import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE            =		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE            =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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",
}
_SCREAMING_SNAKE_CASE            =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			str						,__lowercase						:			Tuple						,__lowercase						:			Any						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    Optional[int]         =				getattr(A__						,A__     )

		if weight_type is not None:
				A_		:    Union[str, Any]         =				getattr(A__						,A__     ).shape
		else:
				A_		:    Any         =				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":
				A_		:    Tuple         =				value
		elif weight_type == "weight_g":
				A_		:    Optional[int]         =				value
		elif weight_type == "weight_v":
				A_		:    Tuple         =				value
		elif weight_type == "bias":
				A_		:    List[str]         =				value
		elif weight_type == "running_mean":
				A_		:    str         =				value
		elif weight_type == "running_var":
				A_		:    List[str]         =				value
		elif weight_type == "num_batches_tracked":
				A_		:    Union[str, Any]         =				value
		elif weight_type == "inv_freq":
				A_		:    Optional[Any]         =				value
		else:
				A_		:    int         =				value

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




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Any						,__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				[]
		A_		:    Tuple         =				fairseq_model.state_dict()

		A_		:    Tuple         =				hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				A_		:    int         =				False
				if "conv_layers" in name:
						load_conv_layer(
						    A__						,A__						,A__						,A__						,hf_model.config.feat_extract_norm == 'group'						,)
						A_		:    Optional[Any]         =				True
				else:
						for key, mapped_key in MAPPING.items():
								A_		:    Dict         =				"""wav2vec2_conformer.""" + 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]:
										A_		:    int         =				True
										if "*" in mapped_key:
												A_		:    Optional[int]         =				name.split(A__     )[0].split('.'     )[-2]
												A_		:    List[str]         =				mapped_key.replace('*'						,A__     )
										if "pos_bias_u" in name:
												A_		:    List[str]         =				None
										elif "pos_bias_v" in name:
												A_		:    Any         =				None
										elif "weight_g" in name:
												A_		:    str         =				"""weight_g"""
										elif "weight_v" in name:
												A_		:    Tuple         =				"""weight_v"""
										elif "bias" in name:
												A_		:    Optional[Any]         =				"""bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A_		:    Optional[Any]         =				"""weight"""
										elif "running_mean" in name:
												A_		:    List[str]         =				"""running_mean"""
										elif "inv_freq" in name:
												A_		:    Union[str, Any]         =				"""inv_freq"""
										elif "running_var" in name:
												A_		:    Any         =				"""running_var"""
										elif "num_batches_tracked" in name:
												A_		:    Tuple         =				"""num_batches_tracked"""
										else:
												A_		:    Any         =				None
										set_recursively(A__						,A__						,A__						,A__						,A__     )
								continue
				if not is_used:
						unused_weights.append(A__     )

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




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			Any						,__lowercase						:			List[Any]						,__lowercase						:			str						,__lowercase						:			Tuple     ):
		'''simple docstring'''




		A_		:    List[Any]         =				full_name.split('conv_layers.'     )[-1]
		A_		:    int         =				name.split('.'     )
		A_		:    Dict         =				int(items[0]     )
		A_		:    Dict         =				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.'''     )
						A_		:    Optional[Any]         =				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.'''     )
						A_		:    Union[str, Any]         =				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.'''     )
						A_		:    Tuple         =				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.'''     )
						A_		:    List[Any]         =				value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(A__     )




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Dict=None						,__lowercase						:			List[Any]=None						,__lowercase						:			Any=True     ):
		'''simple docstring'''




		if config_path is not None:
				A_		:    List[str]         =				WavaVecaConformerConfig.from_pretrained(A__						,hidden_act='swish'     )
		else:
				A_		:    int         =				WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				A_		:    str         =				"""rotary"""

		if is_finetuned:
				if dict_path:
						A_		:    Optional[Any]         =				Dictionary.load(A__     )

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

						# fairseq has the <pad> and <s> switched
						A_		:    str         =				0
						A_		:    List[str]         =				1
						with open(A__						,'w'						,encoding='utf-8'     ) as vocab_handle:
								json.dump(A__						,A__     )
						A_		:    List[str]         =				WavaVecaCTCTokenizer(
						    A__						,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=A__						,)
						A_		:    Optional[Any]         =				True if config.feat_extract_norm == """layer""" else False
						A_		:    str         =				WavaVecaFeatureExtractor(
						    feature_size=1						,sampling_rate=1_60_00						,padding_value=0						,do_normalize=A__						,return_attention_mask=A__						,)
						A_		:    str         =				WavaVecaProcessor(feature_extractor=A__						,tokenizer=A__     )
						processor.save_pretrained(A__     )

				A_		:    Dict         =				WavaVecaConformerForCTC(A__     )
		else:
				A_		:    List[Any]         =				WavaVecaConformerForPreTraining(A__     )

		if is_finetuned:
				A_		:    Dict         =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
		else:
				A_		:    Dict         =				argparse.Namespace(task='audio_pretraining'     )
				A_		:    str         =				fairseq.tasks.setup_task(A__     )

				A_		:    int         =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,task=A__     )

		A_		:    int         =				model[0].eval()

		recursively_load_weights(A__						,A__						,not is_finetuned     )

		hf_wavavec.save_pretrained(A__     )


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE            =		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"""
	)
	_SCREAMING_SNAKE_CASE            =		parser.parse_args()
	convert_wavaveca_conformer_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)







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


_UpperCAmelCase            =		{
    """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__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

	_UpperCAmelCase            =		ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

	print(F"""Total reward: {total_reward}""")






