import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		"""▁"""

_UpperCAmelCase            =		{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_UpperCAmelCase            =		{
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
_UpperCAmelCase            =		{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']

				lowerCamelCase_    = []
				lowerCamelCase_    = []
				def __init__(						self		,     lowercase		,     lowercase		,     lowercase=None		,     lowercase=None		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<pad>"		,     lowercase="<unk>"		,     lowercase="m2m100"		,     lowercase = None		,     lowercase=8		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Tuple         =				{} if sp_model_kwargs is None else sp_model_kwargs

						A_		:    List[Any]         =				language_codes
						A_		:    int         =				FAIRSEQ_LANGUAGE_CODES[language_codes]
						A_		:    Union[str, Any]         =				{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

						A_		:    str         =				kwargs.get('additional_special_tokens'		,     [] )
						kwargs["additional_special_tokens"] += [
						    self.get_lang_token(lowercase )
						    for lang_code in fairseq_language_code
						    if self.get_lang_token(lowercase ) not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    src_lang=lowercase		,     tgt_lang=lowercase		,     bos_token=lowercase		,     eos_token=lowercase		,     sep_token=lowercase		,     unk_token=lowercase		,     pad_token=lowercase		,     language_codes=lowercase		,     sp_model_kwargs=self.sp_model_kwargs		,     num_madeup_words=lowercase		,     **lowercase		,     )

						A_		:    str         =				vocab_file
						A_		:    Tuple         =				load_json(lowercase )
						A_		:    Dict         =				{v: k for k, v in self.encoder.items()}
						A_		:    Tuple         =				spm_file
						A_		:    Optional[int]         =				load_spm(lowercase		,     self.sp_model_kwargs )

						A_		:    Union[str, Any]         =				len(self.encoder )

						A_		:    List[str]         =				{
						    self.get_lang_token(lowercase ): self.encoder_size + i for i, lang_code in enumerate(lowercase )
						}
						A_		:    Dict         =				{lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase )}
						A_		:    List[Any]         =				{v: k for k, v in self.lang_token_to_id.items()}

						A_		:    Any         =				src_lang if src_lang is not None else 'en'
						A_		:    Any         =				tgt_lang
						A_		:    Tuple         =				self.get_lang_id(self._src_lang )
						self.set_src_lang_special_tokens(self._src_lang )

						A_		:    int         =				num_madeup_words
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.encoder ) + len(self.lang_token_to_id )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self._src_lang
				@src_lang.setter
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				new_src_lang
						self.set_src_lang_special_tokens(self._src_lang )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.sp_model.encode(lowercase		,     out_type=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.lang_token_to_id:
								return self.lang_token_to_id[token]
						return self.encoder.get(lowercase		,     self.encoder[self.unk_token] )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if index in self.id_to_lang_token:
								return self.id_to_lang_token[index]
						return self.decoder.get(lowercase		,     self.unk_token )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						A_		:    Any         =				''
						for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
										out_string += self.sp_model.decode(lowercase ) + token
										A_		:    str         =				[]
								else:
										current_sub_tokens.append(lowercase )
						out_string += self.sp_model.decode(lowercase )
						return out_string.strip()
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						A_		:    List[Any]         =				[1] * len(self.prefix_tokens )
						A_		:    Optional[int]         =				[1] * len(self.suffix_tokens )
						if token_ids_a is None:
								return prefix_ones + ([0] * len(lowercase )) + suffix_ones
						return prefix_ones + ([0] * len(lowercase )) + ([0] * len(lowercase )) + suffix_ones
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
						vocab.update(self.added_tokens_encoder )
						return vocab
				def __getstate__(						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.__dict__.copy()
						A_		:    List[str]         =				None
						return state
				def __setstate__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				d

						# for backward compatibility
						if not hasattr(self		,     'sp_model_kwargs' ):
								A_		:    List[Any]         =				{}

						A_		:    List[str]         =				load_spm(self.spm_file		,     self.sp_model_kwargs )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				Path(lowercase )
						if not save_dir.is_dir():
								raise OSError(F'''{save_directory} should be a directory''' )
						A_		:    str         =				save_dir / (
						    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
						)
						A_		:    Union[str, Any]         =				save_dir / (
						    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
						)

						save_json(self.encoder		,     lowercase )

						if os.path.abspath(self.spm_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.spm_file ):
								copyfile(self.spm_file		,     lowercase )
						elif not os.path.isfile(self.spm_file ):
								with open(lowercase		,     'wb' ) as fi:
										A_		:    Union[str, Any]         =				self.sp_model.serialized_model_proto()
										fi.write(lowercase )

						return (str(lowercase ), str(lowercase ))
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = "en"		,     lowercase = None		,     lowercase = "ro"		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Tuple         =				src_lang
						A_		:    Union[str, Any]         =				tgt_lang
						self.set_src_lang_special_tokens(self.src_lang )
						return super().prepare_seqaseq_batch(lowercase		,     lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						if src_lang is None or tgt_lang is None:
								raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
						A_		:    Optional[Any]         =				src_lang
						A_		:    List[str]         =				self(lowercase		,     add_special_tokens=lowercase		,     **lowercase )
						A_		:    Tuple         =				self.get_lang_id(lowercase )
						A_		:    Dict         =				tgt_lang_id
						return inputs
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						self.set_src_lang_special_tokens(self.src_lang )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						self.set_tgt_lang_special_tokens(self.tgt_lang )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[str]         =				self.get_lang_token(lowercase )
						A_		:    Optional[Any]         =				self.lang_token_to_id[lang_token]
						A_		:    List[Any]         =				[self.cur_lang_id]
						A_		:    Union[str, Any]         =				[self.eos_token_id]
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.get_lang_token(lowercase )
						A_		:    str         =				self.lang_token_to_id[lang_token]
						A_		:    Union[str, Any]         =				[self.cur_lang_id]
						A_		:    Optional[int]         =				[self.eos_token_id]
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.lang_code_to_token[lang]







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.get_lang_token(lowercase )
						return self.lang_token_to_id[lang_token]




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




		A_		:    int         =				sentencepiece.SentencePieceProcessor(**__lowercase     )
		spm.Load(str(__lowercase     )     )
		return spm




def     UpperCamelCase						(	__lowercase						:			str     ):
		'''simple docstring'''




		with open(__lowercase						,'r'     ) as f:
				return json.load(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			str     ):
		'''simple docstring'''




		with open(__lowercase						,'w'     ) as f:
				json.dump(__lowercase						,__lowercase						,indent=2     )







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}""")






