importlib.util.find_spec('''torch_npu'''						) is None:
								return False

				import torch
				import torch_npu  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no NPU is found
												__a    							=					torch.npu.device_count()
												return torch.npu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''npu'''						) and torch.npu.is_available()






@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						Union[str, Any]:
				if not parse_flag_from_env('''ACCELERATE_USE_XPU'''		,					default=a__						):
								return False
				if is_ipex_available():
								import torch

								if is_torch_version('''<='''		,					'''1.12'''						):
												return False
				else:
								return False

				import intel_extension_for_pytorch  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no XPU  is found
												__a    							=					torch.xpu.device_count()
												return torch.xpu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''xpu'''						) and torch.xpu.is_available()