


"""simple docstring"""


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A     :   Tuple			    =			logging.get_logger(__name__)

A     :   Optional[int]			    =			{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
A     :   List[str]			    =			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			for attribute in key.split("."      ):
						__lowerCAmelCase				    =			getattr(__lowerCamelCase    , __lowerCamelCase      )

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

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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			fairseq_model.state_dict()

			__lowerCAmelCase				    =			hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						__lowerCAmelCase				    =			False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase    , __lowerCamelCase    , __lowerCamelCase    , __lowerCamelCase    , hf_model.config.feat_extract_norm == "group"    , )
									__lowerCAmelCase				    =			True
						else:
									for key, mapped_key in MAPPING.items():
												__lowerCAmelCase				    =			"unispeech_sat." + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split("."      )[:-1]      ) != key):
																		# special case since naming is very similar
																		continue
															__lowerCAmelCase				    =			True
															if "*" in mapped_key:
																		__lowerCAmelCase				    =			name.split(__lowerCamelCase      )[0].split("."      )[-2]
																		__lowerCAmelCase				    =			mapped_key.replace("*"    , __lowerCamelCase      )
															if "weight_g" in name:
																		__lowerCAmelCase				    =			"weight_g"
															elif "weight_v" in name:
																		__lowerCAmelCase				    =			"weight_v"
															elif "bias" in name:
																		__lowerCAmelCase				    =			"bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		__lowerCAmelCase				    =			"weight"
															else:
																		__lowerCAmelCase				    =			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	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			full_name.split("conv_layers."      )[-1]
			__lowerCAmelCase				    =			name.split("."      )
			__lowerCAmelCase				    =			int(items[0]      )
			__lowerCAmelCase				    =			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."      )
									__lowerCAmelCase				    =			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."      )
									__lowerCAmelCase				    =			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[layer_id].layer_norm.bias.data.shape} was found."      )
									__lowerCAmelCase				    =			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[layer_id].layer_norm.weight.data.shape} was found."      )
									__lowerCAmelCase				    =			value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
			else:
						unused_weights.append(__lowerCamelCase      )


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None    , _UpperCamelCase=True      ):

			'''simple docstring'''




			if config_path is not None:
						__lowerCAmelCase				    =			UniSpeechSatConfig.from_pretrained(__lowerCamelCase      )
			else:
						__lowerCAmelCase				    =			UniSpeechSatConfig()

			__lowerCAmelCase				    =			""

			if is_finetuned:
						__lowerCAmelCase				    =			UniSpeechSatForCTC(__lowerCamelCase      )
			else:
						__lowerCAmelCase				    =			UniSpeechSatForPreTraining(__lowerCamelCase      )

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

			recursively_load_weights(__lowerCamelCase    , __lowerCamelCase      )

			hf_wavavec.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
	A     :   Dict			    =			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     :   Any			    =			parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)






"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



