

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

__A   :     str      =			logging.get_logger(__name__)

__A   :     int      =			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__A   :     Optional[Any]      =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

__A   :     Any      =			{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}
@lru_cache()
def 		SCREAMING_SNAKE_CASE__				(							)					-> Dict:



    '''simple docstring'''






    lowerCAmelCase				:			Union[str, Any]    			=      (
        list(range(ord('!'				),      ord('~'				) + 1				)				) + list(range(ord('¡'				),      ord('¬'				) + 1				)				) + list(range(ord('®'				),      ord('ÿ'				) + 1				)				)
    )
    lowerCAmelCase				:			Optional[int]    			=      bs[:]
    lowerCAmelCase				:			Dict    			=      0
    for b in range(2**8				):
        if b not in bs:
            bs.append(_UpperCAmelCase				)
            cs.append(2**8 + n				)
            n += 1
    lowerCAmelCase				:			Dict    			=      [chr(_UpperCAmelCase				) for n in cs]
    return dict(zip(_UpperCAmelCase,      _UpperCAmelCase				)				)
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Any:



    '''simple docstring'''






    lowerCAmelCase				:			int    			=      set()
    lowerCAmelCase				:			Optional[int]    			=      word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)				)
        lowerCAmelCase				:			str    			=      char
    return pairs






class    __A				(							lowerCAmelCase__ ):
    lowerCAmelCase_							:		Optional[int]						=							VOCAB_FILES_NAMES
    lowerCAmelCase_							:		int						=							PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_							:		Optional[int]						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_							:		Any						=							["""input_ids""", """attention_mask"""]






    def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Any="replace"    ,				UpperCAmelCase_			:    List[Any]="<|endoftext|>"    ,				UpperCAmelCase_			:    int="<|endoftext|>"    ,				UpperCAmelCase_			:    Optional[int]="<|endoftext|>"    ,				UpperCAmelCase_			:    int=None    ,				UpperCAmelCase_			:    Union[str, Any]=False    ,				UpperCAmelCase_			:    Any=False    ,				**UpperCAmelCase_			:    Union[str, Any]    ,				):
        lowerCAmelCase				:			Dict    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else bos_token
        lowerCAmelCase				:			Optional[Any]    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else eos_token
        lowerCAmelCase				:			int    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else unk_token
        lowerCAmelCase				:			List[Any]    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else pad_token
        super().__init__(
            errors=lowerCAmelCase_    ,				unk_token=lowerCAmelCase_    ,				bos_token=lowerCAmelCase_    ,				eos_token=lowerCAmelCase_    ,				pad_token=lowerCAmelCase_    ,				add_prefix_space=lowerCAmelCase_    ,				add_bos_token=lowerCAmelCase_    ,				**lowerCAmelCase_    ,				)
        lowerCAmelCase				:			Tuple    			=      add_bos_token

        with open(lowerCAmelCase_    ,				encoding='utf-8'    ) as vocab_handle:
            lowerCAmelCase				:			List[str]    			=      json.load(lowerCAmelCase_    )
        lowerCAmelCase				:			Dict    			=      {v: k for k, v in self.encoder.items()}
        lowerCAmelCase				:			Any    			=      errors  # how to handle errors in decoding
        lowerCAmelCase				:			List[str]    			=      bytes_to_unicode()
        lowerCAmelCase				:			str    			=      {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCAmelCase_    ,				encoding='utf-8'    ) as merges_handle:
            lowerCAmelCase				:			List[Any]    			=      merges_handle.read().split('\n'    )[1:-1]
        lowerCAmelCase				:			Optional[int]    			=      [tuple(merge.split()    ) for merge in bpe_merges]
        lowerCAmelCase				:			Optional[int]    			=      dict(zip(lowerCAmelCase_    ,				range(len(lowerCAmelCase_    )    )    )    )
        lowerCAmelCase				:			Tuple    			=      {}
        lowerCAmelCase				:			str    			=      add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        lowerCAmelCase				:			List[str]    			=      re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )






