




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__lowerCAmelCase :int     =    logging.get_logger(__name__)

__lowerCAmelCase :Tuple     =    {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
__lowerCAmelCase :Dict     =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   {}
					with open(UpperCAmelCase  ,  "r"				) as file:
										for line_number, line in enumerate(UpperCAmelCase				):
															_snake_case       :					Union[str, Any]				       =   line.strip()
															if line:
																				_snake_case       :					Optional[int]				       =   line.split()
																				_snake_case       :					Any				       =   line_number
																				_snake_case       :					Dict				       =   words[0]
																				_snake_case       :					str				       =   value
					return result







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					for attribute in key.split("."				):
										_snake_case       :					List[str]				       =   getattr(UpperCAmelCase  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(UpperCAmelCase				):
															_snake_case       :					Optional[int]				       =   PARAM_MAPPING[full_name.split("."				)[-1]]
															_snake_case       :					Any				       =   "param"

					if weight_type is not None and weight_type != "param":
										_snake_case       :					Tuple				       =   getattr(UpperCAmelCase  ,  UpperCAmelCase				).shape
					elif weight_type is not None and weight_type == "param":
										_snake_case       :					str				       =   hf_pointer
										for attribute in hf_param_name.split("."				):
															_snake_case       :					Tuple				       =   getattr(UpperCAmelCase  ,  UpperCAmelCase				)
										_snake_case       :					Tuple				       =   shape_pointer.shape

										# let's reduce dimension
										_snake_case       :					Tuple				       =   value[0]
					else:
										_snake_case       :					int				       =   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":
										_snake_case       :					Tuple				       =   value
					elif weight_type == "weight_g":
										_snake_case       :					Any				       =   value
					elif weight_type == "weight_v":
										_snake_case       :					Optional[Any]				       =   value
					elif weight_type == "bias":
										_snake_case       :					int				       =   value
					elif weight_type == "param":
										for attribute in hf_param_name.split("."				):
															_snake_case       :					List[Any]				       =   getattr(UpperCAmelCase  ,  UpperCAmelCase				)
										_snake_case       :					Tuple				       =   value
					else:
										_snake_case       :					str				       =   value

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







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Union[str, Any]				       =   None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(UpperCAmelCase				):
															_snake_case       :					Any				       =   PARAM_MAPPING[full_name.split("."				)[-1]]
															_snake_case       :					str				       =   "param"

					if weight_type is not None and weight_type != "param":
										_snake_case       :					Any				       =   ".".join([key, weight_type]				)
					elif weight_type is not None and weight_type == "param":
										_snake_case       :					int				       =   ".".join([key, hf_param_name]				)
					else:
										_snake_case       :					Tuple				       =   key

					_snake_case       :					str				       =   value if "lm_head" in full_key else value[0]


__lowerCAmelCase :Tuple     =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None				):
					_snake_case       :					int				       =   False
					for key, mapped_key in MAPPING.items():
										_snake_case       :					List[str]				       =   "wav2vec2." + 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]:
															_snake_case       :					Union[str, Any]				       =   True
															if "*" in mapped_key:
																				_snake_case       :					List[Any]				       =   name.split(UpperCAmelCase				)[0].split("."				)[-2]
																				_snake_case       :					List[Any]				       =   mapped_key.replace("*"  ,  UpperCAmelCase				)
															if "weight_g" in name:
																				_snake_case       :					Union[str, Any]				       =   "weight_g"
															elif "weight_v" in name:
																				_snake_case       :					int				       =   "weight_v"
															elif "bias" in name:
																				_snake_case       :					str				       =   "bias"
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				_snake_case       :					Dict				       =   "weight"
															else:
																				_snake_case       :					str				       =   None
															if hf_dict is not None:
																				rename_dict(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)
															else:
																				set_recursively(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)
															return is_used
					return is_used







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[Any]				       =   []
					_snake_case       :					Optional[Any]				       =   fairseq_model.state_dict()

					_snake_case       :					List[Any]				       =   hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										_snake_case       :					Tuple				       =   False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  hf_model.config.feat_extract_norm == "group"  ,  )
															_snake_case       :					Optional[Any]				       =   True
										else:
															_snake_case       :					Dict				       =   load_wavaveca_layer(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)
										if not is_used:
															unused_weights.append(UpperCAmelCase				)

