


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

A	:		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',
}
A	:		Any    =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							str   ,   __magic_name__					:							Dict   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							Dict					)       ->  Any:
		"""simple docstring"""




		for attribute in key.split("""."""					):
				lowercase__					      =		getattr(__magic_name__   ,   __magic_name__					)

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

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






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							Optional[int]					)       ->  Tuple:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		fairseq_model.state_dict()

		lowercase__					      =		hf_model.feature_extractor

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

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

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

		return proj_weight






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Any   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							int   ,   __magic_name__					:							Dict					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		full_name.split("""conv_layers."""					)[-1]
		lowercase__					      =		name.split("""."""					)
		lowercase__					      =		int(items[0]					)
		lowercase__					      =		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.'''
						)
						lowercase__					      =		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.'''
						)
						lowercase__					      =		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."
						)
						lowercase__					      =		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.'''
						)
						lowercase__					      =		value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
		else:
				unused_weights.append(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							Any					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__      ,     lowercase__					      =		emb.weight.shape
		lowercase__					      =		nn.Linear(__magic_name__   ,   __magic_name__   ,   bias=__magic_name__					)
		lowercase__					      =		emb.weight.data
		return lin_layer






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  Union[str, Any]:
		"""simple docstring"""




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

		lowercase__					      =		len(__magic_name__					)

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

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






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[int]   ,   )       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		WavaVecaConfig.from_pretrained(__magic_name__					)
		lowercase__					      =		SpeechaTextaConfig.from_pretrained(
		    __magic_name__   ,   vocab_size=__magic_name__   ,   decoder_layers=__magic_name__   ,   do_stable_layer_norm=__magic_name__					)

		lowercase__					      =		WavaVecaFeatureExtractor(
		    feature_size=1   ,   sampling_rate=1_6000   ,   padding_value=0   ,   do_normalize=__magic_name__   ,   return_attention_mask=__magic_name__   ,   )

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

		# set weights for wav2vec2 encoder
		lowercase__					      =		WavaVecaModel(__magic_name__					)
		lowercase__					      =		recursively_load_weights_wavaveca(model.encoder   ,   __magic_name__					)

		lowercase__					      =		SpeechaTextaForCausalLM(__magic_name__					)
		lowercase__      ,     lowercase__					      =		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,   strict=__magic_name__					)

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

		lowercase__					      =		SpeechEncoderDecoderModel(encoder=__magic_name__   ,   decoder=__magic_name__					)
		lowercase__					      =		False

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

		lowercase__					      =		create_vocab_dict(__magic_name__					)

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

		lowercase__					      =		SpeechaTextaTokenizer(os.path.join(__magic_name__   ,   """vocab.json"""					)					)
		tokenizer.save_pretrained(__magic_name__					)

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

		lowercase__					      =		SpeechEncoderDecoderConfig.from_dict(__magic_name__					)

		hf_wavavec.save_pretrained(__magic_name__					)
		feature_extractor.save_pretrained(__magic_name__					)


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(
	    '--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=1_0_2_2_4, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	A	:		Union[str, Any]    =       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 __future__ import annotations

from collections import deque



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Any				, _UpperCAmelCase   :						list[str]		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		[]
							self.adlist.append(
							    {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}		)

							for keyword in keywords:
									self.add_keyword(_UpperCAmelCase		)
							self.set_fail_transitions()

					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						str		)						->       int | None:




							"""simple docstring"""


							for state in self.adlist[current_state]["next_states"]:
									if char == self.adlist[state]["value"]:
											return state
							return None

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						str		)						->       None:




							"""simple docstring"""


							lowercase__					      =		0
							for character in keyword:
									lowercase__					      =		self.find_next_state(_UpperCAmelCase				, _UpperCAmelCase		)
									if next_state is None:
											self.adlist.append(
											    {
											        """value""": character,
											        """next_states""": [],
											        """fail_state""": 0,
											        """output""": [],
											    }		)
											self.adlist[current_state]["next_states"].append(len(self.adlist		) - 1		)
											lowercase__					      =		len(self.adlist		) - 1
									else:
											lowercase__					      =		next_state
							self.adlist[current_state]["output"].append(_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Any		)						->       None:




							"""simple docstring"""


							lowercase__					      =		deque()
							for node in self.adlist[0]["next_states"]:
									q.append(_UpperCAmelCase		)
									lowercase__					      =		0
							while q:
									lowercase__					      =		q.popleft()
									for child in self.adlist[r]["next_states"]:
											q.append(_UpperCAmelCase		)
											lowercase__					      =		self.adlist[r]["""fail_state"""]
											while (
											    self.find_next_state(_UpperCAmelCase				, self.adlist[child]["""value"""]		) is None
											    and state != 0
											):
													lowercase__					      =		self.adlist[state]["""fail_state"""]
											lowercase__					      =		self.find_next_state(
											    _UpperCAmelCase				, self.adlist[child]["""value"""]		)
											if self.adlist[child]["fail_state"] is None:
													lowercase__					      =		0
											lowercase__					      =		(
											    self.adlist[child]["""output"""]
											    + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
											)


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						str		)						->       dict[str, list[int]]:




							"""simple docstring"""


							lowercase__					      =		{}  # returns a dict with keywords and list of its occurrences
							lowercase__					      =		0
							for i in range(len(_UpperCAmelCase		)		):
									while (
									    self.find_next_state(_UpperCAmelCase				, string[i]		) is None
									    and current_state != 0
									):
											lowercase__					      =		self.adlist[current_state]["""fail_state"""]
									lowercase__					      =		self.find_next_state(_UpperCAmelCase				, string[i]		)
									if next_state is None:
											lowercase__					      =		0
									else:
											lowercase__					      =		next_state
											for key in self.adlist[current_state]["output"]:
													if key not in result:
															lowercase__					      =		[]
													result[key].append(i - len(_UpperCAmelCase		) + 1		)
							return result


if __name__ == "__main__":
	import doctest

	doctest.testmod()

