
'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase					:    Any           = {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    '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',
}
lowerCAmelCase					:    Union[str, Any]           = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def 		A_(     A						:		Union[str, Any]		,     A						:		List[str]		,     A						:		Dict		,     A						:		Tuple		,     A						:		Any):
							for attribute in key.split('.'):
														UpperCamelCase   			=     getattr(A		,     A)

							if weight_type is not None:
														UpperCamelCase   			=     getattr(A		,     A).shape
							else:
														UpperCamelCase   			=     hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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   			=     value
							elif weight_type == "weight_g":
														UpperCamelCase   			=     value
							elif weight_type == "weight_v":
														UpperCamelCase   			=     value
							elif weight_type == "bias":
														UpperCamelCase   			=     value
							elif weight_type == "running_mean":
														UpperCamelCase   			=     value
							elif weight_type == "running_var":
														UpperCamelCase   			=     value
							elif weight_type == "num_batches_tracked":
														UpperCamelCase   			=     value
							elif weight_type == "inv_freq":
														UpperCamelCase   			=     value
							else:
														UpperCamelCase   			=     value

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







def 		A_(     A						:		int		,     A						:		Union[str, Any]		,     A						:		str):
							UpperCamelCase   			=     []
							UpperCamelCase   			=     fairseq_model.state_dict()

							UpperCamelCase   			=     hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														UpperCamelCase   			=     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A		,     A		,     A		,     A		,     hf_model.config.feat_extract_norm == 'group'		,     )
																					UpperCamelCase   			=     True
														else:
																					for key, mapped_key in MAPPING.items():
																												UpperCamelCase   			=     'wav2vec2_conformer.' + 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]:
																																			UpperCamelCase   			=     True
																																			if "*" in mapped_key:
																																										UpperCamelCase   			=     name.split(A)[0].split('.')[-2]
																																										UpperCamelCase   			=     mapped_key.replace('*'		,     A)
																																			if "pos_bias_u" in name:
																																										UpperCamelCase   			=     None
																																			elif "pos_bias_v" in name:
																																										UpperCamelCase   			=     None
																																			elif "weight_g" in name:
																																										UpperCamelCase   			=     'weight_g'
																																			elif "weight_v" in name:
																																										UpperCamelCase   			=     'weight_v'
																																			elif "bias" in name:
																																										UpperCamelCase   			=     'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										UpperCamelCase   			=     'weight'
																																			elif "running_mean" in name:
																																										UpperCamelCase   			=     'running_mean'
																																			elif "inv_freq" in name:
																																										UpperCamelCase   			=     'inv_freq'
																																			elif "running_var" in name:
																																										UpperCamelCase   			=     'running_var'
																																			elif "num_batches_tracked" in name:
																																										UpperCamelCase   			=     'num_batches_tracked'
																																			else:
																																										UpperCamelCase   			=     None
																																			set_recursively(A		,     A		,     A		,     A		,     A)
																												continue
														if not is_used:
																					unused_weights.append(A)

							logger.warning(f'''Unused weights: {unused_weights}''')







def 		A_(     A						:		str		,     A						:		int		,     A						:		int		,     A						:		List[str]		,     A						:		Tuple):
							UpperCamelCase   			=     full_name.split('conv_layers.')[-1]
							UpperCamelCase   			=     name.split('.')
							UpperCamelCase   			=     int(items[0])
							UpperCamelCase   			=     int(items[1])

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''')
																					UpperCamelCase   			=     value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''')
																					UpperCamelCase   			=     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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''')
																					UpperCamelCase   			=     value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''')
																					UpperCamelCase   			=     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 		A_(     A						:		Dict		,     A						:		List[str]		,     A						:		Any=None		,     A						:		str=None		,     A						:		Optional[int]=True):
							if config_path is not None:
														UpperCamelCase   			=     WavaVecaConformerConfig.from_pretrained(A		,     hidden_act='swish')
							else:
														UpperCamelCase   			=     WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														UpperCamelCase   			=     'rotary'

							if is_finetuned:
														if dict_path:
																					UpperCamelCase   			=     Dictionary.load(A)

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

																					# fairseq has the <pad> and <s> switched
																					UpperCamelCase   			=     0
																					UpperCamelCase   			=     1
																					with open(A		,     'w'		,     encoding='utf-8') as vocab_handle:
																												json.dump(A		,     A)
																					UpperCamelCase   			=     WavaVecaCTCTokenizer(
																					    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		,     )
																					UpperCamelCase   			=     True if config.feat_extract_norm == 'layer' else False
																					UpperCamelCase   			=     WavaVecaFeatureExtractor(
																					    feature_size=1		,     sampling_rate=1_6000		,     padding_value=0		,     do_normalize=A		,     return_attention_mask=A		,     )
																					UpperCamelCase   			=     WavaVecaProcessor(feature_extractor=A		,     tokenizer=A)
																					processor.save_pretrained(A)

														UpperCamelCase   			=     WavaVecaConformerForCTC(A)
							else:
														UpperCamelCase   			=     WavaVecaConformerForPreTraining(A)

							if is_finetuned:
														UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]		,     arg_overrides={'data': '/'.join(dict_path.split('/')[:-1])})
							else:
														UpperCamelCase   			=     argparse.Namespace(task='audio_pretraining')
														UpperCamelCase   			=     fairseq.tasks.setup_task(A)

														UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,     task=A)

							UpperCamelCase   			=     model[0].eval()

							recursively_load_weights(A		,     A		,     not is_finetuned)

							hf_wavavec.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = 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'
						)
						lowerCAmelCase					:    Optional[int]           = parser.parse_args()
						convert_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




