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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_    :		Dict							=	logging.get_logger(__name__)

lowerCamelCase_    :		Union[str, Any]							=	{
    """post_extract_proj""": """feature_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.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(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					for attribute in key.split('.'						):
										__a		 =       getattr(__lowerCamelCase				,    __lowerCamelCase						)

					if weight_type is not None:
										__a		 =       getattr(__lowerCamelCase				,    __lowerCamelCase						).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(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       []
					__a		 =       fairseq_model.state_dict()

					__a		 =       hf_model.sew.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(
															    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    hf_model.config.feat_extract_norm == 'group'				,    )
															__a		 =       True
										else:
															for key, mapped_key in MAPPING.items():
																				__a		 =       'sew.' + 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(__lowerCamelCase						)[0].split('.'						)[-2]
																														__a		 =       mapped_key.replace('*'				,    __lowerCamelCase						)
																									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(__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						):
					__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(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       SEWConfig()
					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(__lowerCamelCase						)
					__a		 =       fs_config.encoder_layers
					__a		 =       fs_config.squeeze_factor

					# 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(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase=None				,    __lowerCamelCase=True						):

					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		 =       SEWConfig.from_pretrained(__lowerCamelCase						)
					else:
										__a		 =       convert_config(model[0]				,    __lowerCamelCase						)
					__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=__lowerCamelCase				,    return_attention_mask=__lowerCamelCase				,    )

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

															# 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(__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						)
															with open(__lowerCamelCase				,    'w'				,    encoding='utf-8'						) as vocab_handle:
																				json.dump(target_dict.indices				,    __lowerCamelCase						)
															__a		 =       WavaVecaCTCTokenizer(
															    __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				,    )
															__a		 =       WavaVecaProcessor(feature_extractor=__lowerCamelCase				,    tokenizer=__lowerCamelCase						)
															processor.save_pretrained(__lowerCamelCase						)

										__a		 =       SEWForCTC(__lowerCamelCase						)
					else:
										__a		 =       SEWModel(__lowerCamelCase						)
										feature_extractor.save_pretrained(__lowerCamelCase						)

					recursively_load_weights(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)

					hf_model.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_    :		Optional[Any]							=	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"""
	)
	lowerCamelCase_    :		Dict							=	parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)





from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