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







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







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False				):
					if config_path is not None:
										_snake_case       :					Tuple				       =   WavaVecaConfig.from_pretrained(UpperCAmelCase				)
					else:
										_snake_case       :					str				       =   WavaVecaConfig()

					if is_seq_class:
										_snake_case       :					Tuple				       =   read_txt_into_dict(UpperCAmelCase				)
										_snake_case       :					Tuple				       =   idalabel
										_snake_case       :					str				       =   WavaVecaForSequenceClassification(UpperCAmelCase				)
										_snake_case       :					int				       =   WavaVecaFeatureExtractor(
										    feature_size=1  ,  sampling_rate=16_000  ,  padding_value=0  ,  do_normalize=UpperCAmelCase  ,  return_attention_mask=UpperCAmelCase  ,  )
										feature_extractor.save_pretrained(UpperCAmelCase				)

					elif is_finetuned:
										if dict_path:
															_snake_case       :					Optional[int]				       =   Dictionary.load(UpperCAmelCase				)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_snake_case       :					Tuple				       =   target_dict.pad_index
															_snake_case       :					List[Any]				       =   target_dict.bos_index
															_snake_case       :					Dict				       =   target_dict.eos_index
															_snake_case       :					str				       =   len(target_dict.symbols				)
															_snake_case       :					List[str]				       =   os.path.join(UpperCAmelCase  ,  "vocab.json"				)
															if not os.path.isdir(UpperCAmelCase				):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase				)				)
																				return
															os.makedirs(UpperCAmelCase  ,  exist_ok=UpperCAmelCase				)
															_snake_case       :					Union[str, Any]				       =   target_dict.indices

															# fairseq has the <pad> and <s> switched
															_snake_case       :					List[Any]				       =   0
															_snake_case       :					str				       =   1
															with open(UpperCAmelCase  ,  "w"  ,  encoding="utf-8"				) as vocab_handle:
																				json.dump(UpperCAmelCase  ,  UpperCAmelCase				)
															_snake_case       :					Tuple				       =   WavaVecaCTCTokenizer(
															    UpperCAmelCase  ,  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=UpperCAmelCase  ,  )
															_snake_case       :					Any				       =   True if config.feat_extract_norm == "layer" else False
															_snake_case       :					int				       =   WavaVecaFeatureExtractor(
															    feature_size=1  ,  sampling_rate=16_000  ,  padding_value=0  ,  do_normalize=UpperCAmelCase  ,  return_attention_mask=UpperCAmelCase  ,  )
															_snake_case       :					Optional[Any]				       =   WavaVecaProcessor(feature_extractor=UpperCAmelCase  ,  tokenizer=UpperCAmelCase				)
															processor.save_pretrained(UpperCAmelCase				)

										_snake_case       :					List[Any]				       =   WavaVecaForCTC(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   WavaVecaForPreTraining(UpperCAmelCase				)

					if is_finetuned or is_seq_class:
										_snake_case       :					List[Any]				       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]  ,  arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
					else:
										_snake_case       :					List[Any]				       =   argparse.Namespace(task="audio_pretraining"				)
										_snake_case       :					List[Any]				       =   fairseq.tasks.setup_task(UpperCAmelCase				)

										_snake_case       :					Dict				       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,  task=UpperCAmelCase				)

					_snake_case       :					Optional[int]				       =   model[0].eval()

					recursively_load_weights(UpperCAmelCase  ,  UpperCAmelCase  ,  not is_finetuned				)

					hf_wavavec.save_pretrained(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :int     =    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'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							__lowerCAmelCase :str     =    parser.parse_args()

							__lowerCAmelCase :Optional[int]     =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)







def    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "down_block_types": down_block_types,
					    "block_out_channels": block_out_channels,
					    "up_block_types": up_block_types,
					    "layers_per_block": 1,
					    "use_timestep_embedding": True,
					    "out_block_type": "OutConv1DBlock",
					    "norm_num_groups": 8,
					    "downsample_each_block": False,
					    "in_channels": 14,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "sample_size": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					Any				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   state_dict.pop(UpperCAmelCase				)
					hf_value_function.load_state_dict(UpperCAmelCase				)

					torch.save(hf_value_function.state_dict()  ,  F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""				)
					with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""  ,  "w"				) as f:
										json.dump(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					Any				       =   {
					    "in_channels": 14,
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": (),
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": (32, 64, 128, 256),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 65_536,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "norm_num_groups": 8,
					    "act_fn": "mish",
					}

					_snake_case       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)

					_snake_case       :					Optional[int]				       =   dict(zip(state_dict.keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Any				       =   state_dict.pop(UpperCAmelCase				)

					hf_value_function.load_state_dict(UpperCAmelCase				)

					torch.save(hf_value_function.state_dict()  ,  "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"				)
					with open("hub/hopper-medium-v2/value_function/config.json"  ,  "w"				) as f:
										json.dump(UpperCAmelCase  ,  UpperCAmelCase				)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()