






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()
__UpperCamelCase					:		Optional[int]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		Optional[Any]	  =						{
    """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""",
}
__UpperCamelCase					:		Optional[Any]	  =						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Dict:







				"""simple docstring"""





				for attribute in key.split('.'  ):
								snake_case__									=     getattr(SCREAMING_SNAKE_CASE__      ,   SCREAMING_SNAKE_CASE__  )

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

				logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )
def      a_ (      _A      ,   _A  )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     []
				snake_case__									=     fairseq_model.state_dict()

				snake_case__									=     hf_model.feature_extractor

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

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

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

				return proj_weight
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     full_name.split('conv_layers.'  )[-1]
				snake_case__									=     name.split('.'  )
				snake_case__									=     int(items[0]  )
				snake_case__									=     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.'''
												)
												snake_case__									=     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.'''
												)
												snake_case__									=     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."
												)
												snake_case__									=     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.'''
												)
												snake_case__									=     value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__  )
def      a_ (      _A  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     emb.weight.shape
				snake_case__									=     nn.Linear(SCREAMING_SNAKE_CASE__      ,   SCREAMING_SNAKE_CASE__      ,   bias=SCREAMING_SNAKE_CASE__  )
				snake_case__									=     emb.weight.data
				return lin_layer
def      a_ (      _A  )			->			Any:







				"""simple docstring"""





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

				snake_case__									=     len(SCREAMING_SNAKE_CASE__  )

				snake_case__									=     {
				    """<s>""": 0,
				    """<pad>""": 1,
				    """</s>""": 2,
				    """<unk>""": 3,
				}

				vocab_dict.update(dict(zip(SCREAMING_SNAKE_CASE__      ,   range(4      ,   num_words + 4  )  )  )  )
				return vocab_dict
@torch.no_grad()
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A      ,   _A      ,   _A      ,   )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__  )
				snake_case__									=     SpeechaTextaConfig.from_pretrained(
				    SCREAMING_SNAKE_CASE__      ,   vocab_size=SCREAMING_SNAKE_CASE__      ,   decoder_layers=SCREAMING_SNAKE_CASE__      ,   do_stable_layer_norm=SCREAMING_SNAKE_CASE__  )

				snake_case__									=     WavaVecaFeatureExtractor(
				    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=SCREAMING_SNAKE_CASE__      ,   return_attention_mask=SCREAMING_SNAKE_CASE__      ,   )

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

				# set weights for wav2vec2 encoder
				snake_case__									=     WavaVecaModel(SCREAMING_SNAKE_CASE__  )
				snake_case__									=     recursively_load_weights_wavaveca(model.encoder      ,   SCREAMING_SNAKE_CASE__  )

				snake_case__									=     SpeechaTextaForCausalLM(SCREAMING_SNAKE_CASE__  )
				snake_case__									=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,   strict=SCREAMING_SNAKE_CASE__  )

				# set output linear layer
				unexpected_keys.remove('embed_out'  )
				snake_case__									=     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}'''  )

				snake_case__									=     SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__      ,   decoder=SCREAMING_SNAKE_CASE__  )
				snake_case__									=     False

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

				snake_case__									=     create_vocab_dict(SCREAMING_SNAKE_CASE__  )

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

				snake_case__									=     SpeechaTextaTokenizer(os.path.join(SCREAMING_SNAKE_CASE__      ,   'vocab.json'  )  )
				tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__  )

				snake_case__									=     hf_wavavec.config.to_dict()
				snake_case__									=     tokenizer.pad_token_id
				snake_case__									=     tokenizer.bos_token_id
				snake_case__									=     tokenizer.eos_token_id
				snake_case__									=     """speech_to_text_2"""
				snake_case__									=     """wav2vec2"""

				snake_case__									=     SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__  )

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__  )
				feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
							__UpperCamelCase					:		Union[str, 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(
							    """--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""")

							__UpperCamelCase					:		List[str]	  =						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,
							)








from argparse import ArgumentParser

from .env import EnvironmentCommand
def      a_ (      )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     ArgumentParser('Diffusers CLI tool'      ,   usage='diffusers-cli <command> [<args>]'  )
				snake_case__									=     parser.add_subparsers(help='diffusers-cli command helpers'  )

				# Register commands
				EnvironmentCommand.register_subcommand(_A  )

				# Let's go
				snake_case__									=     parser.parse_args()

				if not hasattr(_A      ,   'func'  ):
								parser.print_help()
								exit(1  )

				# Run
				snake_case__									=     args.func(_A  )
				service.run()


if __name__ == "__main__":
							main()




