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					(   a_				: Optional[int]						,			a_				: List[Any]						,			a_				: str						,			a_				: Optional[Any]						,			a_				: Optional[Any]						,			a_				: str):
			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						:str						  =     '''lm_head'''

						lowerCamelCase						:Union[str, Any]						  =     getattr(a_						,			a_)

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

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





def  _lowerCamelCase					(   a_				: Union[str, Any]						,			a_				: Dict						,			a_				: Any):
			lowerCamelCase						:Any						  =     []
			lowerCamelCase						:Tuple						  =     fairseq_model.state_dict()

			lowerCamelCase						:str						  =     hf_model.unispeech.feature_extractor

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

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





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





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Optional[int]						,			a_				: Any=None						,			a_				: Dict=None						,			a_				: List[Any]=True):
			if config_path is not None:
						lowerCamelCase						:Dict						  =     UniSpeechConfig.from_pretrained(a_)
			else:
						lowerCamelCase						:Tuple						  =     UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									lowerCamelCase						:Any						  =     Dictionary.load_from_json(a_)

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

									# fairseq has the <pad> and <s> switched
									lowerCamelCase						:Optional[int]						  =     42
									lowerCamelCase						:Any						  =     43
									with open(a_						,			'''w'''						,			encoding='''utf-8''') as vocab_handle:
												json.dump(a_						,			a_)
									lowerCamelCase						:Optional[Any]						  =     WavaVecaPhonemeCTCTokenizer(
									    a_						,			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=a_						,			)
									lowerCamelCase						:Optional[int]						  =     True if config.feat_extract_norm == '''layer''' else False
									lowerCamelCase						:int						  =     WavaVecaFeatureExtractor(
									    feature_size=1						,			sampling_rate=1_60_00						,			padding_value=0						,			do_normalize=a_						,			return_attention_mask=a_						,			)
									lowerCamelCase						:Dict						  =     WavaVecaProcessor(feature_extractor=a_						,			tokenizer=a_)
									processor.save_pretrained(a_)

						lowerCamelCase						:int						  =     UniSpeechForCTC(a_)
			else:
						lowerCamelCase						:Optional[Any]						  =     UniSpeechForPreTraining(a_)

			if is_finetuned:
						lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						: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						:str						  =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

			lowerCamelCase						:Union[str, Any]						  =     model[0].eval()

			recursively_load_weights(a_						,			a_						,			a_)

			hf_unispeech.save_pretrained(a_)


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 inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     inspect.getfile(accelerate.test_utils		)
										lowerCamelCase						:Dict						  =     os.path.sep.join(
										    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										lowerCamelCase						:Any						  =     test_metrics







							@require_cpu
							def 				snake_case  (				self     :    Dict		):
										debug_launcher(self.test_metrics.main	,					num_processes=1		)







							@require_cpu
							def 				snake_case  (				self     :    int		):
										debug_launcher(self.test_metrics.main		)







							@require_single_gpu
							def 				snake_case  (				self     :    Any		):
										self.test_metrics.main()







							@require_multi_gpu
							def 				snake_case  (				self     :    Optional[int]		):
										print(F"Found {torch.cuda.device_count()} devices."		)
										lowerCamelCase						:Optional[int]						  =     ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__snake_case	,					env=os.environ.copy()		)



