

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''laion/clap-htsat-unfused'''
       __lowerCAmelCase					:						int					=					tempfile.mkdtemp()
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Optional[Any]:



       '''simple docstring'''





       return RobertaTokenizer.from_pretrained(self.checkpoint      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->List[str]:



       '''simple docstring'''





       return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.get_tokenizer()
       __lowerCAmelCase					:						Any					=					self.get_feature_extractor()

       __lowerCAmelCase					:						Optional[Any]					=					ClapProcessor(tokenizer=A_      ,							feature_extractor=A_					)

       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Any					=					ClapProcessor.from_pretrained(self.tmpdirname					)

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

       self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor.to_json_string()					)
       self.assertIsInstance(processor.feature_extractor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					ClapProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						str					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						Any					=					self.get_feature_extractor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						Dict					=					ClapProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

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

       self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.feature_extractor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					self.get_feature_extractor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						Dict					=					ClapProcessor(tokenizer=A_      ,							feature_extractor=A_					)

       __lowerCAmelCase					:						str					=					floats_list((3, 1000)					)

       __lowerCAmelCase					:						List[Any]					=					feature_extractor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						int					=					processor(audios=A_      ,							return_tensors='''np'''					)

       for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1e-2					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					self.get_feature_extractor()
       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					ClapProcessor(tokenizer=A_      ,							feature_extractor=A_					)

       __lowerCAmelCase					:						Any					=					'''This is a test string'''

       __lowerCAmelCase					:						Optional[int]					=					processor(text=A_					)

       __lowerCAmelCase					:						Tuple					=					tokenizer(A_					)

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.get_feature_extractor()
       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()

       __lowerCAmelCase					:						Union[str, Any]					=					ClapProcessor(tokenizer=A_      ,							feature_extractor=A_					)

       __lowerCAmelCase					:						Tuple					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCAmelCase					:						Any					=					processor.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					tokenizer.batch_decode(A_					)

       self.assertListEqual(A_      ,							A_					)





 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_feature_extractor()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Union[str, Any]					=					ClapProcessor(tokenizer=A_      ,							feature_extractor=A_					)

       self.assertListEqual(
           processor.model_input_names[2:]      ,							feature_extractor.model_input_names      ,							msg='''`processor` and `feature_extractor` model input names do not match'''      ,							)





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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


