


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase_	   =     {
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 __lowerCamelCase		:							List[Any]		 =						list(s_dict.keys()						)
 for key in keys:
  __lowerCamelCase		:							int		 =						r'.*/layers_(\d+)'
  __lowerCamelCase		:							List[str]		 =						key
  if re.match(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							Union[str, Any]		 =						re.sub(r'layers_(\d+)'      ,	r'block/\1/layer'      ,	SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							List[Any]		 =						r'(encoder|decoder)\/'

  if re.match(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							Dict		 =						re.match(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						).groups()
   if groups[0] == "encoder":
    __lowerCamelCase		:							Union[str, Any]		 =						re.sub(r'/mlp/'      ,	r'/1/mlp/'      ,	SCREAMING_SNAKE_CASE__						)
    __lowerCamelCase		:							str		 =						re.sub(r'/pre_mlp_layer_norm/'      ,	r'/1/layer_norm/'      ,	SCREAMING_SNAKE_CASE__						)

   elif groups[0] == "decoder":
    __lowerCamelCase		:							Tuple		 =						re.sub(r'/mlp/'      ,	r'/2/mlp/'      ,	SCREAMING_SNAKE_CASE__						)
    __lowerCamelCase		:							List[str]		 =						re.sub(r'/pre_mlp_layer_norm/'      ,	r'/2/layer_norm/'      ,	SCREAMING_SNAKE_CASE__						)

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    __lowerCamelCase		:							Union[str, Any]		 =						new_key.replace(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

  print(f'{key} -> {new_key}'						)
  __lowerCamelCase		:							Optional[int]		 =						s_dict.pop(SCREAMING_SNAKE_CASE__						)

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowerCamelCase		:							Optional[int]		 =						s_dict[
      'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowerCamelCase		:							Optional[int]		 =						s_dict[
      'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()						):
  if "expert" in key:
   __lowerCamelCase		:							Any		 =						s_dict[key].shape[0]
   __lowerCamelCase		:							List[str]		 =						s_dict[key]
   for idx in range(SCREAMING_SNAKE_CASE__						):
    __lowerCamelCase		:							Optional[int]		 =						expert_weihts[idx]
    print(f'{key} -> {key.replace("expert/"      ,	"nested fstring"						)}'						)

   s_dict.pop(SCREAMING_SNAKE_CASE__						)

 return s_dict


lowercase_	   =     {
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(SCREAMING_SNAKE_CASE__      ,	'r'						) as f:
  __lowerCamelCase		:							Optional[Any]		 =						f.read()

 __lowerCamelCase		:							str		 =						re.findall(r'(.*) = ([0-9.]*)'      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[Any]		 =						{}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   __lowerCamelCase		:							Dict		 =						float(SCREAMING_SNAKE_CASE__						) if '.' in value else int(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Tuple		 =						re.findall(r'(.*activations) = \(\'(.*)\',\)'      ,	SCREAMING_SNAKE_CASE__						)[0]
 __lowerCamelCase		:							Union[str, Any]		 =						str(activation[1]						)

 __lowerCamelCase		:							List[str]		 =						num_experts
 __lowerCamelCase		:							Union[str, Any]		 =						SwitchTransformersConfig(**SCREAMING_SNAKE_CASE__						)
 return config
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__="./"      ,	SCREAMING_SNAKE_CASE__=8						):
 # Initialise PyTorch model

 print(f'Loading flax weights from : {flax_checkpoint_path}'						)
 __lowerCamelCase		:							Tuple		 =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__						)

 if gin_file is not None:
  __lowerCamelCase		:							Optional[Any]		 =						convert_gin_to_config(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 else:
  __lowerCamelCase		:							Any		 =						SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Union[str, Any]		 =						SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							int		 =						flax_params['target']
 __lowerCamelCase		:							List[Any]		 =						flatten_dict(SCREAMING_SNAKE_CASE__      ,	sep='/'						)
 __lowerCamelCase		:							Optional[int]		 =						rename_keys(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]		 =						unflatten_dict(SCREAMING_SNAKE_CASE__      ,	sep='/'						)

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 print(f'Save PyTorch model to {pytorch_dump_path}'						)
 pt_model.save_pretrained(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       lowercase_	   =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--switch_t5x_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
               ' model architecture. If not provided, a `gin_file` has to be provided.'
           ),
       )
       parser.add_argument(
           '--gin_file',
           default=None,
           type=str,
           required=False,
           help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
       )
       parser.add_argument(
           '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
       )
       parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
       lowercase_	   =     parser.parse_args()
       convert_flax_checkpoint_to_pytorch(
           args.switch_tax_checkpoint_path,
           args.config_name,
           args.gin_file,
           args.pytorch_dump_folder_path,
           args.num_experts,
       )





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


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

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

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

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

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

