"""simple docstring"""
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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__					       =	{
    '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'
        ),
    },
}

lowercase__					       =	{
    'Salesforce/codegen-350M-mono': 2048,
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		PRETRAINED_VOCAB_FILES_MAP
      a__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		["""input_ids""", """attention_mask"""]
      a__        =		CodeGenTokenizer



      def __init__(						self    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase="<|endoftext|>"    ,			lowercase="<|endoftext|>"    ,			lowercase="<|endoftext|>"    ,			lowercase=False    ,			**lowercase    ,			)	->			Optional[Any]:





        '''simple docstring'''





        super().__init__(
            lowercase    ,			lowercase    ,			tokenizer_file=lowercase    ,			unk_token=lowercase    ,			bos_token=lowercase    ,			eos_token=lowercase    ,			add_prefix_space=lowercase    ,			**lowercase    ,			)

        if kwargs.pop('add_bos_token'    ,			lowercase):
          a__:     List[str]										=	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.')

        a__:     Any										=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('add_prefix_space'    ,			lowercase) != add_prefix_space:
          a__:     str										=	getattr(lowercase    ,			pre_tok_state.pop('type'))
          a__:     str										=	add_prefix_space
          a__:     List[str]										=	pre_tok_class(**lowercase)

        a__:     Dict										=	add_prefix_space



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			BatchEncoding:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.get('is_split_into_words'    ,			lowercase)
        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(*lowercase    ,			**lowercase)



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			BatchEncoding:





        '''simple docstring'''





        a__:     Dict										=	kwargs.get('is_split_into_words'    ,			lowercase)

        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(*lowercase    ,			**lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			Tuple[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	self._tokenizer.model.save(lowercase    ,			name=lowercase)
        return tuple(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = False    ,			lowercase = None    ,			lowercase = None    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        a__:     int										=	super().decode(
            token_ids=lowercase    ,			skip_special_tokens=lowercase    ,			clean_up_tokenization_spaces=lowercase    ,			**lowercase    ,			)

        if truncate_before_pattern is not None and len(lowercase) > 0:
          a__:     str										=	self.truncate(lowercase    ,			lowercase)

        return decoded_text



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        def find_re(lowercase    ,			lowercase    ,			lowercase):
          a__:     Tuple										=	pattern.search(lowercase    ,			lowercase)
          return m.start() if m else -1

        a__:     List[str]										=	[re.compile(lowercase    ,			re.MULTILINE) for pattern in truncate_before_pattern]

        a__:     str										=	list(re.finditer('^print'    ,			lowercase    ,			re.MULTILINE))

        if len(lowercase) > 1:
          a__:     int										=	completion[: prints[1].start()]

        a__:     Dict										=	list(re.finditer('^def'    ,			lowercase    ,			re.MULTILINE))

        if len(lowercase) > 1:
          a__:     str										=	completion[: defs[1].start()]

        a__:     Optional[int]										=	0

        a__:     int										=	[
            pos for pos in [find_re(lowercase    ,			lowercase    ,			lowercase) for terminal in terminals] if pos != -1
        ]

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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


