



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Any:
							for attribute in key.split('.'			):
														A         =					getattr(lowerCamelCase__				,      lowerCamelCase__			)

							if weight_type is not None:
														A         =					getattr(lowerCamelCase__				,      lowerCamelCase__			).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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Union[str, Any]:
							A         =					[]
							A         =					fairseq_model.state_dict()

							A         =					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														A         =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      hf_model.config.feat_extract_norm == 'group'				,      )
																					A         =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												A         =					'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																												if key in name or (key.split('w2v_model.'			)[-1] == name.split('.'			)[0] and not is_finetuned):
																																			A         =					True
																																			if "*" in mapped_key:
																																										A         =					name.split(lowerCamelCase__			)[0].split('.'			)[-2]
																																										A         =					mapped_key.replace('*'				,      lowerCamelCase__			)
																																			if "weight_g" in name:
																																										A         =					'weight_g'
																																			elif "weight_v" in name:
																																										A         =					'weight_v'
																																			elif "weight" in name:
																																										A         =					'weight'
																																			elif "bias" in name:
																																										A         =					'bias'
																																			else:
																																										A         =					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__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Optional[Any]:
							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(lowerCamelCase__			)

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=True			) ->							List[str]:
							if config_path is not None:
														A         =					HubertConfig.from_pretrained(lowerCamelCase__			)
							else:
														A         =					HubertConfig()

							if is_finetuned:
														if dict_path:
																					A         =					Dictionary.load(lowerCamelCase__			)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					A         =					target_dict.pad_index
																					A         =					target_dict.bos_index
																					A         =					target_dict.eos_index
																					A         =					len(target_dict.symbols			)
																					A         =					os.path.join(lowerCamelCase__				,      'vocab.json'			)
																					if not os.path.isdir(lowerCamelCase__			):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCamelCase__			)			)
																												return
																					os.makedirs(lowerCamelCase__				,      exist_ok=lowerCamelCase__			)
																					with open(lowerCamelCase__				,      'w'				,      encoding='utf-8'			) as vocab_handle:
																												json.dump(target_dict.indices				,      lowerCamelCase__			)
																					A         =					WavaVecaCTCTokenizer(
																					    lowerCamelCase__				,      unk_token=target_dict.unk_word				,      pad_token=target_dict.pad_word				,      bos_token=target_dict.bos_word				,      eos_token=target_dict.eos_word				,      word_delimiter_token='|'				,      do_lower_case=lowerCamelCase__				,      )
																					A         =					True if config.feat_extract_norm == 'layer' else False
																					A         =					WavaVecaFeatureExtractor(
																					    feature_size=1				,      sampling_rate=16000				,      padding_value=0				,      do_normalize=lowerCamelCase__				,      return_attention_mask=lowerCamelCase__				,      )
																					A         =					WavaVecaProcessor(feature_extractor=lowerCamelCase__				,      tokenizer=lowerCamelCase__			)
																					processor.save_pretrained(lowerCamelCase__			)

														A         =					HubertForCTC(lowerCamelCase__			)
							else:
														A         =					HubertModel(lowerCamelCase__			)

							if is_finetuned:
														A      ,  A      ,  A         =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]				,      arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
							else:
														A      ,  A      ,  A         =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

							A         =					model[0].eval()

							recursively_load_weights(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

							hf_wavavec.save_pretrained(lowerCamelCase__			)


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('--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    =			parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					state_dim
									A         =					act_dim
									A         =					hidden_size
									A         =					max_ep_len
									A         =					action_tanh
									A         =					vocab_size
									A         =					n_positions
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					scale_attn_weights
									A         =					use_cache
									A         =					scale_attn_by_inverse_layer_idx
									A         =					reorder_and_upcast_attn

									A         =					bos_token_id
									A         =					eos_token_id

									super().__init__(bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)