    @property
    def 	lowercase__ (	self			:    Tuple    ):
        return len(self.encoder    )






    def 	lowercase__ (	self			:    str    ):
        return dict(self.encoder    ,				**self.added_tokens_encoder    )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ):






        if token in self.cache:
            return self.cache[token]
        lowerCAmelCase				:			str    			=      tuple(lowerCAmelCase_    )
        lowerCAmelCase				:			str    			=      get_pairs(lowerCAmelCase_    )

        if not pairs:
            return token

        while True:
            lowerCAmelCase				:			Any    			=      min(lowerCAmelCase_    ,				key=lambda UpperCAmelCase_    : self.bpe_ranks.get(lowerCAmelCase_    ,				float('inf'    )    )    )
            if bigram not in self.bpe_ranks:
                break
            lowerCAmelCase			,  lowerCAmelCase				:			int    			=      bigram
            lowerCAmelCase				:			int    			=      []
            lowerCAmelCase				:			Optional[Any]    			=      0
            while i < len(lowerCAmelCase_    ):
                try:
                    lowerCAmelCase				:			Union[str, Any]    			=      word.index(lowerCAmelCase_    ,				lowerCAmelCase_    )
                except ValueError:
                    new_word.extend(word[i:]    )
                    break
                else:
                    new_word.extend(word[i:j]    )
                    lowerCAmelCase				:			Optional[int]    			=      j

                if word[i] == first and i < len(lowerCAmelCase_    ) - 1 and word[i + 1] == second:
                    new_word.append(first + second    )
                    i += 2
                else:
                    new_word.append(word[i]    )
                    i += 1
            lowerCAmelCase				:			List[Any]    			=      tuple(lowerCAmelCase_    )
            lowerCAmelCase				:			List[Any]    			=      new_word
            if len(lowerCAmelCase_    ) == 1:
                break
            else:
                lowerCAmelCase				:			int    			=      get_pairs(lowerCAmelCase_    )
        lowerCAmelCase				:			str    			=      ' '.join(lowerCAmelCase_    )
        lowerCAmelCase				:			str    			=      word
        return word






    def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict=None    ):






        if self.add_bos_token:
            lowerCAmelCase				:			List[str]    			=      [self.bos_token_id]
        else:
            lowerCAmelCase				:			int    			=      []

        lowerCAmelCase				:			List[str]    			=      bos_token_ids + token_ids_a

        if token_ids_a is None:
            return output

        return output + bos_token_ids + token_ids_a






    def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    List[int]    ,				UpperCAmelCase_			:    Optional[List[int]] = None    ,				UpperCAmelCase_			:    bool = False    ):






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

        if not self.add_bos_token:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCAmelCase_    ,				token_ids_a=lowerCAmelCase_    ,				already_has_special_tokens=lowerCAmelCase_    )

        if token_ids_a is None:
            return [1] + ([0] * len(lowerCAmelCase_    ))
        return [1] + ([0] * len(lowerCAmelCase_    )) + [1] + ([0] * len(lowerCAmelCase_    ))






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ):
        lowerCAmelCase				:			Tuple    			=      []
        for token in re.findall(self.pat    ,				lowerCAmelCase_    ):
            lowerCAmelCase				:			Tuple    			=      ''.join(
                self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_    ).split(' '    )    )
        return bpe_tokens






    def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ):
        return self.encoder.get(lowerCAmelCase_    ,				self.encoder.get(self.unk_token    )    )






    def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Optional[Any]    ):
        return self.decoder.get(lowerCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Dict    ):
        lowerCAmelCase				:			str    			=      ''.join(lowerCAmelCase_    )
        lowerCAmelCase				:			Any    			=      bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'    ,				errors=self.errors    )
        return text






    def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[str] = None    ):






        if not os.path.isdir(lowerCAmelCase_    ):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
            return
        lowerCAmelCase				:			List[Any]    			=      os.path.join(
            lowerCAmelCase_    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
        lowerCAmelCase				:			Optional[int]    			=      os.path.join(
            lowerCAmelCase_    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

        with open(lowerCAmelCase_    ,				'w'    ,				encoding='utf-8'    ) as f:
            f.write(json.dumps(self.encoder    ,				indent=2    ,				sort_keys=lowerCAmelCase_    ,				ensure_ascii=lowerCAmelCase_    ) + '\n'    )

