






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__A      =				[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					for attribute in key.split('''.''' ):
										_A								= getattr(_lowercase					,			_lowercase )

					if weight_type is not None:
										_A								= getattr(_lowercase					,			_lowercase ).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      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= fairseq_model.state_dict()

					_A								= hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					_A								= None

					for name, value in fairseq_dict.items():
										_A								= False
										if "conv_layers" in name:
															load_conv_layer(
															    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			hf_model.config.feat_extract_norm == '''group'''					,			)
															_A								= True
										elif name.split('''.''' )[0] == "proj":
															_A								= fairseq_model.proj
															_A								= True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
																									_A								= True
																									if "*" in mapped_key:
																														_A								= name.split(_lowercase )[0].split('''.''' )[-2]
																														_A								= mapped_key.replace('''*'''					,			_lowercase )
																									if "weight_g" in name:
																														_A								= '''weight_g'''
																									elif "weight_v" in name:
																														_A								= '''weight_v'''
																									elif "bias" in name:
																														_A								= '''bias'''
																									elif "weight" in name:
																														_A								= '''weight'''
																									else:
																														_A								= None
																									set_recursively(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
																				continue
										if not is_used:
															unused_weights.append(_lowercase )

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

					return proj_weight




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_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(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A							,_A								= emb.weight.shape
					_A								= nn.Linear(_lowercase					,			_lowercase					,			bias=_lowercase )
					_A								= emb.weight.data
					return lin_layer




def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(_lowercase					,			'''r'''					,			encoding='''utf-8''' ) as f:
										_A								= f.readlines()
										_A								= [line.split(''' ''' )[0] for line in lines]

					_A								= len(_lowercase )

					_A								= {
					    '''<s>''': 0,
					    '''<pad>''': 1,
					    '''</s>''': 2,
					    '''<unk>''': 3,
					}

					vocab_dict.update(dict(zip(_lowercase					,			range(4					,			num_words + 4 ) ) ) )
					return vocab_dict




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			):

					'''simple docstring'''







					_A								= WavaVecaConfig.from_pretrained(_lowercase )
					_A								= SpeechaTextaConfig.from_pretrained(
					    _lowercase					,			vocab_size=_lowercase					,			decoder_layers=_lowercase					,			do_stable_layer_norm=_lowercase )

					_A								= WavaVecaFeatureExtractor(
					    feature_size=1					,			sampling_rate=1_60_00					,			padding_value=0					,			do_normalize=_lowercase					,			return_attention_mask=_lowercase					,			)

					_A							,_A							,_A								= fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]					,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
					_A								= model[0].eval()

					# set weights for wav2vec2 encoder
					_A								= WavaVecaModel(_lowercase )
					_A								= recursively_load_weights_wavaveca(model.encoder					,			_lowercase )

					_A								= SpeechaTextaForCausalLM(_lowercase )
					_A							,_A								= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,			strict=_lowercase )

					# set output linear layer
					unexpected_keys.remove('''embed_out''' )
					_A								= nn.Parameter(model.decoder.embed_out.detach() )

					# layer norm is init to identity matrix so leaving it is fine
					logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
					logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )

					_A								= SpeechEncoderDecoderModel(encoder=_lowercase					,			decoder=_lowercase )
					_A								= False

					# add projection layer
					_A								= nn.Parameter(projection_layer.weight )
					_A								= nn.Parameter(projection_layer.bias )

					_A								= create_vocab_dict(_lowercase )

					with open(os.path.join(_lowercase					,			'''vocab.json''' )					,			'''w''' ) as fp:
										json.dump(_lowercase					,			_lowercase )

					_A								= SpeechaTextaTokenizer(os.path.join(_lowercase					,			'''vocab.json''' ) )
					tokenizer.save_pretrained(_lowercase )

					_A								= hf_wavavec.config.to_dict()
					_A								= tokenizer.pad_token_id
					_A								= tokenizer.bos_token_id
					_A								= tokenizer.eos_token_id
					_A								= '''speech_to_text_2'''
					_A								= '''wav2vec2'''

					_A								= SpeechEncoderDecoderConfig.from_dict(_lowercase )

					hf_wavavec.save_pretrained(_lowercase )
					feature_extractor.save_pretrained(_lowercase )


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(
				    '--encoder_config_path',
				    default='facebook/wav2vec2-large-lv60',
				    type=str,
				    help='Path to hf encoder wav2vec2 checkpoint config',
				)
				parser.add_argument(
				    '--decoder_config_path',
				    default='facebook/s2t-small-mustc-en-fr-st',
				    type=str,
				    help='Path to hf decoder s2t checkpoint config',
				)
				parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
				parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

				__A      =				parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= len(_lowercase ), len(grid[0] )
					if (
					    min(_lowercase					,			_lowercase ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col) )

					_A								= 0
					count += depth_first_search(_lowercase					,			row + 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			row - 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col + 1					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col - 1					,			_lowercase )

					visit.remove((row, col) )
					return count


if __name__ == "__main__":
				import doctest

				doctest.testmod()



