
'''simple docstring'''





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import DecisionTransformerModel
						from transformers.models.decision_transformer.modeling_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)

class 						a   :




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[Any]=13,    SCREAMING_SNAKE_CASE_	:				int=7,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=6,    SCREAMING_SNAKE_CASE_	:				Optional[int]=17,    SCREAMING_SNAKE_CASE_	:				int=23,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=11,    SCREAMING_SNAKE_CASE_	:				Dict=True,    ):
										snake_case       :     List[Any] =    parent
										snake_case       :     List[str] =    batch_size
										snake_case       :     Optional[Any] =    seq_length
										snake_case       :     Optional[int] =    act_dim
										snake_case       :     int =    state_dim
										snake_case       :     Any =    hidden_size
										snake_case       :     Optional[Any] =    max_length
										snake_case       :     Optional[int] =    is_training




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Optional[Any] =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
										snake_case       :     List[Any] =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
										snake_case       :     Optional[int] =    floats_tensor((self.batch_size, self.seq_length, 1)      )
										snake_case       :     Tuple =    floats_tensor((self.batch_size, self.seq_length, 1)      )
										snake_case       :     str =    ids_tensor((self.batch_size, self.seq_length),    vocab_size=10_00      )
										snake_case       :     Tuple =    random_attention_mask((self.batch_size, self.seq_length)      )

										snake_case       :     Optional[Any] =    self.get_config()

										return (
										    config,
										    states,
										    actions,
										    rewards,
										    returns_to_go,
										    timesteps,
										    attention_mask,
										)




				def 	__snake_case							(							self	:				Any      ):
										return DecisionTransformerConfig(
										    batch_size=self.batch_size,    seq_length=self.seq_length,    act_dim=self.act_dim,    state_dim=self.state_dim,    hidden_size=self.hidden_size,    max_length=self.max_length,    )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str,    ):
										snake_case       :     List[Any] =    DecisionTransformerModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										self.parent.assertEqual(result.state_preds.shape,    states.shape      )
										self.parent.assertEqual(result.action_preds.shape,    actions.shape      )
										self.parent.assertEqual(result.return_preds.shape,    returns_to_go.shape      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape,    (self.batch_size, self.seq_length * 3, self.hidden_size)      )  # seq length *3 as there are 3 modelities: states, returns and actions




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Dict =    self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										),					(
										    snake_case
										),					(
										    snake_case
										),					(
										    snake_case
										),					(
										    snake_case
										),					(
										    snake_case
										),					(
										    snake_case
										),					
										)       :     Optional[Any] =    config_and_inputs
										snake_case       :     Optional[Any] =    {
										    '''states''': states,
										    '''actions''': actions,
										    '''rewards''': rewards,
										    '''returns_to_go''': returns_to_go,
										    '''timesteps''': timesteps,
										    '''attention_mask''': attention_mask,
										}
										return config, inputs_dict




@require_torch
class 						a   (				__magic_name__				,__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (DecisionTransformerModel,) if is_torch_available() else ()
				_snake_case		    = ()
				_snake_case		    = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

				# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
				_snake_case		    = False

				# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[Any] =    DecisionTransformerModelTester(self      )
										snake_case       :     Tuple =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_,    hidden_size=37      )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										self.config_tester.run_common_tests()




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     int =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




				@slow
				def 	__snake_case							(							self	:				Optional[Any]      ):
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case       :     Union[str, Any] =    DecisionTransformerModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										snake_case,					snake_case       :     Optional[int] =    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case       :     List[Any] =    model_class(SCREAMING_SNAKE_CASE_      )
																snake_case       :     Any =    inspect.signature(model.forward      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case       :     List[Any] =    [*signature.parameters.keys()]

																snake_case       :     Optional[int] =    [
																    '''states''',
																    '''actions''',
																    '''rewards''',
																    '''returns_to_go''',
																    '''timesteps''',
																    '''attention_mask''',
																]

																self.assertListEqual(arg_names[: len(SCREAMING_SNAKE_CASE_      )],    SCREAMING_SNAKE_CASE_      )




@require_torch
class 						a   (				unittest.TestCase    ):