        lowerCAmelCase				:			List[Any]    			=      0
        with open(lowerCAmelCase_    ,				'w'    ,				encoding='utf-8'    ) as writer:
            writer.write('#version: 0.2\n'    )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,				key=lambda UpperCAmelCase_    : kv[1]    ):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                        ' Please check that the tokenizer is not corrupted!'    )
                    lowerCAmelCase				:			Optional[int]    			=      token_index
                writer.write(' '.join(lowerCAmelCase_    ) + '\n'    )
                index += 1

        return vocab_file, merge_file






    def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    str=False    ,				**UpperCAmelCase_			:    Optional[int]    ):
        lowerCAmelCase				:			Union[str, Any]    			=      kwargs.pop('add_prefix_space'    ,				self.add_prefix_space    )
        if is_split_into_words or add_prefix_space:
            lowerCAmelCase				:			Optional[Any]    			=      ' ' + text
        return (text, kwargs)






    def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    "Conversation"    ):
        lowerCAmelCase				:			Any    			=      []
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(lowerCAmelCase_    ,				add_special_tokens=lowerCAmelCase_    ) + [self.eos_token_id]    )
        if len(lowerCAmelCase_    ) > self.model_max_length:
            lowerCAmelCase				:			Dict    			=      input_ids[-self.model_max_length :]
        return input_ids








import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:



    '''simple docstring'''






    lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
    lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
    lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
    lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)

    if model.config.max_length is not None:
        lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
        lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
        lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
        lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        lowerCAmelCase				:			List[str]    			=      torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
        lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
        lowerCAmelCase				:			Any    			=      torch.cat(
            [
                torch.zeros(
                    (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),      device=states.device,      ),
                states,
            ],      dim=1,      ).to(dtype=torch.floataa				)
        lowerCAmelCase				:			int    			=      torch.cat(
            [
                torch.zeros(
                    (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),      device=actions.device,      ),
                actions,
            ],      dim=1,      ).to(dtype=torch.floataa				)
        lowerCAmelCase				:			Union[str, Any]    			=      torch.cat(
            [
                torch.zeros(
                    (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),      device=returns_to_go.device,      ),
                returns_to_go,
            ],      dim=1,      ).to(dtype=torch.floataa				)
        lowerCAmelCase				:			Tuple    			=      torch.cat(
            [
                torch.zeros(
                    (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),      device=timesteps.device				),
                timesteps,
            ],      dim=1,      ).to(dtype=torch.long				)
    else:
        lowerCAmelCase				:			Optional[Any]    			=      None

    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
        states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )

    return action_preds[0, -1]


# build the environment

__A   :     Dict      =			gym.make('''Hopper-v3''')
__A   :     Any      =			env.observation_space.shape[0]
__A   :     Optional[Any]      =			env.action_space.shape[0]
__A   :     Any      =			1000
__A   :     Tuple      =			'''cuda'''
__A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
__A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__A   :     str      =			np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
__A   :     Dict      =			np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
__A   :     int      =			torch.from_numpy(state_mean).to(device=device)
__A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
__A   :     Any      =			model.to(device)
model.eval()

for ep in range(10):
 __A     ,      __A   :     Optional[int]      =			0, 0
 __A   :     List[str]      =			env.reset()
 __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)

 __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 for t in range(max_ep_len):
  env.render()
  # add padding
  __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])

  __A   :     int      =			get_action(
      model,
      (states.to(dtype=torch.floataa) - state_mean) / state_std,
      actions.to(dtype=torch.floataa),
      rewards.to(dtype=torch.floataa),
      target_return.to(dtype=torch.floataa),
      timesteps.to(dtype=torch.long),
  )
  __A   :     List[str]      =			action
  __A   :     List[str]      =			action.detach().cpu().numpy()

  __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)

  __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __A   :     Any      =			torch.cat([states, cur_state], dim=0)
  __A   :     Dict      =			reward

  __A   :     str      =			target_return[0, -1] - (reward / scale)
  __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __A   :     List[Any]      =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

  episode_return += reward
  episode_length += 1

  if done:
   break





