

"""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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							List[str]  ,lowercase_     :							Union[str, Any]=1_3  ,lowercase_     :							Dict=7  ,lowercase_     :							Any=6  ,lowercase_     :							Union[str, Any]=1_7  ,lowercase_     :							Optional[Any]=2_3  ,lowercase_     :							Tuple=1_1  ,lowercase_     :							str=True  ,):
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Union[str, Any]    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      seq_length
						lowerCAmelCase__      :		Optional[int]    							=      act_dim
						lowerCAmelCase__      :		List[Any]    							=      state_dim
						lowerCAmelCase__      :		Optional[Any]    							=      hidden_size
						lowerCAmelCase__      :		int    							=      max_length
						lowerCAmelCase__      :		List[Any]    							=      is_training







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		str    							=      floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
						lowerCAmelCase__      :		List[str]    							=      floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
						lowerCAmelCase__      :		Union[str, Any]    							=      floats_tensor((self.batch_size, self.seq_length, 1)						)
						lowerCAmelCase__      :		Optional[Any]    							=      floats_tensor((self.batch_size, self.seq_length, 1)						)
						lowerCAmelCase__      :		str    							=      ids_tensor((self.batch_size, self.seq_length)  ,vocab_size=1_0_0_0						)
						lowerCAmelCase__      :		Optional[Any]    							=      random_attention_mask((self.batch_size, self.seq_length)						)

						lowerCAmelCase__      :		Optional[int]    							=      self.get_config()

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







					def 			__lowerCAmelCase					(			self     :							Union[str, 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 			__lowerCAmelCase					(			self     :							Tuple  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							Tuple  ,lowercase_     :							List[Any]  ,lowercase_     :							Dict  ,lowercase_     :							str  ,lowercase_     :							List[Any]  ,lowercase_     :							Union[str, Any]  ,):
						lowerCAmelCase__      :		Union[str, Any]    							=      DecisionTransformerModel(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Union[str, Any]    							=      model(lowercase_  ,lowercase_  ,lowercase_  ,lowercase_  ,lowercase_  ,lowercase_						)

						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 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		str    							=      self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,
						)      :		Dict    							=      config_and_inputs
						lowerCAmelCase__      :		Tuple    							=      {
						    '''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 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(DecisionTransformerModel,) if is_torch_available() else ()
					lowercase__												=			()
					lowercase__												=			{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		str    							=      DecisionTransformerModelTester(self						)
						lowerCAmelCase__      :		List[str]    							=      ConfigTester(self  ,config_class=lowercase_  ,hidden_size=3_7						)







					def 			__lowerCAmelCase					(			self     :							str						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_						)







					@slow
					def 			__lowerCAmelCase					(			self     :							List[str]						):
						for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__      :		str    							=      DecisionTransformerModel.from_pretrained(lowercase_						)
							self.assertIsNotNone(lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase__      :		Union[str, Any]    							=      [
							    '''states''',
							    '''actions''',
							    '''rewards''',
							    '''returns_to_go''',
							    '''timesteps''',
							    '''attention_mask''',
							]

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







@require_torch
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@slow
					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		str    							=      2  # number of steps of autoregressive prediction we will perform
						lowerCAmelCase__      :		Optional[int]    							=      1_0  # defined by the RL environment, may be normalized
						lowerCAmelCase__      :		List[Any]    							=      DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''						)
						lowerCAmelCase__      :		int    							=      model.to(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      model.config
						torch.manual_seed(0						)
						lowerCAmelCase__      :		Optional[int]    							=      torch.randn(1  ,1  ,config.state_dim						).to(device=lowercase_  ,dtype=torch.floataa						)  # env.reset()

						lowerCAmelCase__      :		Optional[int]    							=      torch.tensor(
						    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]  ,device=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      torch.tensor(lowercase_  ,device=lowercase_  ,dtype=torch.floataa						).reshape(1  ,1  ,1						)
						lowerCAmelCase__      :		str    							=      state
						lowerCAmelCase__      :		List[str]    							=      torch.zeros(1  ,0  ,config.act_dim  ,device=lowercase_  ,dtype=torch.floataa						)
						lowerCAmelCase__      :		str    							=      torch.zeros(1  ,0  ,device=lowercase_  ,dtype=torch.floataa						)
						lowerCAmelCase__      :		Dict    							=      torch.tensor(0  ,device=lowercase_  ,dtype=torch.long						).reshape(1  ,1						)

						for step in range(lowercase_						):
							lowerCAmelCase__      :		Any    							=      torch.cat([actions, torch.zeros(1  ,1  ,config.act_dim  ,device=lowercase_						)]  ,dim=1						)
							lowerCAmelCase__      :		Union[str, Any]    							=      torch.cat([rewards, torch.zeros(1  ,1  ,device=lowercase_						)]  ,dim=1						)

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

							with torch.no_grad():
								lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		List[str]    							=      model(
								    states=lowercase_  ,actions=lowercase_  ,rewards=lowercase_  ,returns_to_go=lowercase_  ,timesteps=lowercase_  ,attention_mask=lowercase_  ,return_dict=lowercase_  ,)

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

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







"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,remove_space=lowercase_  ,keep_accents=lowercase_  ,bos_token=lowercase_  ,eos_token=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		List[Any]    							=      do_lower_case
						lowerCAmelCase__      :		str    							=      remove_space
						lowerCAmelCase__      :		Optional[int]    							=      keep_accents
						lowerCAmelCase__      :		Dict    							=      vocab_file
						lowerCAmelCase__      :		Optional[int]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [self.cls_token_id]

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







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''						)

						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						):
							copyfile(self.vocab_file  ,lowercase_						)

						return (out_vocab_file,)