				@slow
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     List[str] =    2  # number of steps of autoregressive prediction we will perform
										snake_case       :     str =    10  # defined by the RL environment, may be normalized
										snake_case       :     List[str] =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''      )
										snake_case       :     int =    model.to(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    model.config
										torch.manual_seed(0      )
										snake_case       :     List[Any] =    torch.randn(1,    1,    config.state_dim      ).to(device=SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      )  # env.reset()

										snake_case       :     Optional[Any] =    torch.tensor(
										    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]],    device=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    torch.tensor(SCREAMING_SNAKE_CASE_,    device=SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      ).reshape(1,    1,    1      )
										snake_case       :     Any =    state
										snake_case       :     Tuple =    torch.zeros(1,    0,    config.act_dim,    device=SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      )
										snake_case       :     Any =    torch.zeros(1,    0,    device=SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      )
										snake_case       :     List[str] =    torch.tensor(0,    device=SCREAMING_SNAKE_CASE_,    dtype=torch.long      ).reshape(1,    1      )

										for step in range(SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Tuple =    torch.cat([actions, torch.zeros(1,    1,    config.act_dim,    device=SCREAMING_SNAKE_CASE_      )],    dim=1      )
																snake_case       :     List[Any] =    torch.cat([rewards, torch.zeros(1,    1,    device=SCREAMING_SNAKE_CASE_      )],    dim=1      )

																snake_case       :     str =    torch.ones(1,    states.shape[1]      ).to(dtype=torch.long,    device=states.device      )

																with torch.no_grad():
																						snake_case,					snake_case,					snake_case       :     int =    model(
																						    states=SCREAMING_SNAKE_CASE_,    actions=SCREAMING_SNAKE_CASE_,    rewards=SCREAMING_SNAKE_CASE_,    returns_to_go=SCREAMING_SNAKE_CASE_,    timesteps=SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    return_dict=SCREAMING_SNAKE_CASE_,    )

																self.assertEqual(action_pred.shape,    actions.shape      )
																self.assertTrue(torch.allclose(action_pred[0, -1],    expected_outputs[step],    atol=1e-4      )      )
																snake_case,					snake_case,					snake_case,					snake_case       :     Any =    (  # env.step(action)
																    torch.randn(1,    1,    config.state_dim      ).to(device=SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      ),
																    1.0,
																    False,
																    {},
																)

																snake_case       :     str =    action_pred[0, -1]
																snake_case       :     int =    torch.cat([states, state],    dim=1      )
																snake_case       :     Union[str, Any] =    returns_to_go[0, -1] - reward
																snake_case       :     Optional[Any] =    torch.cat([returns_to_go, pred_return.reshape(1,    1,    1      )],    dim=1      )
																snake_case       :     Any =    torch.cat(
																    [timesteps, torch.ones((1, 1),    device=SCREAMING_SNAKE_CASE_,    dtype=torch.long      ) * (step + 1)],    dim=1      )



'''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


UpperCAmelCase    					=   logging.get_logger(__name__)

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

UpperCAmelCase    					=   {
    "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"
        ),
    },
}

UpperCAmelCase    					=   {
    "Salesforce/codegen-350M-mono": 2_048,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = CodeGenTokenizer




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

										if kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Dict =    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.'''      )

										snake_case       :     int =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     Optional[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Union[str, Any] =    add_prefix_space
																snake_case       :     Optional[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     int =    add_prefix_space




				def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     Optional[int] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				bool = None,    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										snake_case       :     Dict =    super().decode(
										    token_ids=SCREAMING_SNAKE_CASE_,    skip_special_tokens=SCREAMING_SNAKE_CASE_,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if truncate_before_pattern is not None and len(SCREAMING_SNAKE_CASE_      ) > 0:
																snake_case       :     Optional[int] =    self.truncate(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return decoded_text




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										def find_re(SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     Optional[Any] =    pattern.search(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																return m.start() if m else -1

										snake_case       :     Union[str, Any] =    [re.compile(SCREAMING_SNAKE_CASE_,    re.MULTILINE      ) for pattern in truncate_before_pattern]

										snake_case       :     Union[str, Any] =    list(re.finditer('''^print''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Tuple =    completion[: prints[1].start()]

										snake_case       :     List[str] =    list(re.finditer('''^def''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Optional[Any] =    completion[: defs[1].start()]

										snake_case       :     Tuple =    0

										snake_case       :     List[Any] =    [
										    pos for pos in [find_re(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for terminal in terminals] if pos != -1
										]

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

