





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={
    '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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'decision_transformer'
       __snake_case 			=    ['past_key_values']
       __snake_case 			=    {
           'max_position_embeddings': 'n_positions',
           'num_attention_heads': 'n_head',
           'num_hidden_layers': 'n_layer',
       }




       def __init__(      self    ,						__lowerCamelCase=1_7    ,						__lowerCamelCase=4    ,						__lowerCamelCase=1_2_8    ,						__lowerCamelCase=4_0_9_6    ,						__lowerCamelCase=True    ,						__lowerCamelCase=1    ,						__lowerCamelCase=1_0_2_4    ,						__lowerCamelCase=3    ,						__lowerCamelCase=1    ,						__lowerCamelCase=None    ,						__lowerCamelCase="relu"    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=1E-5    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						__lowerCamelCase=5_0_2_5_6    ,						__lowerCamelCase=5_0_2_5_6    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						**__lowerCamelCase    ,						)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							state_dim
            _SCREAMING_SNAKE_CASE       :	int						    =							act_dim
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							max_ep_len
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							action_tanh
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							n_positions
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							n_layer
            _SCREAMING_SNAKE_CASE       :	Dict						    =							n_head
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							n_inner
            _SCREAMING_SNAKE_CASE       :	str						    =							activation_function
            _SCREAMING_SNAKE_CASE       :	str						    =							resid_pdrop
            _SCREAMING_SNAKE_CASE       :	str						    =							embd_pdrop
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							attn_pdrop
            _SCREAMING_SNAKE_CASE       :	Any						    =							layer_norm_epsilon
            _SCREAMING_SNAKE_CASE       :	int						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							scale_attn_weights
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							use_cache
            _SCREAMING_SNAKE_CASE       :	Any						    =							scale_attn_by_inverse_layer_idx
            _SCREAMING_SNAKE_CASE       :	str						    =							reorder_and_upcast_attn

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							bos_token_id
            _SCREAMING_SNAKE_CASE       :	str						    =							eos_token_id

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






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab()							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)
            processor.save_pretrained(
                self.tmpdirname    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						speaker_embeddings_directory=self.speaker_embeddings_directory    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BarkProcessor.from_pretrained(
                self.tmpdirname    ,						self.speaker_embeddings_dict_path    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from npz file
            _SCREAMING_SNAKE_CASE       :	Any						    =							os.path.join(self.tmpdirname    ,						"file.npz"							)
            np.savez(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["history_prompt"]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key].squeeze().tolist()							)