






"""simple docstring"""



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__	:    int			 = logging.get_logger(__name__)

A__	:    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',
}
A__	:    Tuple			 = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Union[str, Any]  )    ->     Any:
							for attribute in key.split("."  ):
														lowerCamelCase_	:							Any									=getattr(lowerCamelCase__    ,						lowerCamelCase__  )

							if weight_type is not None:
														lowerCamelCase_	:							Dict									=getattr(lowerCamelCase__    ,						lowerCamelCase__  ).shape
							else:
														lowerCamelCase_	:							int									=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":
														lowerCamelCase_	:							Tuple									=value
							elif weight_type == "weight_g":
														lowerCamelCase_	:							Dict									=value
							elif weight_type == "weight_v":
														lowerCamelCase_	:							Any									=value
							elif weight_type == "bias":
														lowerCamelCase_	:							List[Any]									=value
							else:
														lowerCamelCase_	:							Tuple									=value

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

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]    ,						lowerCamelCase__     :   int  )    ->     List[Any]:
							lowerCamelCase_	:							Dict									=[]
							lowerCamelCase_	:							Any									=fairseq_model.state_dict()

							lowerCamelCase_	:							List[Any]									=hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							lowerCamelCase_	:							int									=None

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

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

							return proj_weight

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]    ,						lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   str  )    ->     str:
							lowerCamelCase_	:							Dict									=full_name.split("conv_layers."  )[-1]
							lowerCamelCase_	:							Tuple									=name.split("."  )
							lowerCamelCase_	:							int									=int(items[0]  )
							lowerCamelCase_	:							str									=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."""
																					)
																					lowerCamelCase_	:							List[Any]									=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."""
																					)
																					lowerCamelCase_	:							Tuple									=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."
																					)
																					lowerCamelCase_	:							str									=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."""
																					)
																					lowerCamelCase_	:							str									=value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
							else:
														unused_weights.append(lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Dict:
							lowerCamelCase_							,						lowerCamelCase_	:							str									=emb.weight.shape
							lowerCamelCase_	:							int									=nn.Linear(lowerCamelCase__    ,						lowerCamelCase__    ,						bias=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=emb.weight.data
							return lin_layer

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]  )    ->     Optional[int]:
							with open(lowerCamelCase__    ,						"r"    ,						encoding="utf-8"  ) as f:
														lowerCamelCase_	:							Tuple									=f.readlines()
														lowerCamelCase_	:							Dict									=[line.split(" "  )[0] for line in lines]

							lowerCamelCase_	:							Optional[int]									=len(lowerCamelCase__  )

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

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

@torch.no_grad()
def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Tuple    ,						)    ->     Optional[int]:
							lowerCamelCase_	:							str									=WavaVecaConfig.from_pretrained(lowerCamelCase__  )
							lowerCamelCase_	:							Dict									=SpeechaTextaConfig.from_pretrained(
							    lowerCamelCase__    ,						vocab_size=lowerCamelCase__    ,						decoder_layers=lowerCamelCase__    ,						do_stable_layer_norm=lowerCamelCase__  )

							lowerCamelCase_	:							Any									=WavaVecaFeatureExtractor(
							    feature_size=1    ,						sampling_rate=16_000    ,						padding_value=0    ,						do_normalize=lowerCamelCase__    ,						return_attention_mask=lowerCamelCase__    ,						)

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

							# set weights for wav2vec2 encoder
							lowerCamelCase_	:							str									=WavaVecaModel(lowerCamelCase__  )
							lowerCamelCase_	:							int									=recursively_load_weights_wavaveca(model.encoder    ,						lowerCamelCase__  )

							lowerCamelCase_	:							Union[str, Any]									=SpeechaTextaForCausalLM(lowerCamelCase__  )
							lowerCamelCase_							,						lowerCamelCase_	:							Dict									=hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,						strict=lowerCamelCase__  )

							# set output linear layer
							unexpected_keys.remove("embed_out"  )
							lowerCamelCase_	:							List[Any]									=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}"""  )

							lowerCamelCase_	:							int									=SpeechEncoderDecoderModel(encoder=lowerCamelCase__    ,						decoder=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=False

							# add projection layer
							lowerCamelCase_	:							Optional[int]									=nn.Parameter(projection_layer.weight  )
							lowerCamelCase_	:							int									=nn.Parameter(projection_layer.bias  )

							lowerCamelCase_	:							Optional[int]									=create_vocab_dict(lowerCamelCase__  )

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

							lowerCamelCase_	:							Tuple									=SpeechaTextaTokenizer(os.path.join(lowerCamelCase__    ,						"vocab.json"  )  )
							tokenizer.save_pretrained(lowerCamelCase__  )

							lowerCamelCase_	:							Optional[int]									=hf_wavavec.config.to_dict()
							lowerCamelCase_	:							Optional[int]									=tokenizer.pad_token_id
							lowerCamelCase_	:							Union[str, Any]									=tokenizer.bos_token_id
							lowerCamelCase_	:							List[str]									=tokenizer.eos_token_id
							lowerCamelCase_	:							Union[str, Any]									="speech_to_text_2"
							lowerCamelCase_	:							Optional[int]									="wav2vec2"

							lowerCamelCase_	:							List[str]									=SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

							hf_wavavec.save_pretrained(lowerCamelCase__  )
							feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
						A__	:    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=10_224, type=int, help='Vocab size of decoder')
						parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

						A__	:    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,
						)











"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																		    f' {total_score}'
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



