import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
					if is_torch_available():
										import torch
					if is_tf_available():
										import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase       =		{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase       =		{
    "Salesforce/codegen-350M-mono": 2_048,
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     VOCAB_FILES_NAMES
							UpperCAmelCase				   =     PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase				   =     ["input_ids", "attention_mask"]
							UpperCAmelCase				   =     CodeGenTokenizer







							def __init__(   self      :     Optional[Any] ,			_A      :     List[Any]=None ,			_A      :     str=None ,			_A      :     str=None ,			_A      :     List[str]="<|endoftext|>" ,			_A      :     Any="<|endoftext|>" ,			_A      :     Optional[Any]="<|endoftext|>" ,			_A      :     List[Any]=False ,			**_A      :     Optional[int] ,			):
												super().__init__(
												    _A ,			_A ,			tokenizer_file=_A ,			unk_token=_A ,			bos_token=_A ,			eos_token=_A ,			add_prefix_space=_A ,			**_A ,			)

												if kwargs.pop('''add_bos_token''' ,			_A		):
																	_UpperCamelCase			     =     kwargs.pop('''name_or_path''' ,			''''''		)
																	raise ValueError(
																	    '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
																	    '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
																	    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
																	    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
																	    '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
																	    ''' so that the fast tokenizer works correctly.'''		)

												_UpperCamelCase			     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
												if pre_tok_state.get('''add_prefix_space''' ,			_A		) != add_prefix_space:
																	_UpperCamelCase			     =     getattr(_A ,			pre_tok_state.pop('''type'''		)		)
																	_UpperCamelCase			     =     add_prefix_space
																	_UpperCamelCase			     =     pre_tok_class(**_A		)

												_UpperCamelCase			     =     add_prefix_space







							def  UpperCamelCase_						(   self      :     int ,			*_A      :     int ,			**_A      :     Any		):
												_UpperCamelCase			     =     kwargs.get('''is_split_into_words''' ,			_A		)
												assert self.add_prefix_space or not is_split_into_words, (
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			*_A      :     Dict ,			**_A      :     int		):
												_UpperCamelCase			     =     kwargs.get('''is_split_into_words''' ,			_A		)

												assert self.add_prefix_space or not is_split_into_words, (
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str ,			_A      :     Optional[str] = None		):
												_UpperCamelCase			     =     self._tokenizer.model.save(_A ,			name=_A		)
												return tuple(_A		)







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] ,			_A      :     bool = False ,			_A      :     bool = None ,			_A      :     Optional[List[str]] = None ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     super().decode(
												    token_ids=_A ,			skip_special_tokens=_A ,			clean_up_tokenization_spaces=_A ,			**_A ,			)

												if truncate_before_pattern is not None and len(_A		) > 0:
																	_UpperCamelCase			     =     self.truncate(_A ,			_A		)

												return decoded_text







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     str ,			_A      :     Dict		):
												def find_re(_A      :     Optional[Any] ,			_A      :     Optional[int] ,			_A      :     Tuple		):
																	_UpperCamelCase			     =     pattern.search(_A ,			_A		)
																	return m.start() if m else -1

												_UpperCamelCase			     =     [re.compile(_A ,			re.MULTILINE		) for pattern in truncate_before_pattern]

												_UpperCamelCase			     =     list(re.finditer('''^print''' ,			_A ,			re.MULTILINE		)		)

												if len(_A		) > 1:
																	_UpperCamelCase			     =     completion[: prints[1].start()]

												_UpperCamelCase			     =     list(re.finditer('''^def''' ,			_A ,			re.MULTILINE		)		)

												if len(_A		) > 1:
																	_UpperCamelCase			     =     completion[: defs[1].start()]

												_UpperCamelCase			     =     0

												_UpperCamelCase			     =     [
												    pos for pos in [find_re(_A ,			_A ,			_A		) for terminal in terminals] if pos != -1
												]

												if len(_A		) > 0:
																	return completion[: min(_A		)]
												else:
																	return completion



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


_UpperCAmelCase						:  List[Any]									=			{
    """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						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

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

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

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			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						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




