"""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  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=7  ,__UpperCamelCase=6  ,__UpperCamelCase=17  ,__UpperCamelCase=23  ,__UpperCamelCase=11  ,__UpperCamelCase=True  ,)			->   Dict:


        '''simple docstring'''


        lowercase_			:       Dict									=			parent
        lowercase_			:       Union[str, Any]									=			batch_size
        lowercase_			:       Any									=			seq_length
        lowercase_			:       List[Any]									=			act_dim
        lowercase_			:       Dict									=			state_dim
        lowercase_			:       Union[str, Any]									=			hidden_size
        lowercase_			:       Any									=			max_length
        lowercase_			:       str									=			is_training
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
        lowercase_			:       Any									=			floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
        lowercase_			:       List[str]									=			floats_tensor((self.batch_size, self.seq_length, 1)    )
        lowercase_			:       List[Any]									=			floats_tensor((self.batch_size, self.seq_length, 1)    )
        lowercase_			:       Optional[int]									=			ids_tensor((self.batch_size, self.seq_length)  ,vocab_size=1000    )
        lowercase_			:       Union[str, Any]									=			random_attention_mask((self.batch_size, self.seq_length)    )

        lowercase_			:       Dict									=			self.get_config()

        return (
            config,
            states,
            actions,
            rewards,
            returns_to_go,
            timesteps,
            attention_mask,
        )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   Dict:


        '''simple docstring'''


        lowercase_			:       Dict									=			DecisionTransformerModel(config=__UpperCamelCase    )
        model.to(__UpperCamelCase    )
        model.eval()
        lowercase_			:       Any									=			model(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )

        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     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self.prepare_config_and_inputs()
        (
            lowercase_       
        )			:       Any									=			config_and_inputs
        lowercase_			:       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  UpperCamelCase	(    lowercase_		,   lowercase_		,   lowercase_		,   unittest.TestCase						):
   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     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			DecisionTransformerModelTester(self    )
        lowercase_			:       Optional[int]									=			ConfigTester(self  ,config_class=__UpperCamelCase  ,hidden_size=37    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_			:       str									=			DecisionTransformerModel.from_pretrained(__UpperCamelCase    )
             self.assertIsNotNone(__UpperCamelCase    )







   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       int									=			self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_			:       Any									=			[
                 'states',
                 'actions',
                 'rewards',
                 'returns_to_go',
                 'timesteps',
                 'attention_mask',
             ]

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







@require_torch
class  UpperCamelCase	(    unittest.TestCase						):
   @slow
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


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

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

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

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

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

             with torch.no_grad():
                  lowercase_			:       Tuple									=			model(
                      states=__UpperCamelCase  ,actions=__UpperCamelCase  ,rewards=__UpperCamelCase  ,returns_to_go=__UpperCamelCase  ,timesteps=__UpperCamelCase  ,attention_mask=__UpperCamelCase  ,return_dict=__UpperCamelCase  ,)

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

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







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import WhisperFeatureExtractor

if is_torch_available():
       import torch

__SCREAMING_SNAKE_CASE								=random.Random()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]=1.0  ,    __SCREAMING_SNAKE_CASE							:  Dict=None  ,    __SCREAMING_SNAKE_CASE							:  Tuple=None   ):
     if rng is None:
          lowercase_			:       Union[str, Any]									=			global_rng

     lowercase_			:       str									=			[]
     for batch_idx in range(shape[0]   ):
          values.append([]   )
          for _ in range(shape[1]   ):
               values[-1].append(rng.random() * scale   )

     return values







@require_torch
@require_torchaudio
class  UpperCamelCase	(    unittest.TestCase						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=7  ,__UpperCamelCase=400  ,__UpperCamelCase=2000  ,__UpperCamelCase=10  ,__UpperCamelCase=160  ,__UpperCamelCase=8  ,__UpperCamelCase=0.0  ,__UpperCamelCase=4000  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,)			->   List[str]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			parent
        lowercase_			:       Optional[Any]									=			batch_size
        lowercase_			:       Optional[int]									=			min_seq_length
        lowercase_			:       List[Any]									=			max_seq_length
        lowercase_			:       Tuple									=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase_			:       Tuple									=			padding_value
        lowercase_			:       Dict									=			sampling_rate
        lowercase_			:       List[str]									=			return_attention_mask
        lowercase_			:       str									=			do_normalize
        lowercase_			:       str									=			feature_size
        lowercase_			:       List[Any]									=			chunk_length
        lowercase_			:       Optional[int]									=			hop_length
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=False  ,__UpperCamelCase=False    )			->   Union[str, Any]:


        '''simple docstring'''


        def _flatten(__UpperCamelCase    ):
             return list(itertools.chain(*__UpperCamelCase    )    )

        if equal_length:
             lowercase_			:       Dict									=			[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
        else:
             # make sure that inputs increase in size
             lowercase_			:       int									=			[
                 floats_list((x, self.feature_size)    )
                 for x in range(self.min_seq_length  ,self.max_seq_length  ,self.seq_length_diff    )
             ]
        if numpify:
             lowercase_			:       int									=			[np.asarray(__UpperCamelCase    ) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						WhisperFeatureExtractor if is_speech_available() else None
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       int									=			WhisperFeatureExtractionTester(self    )
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Any									=			self.feature_extraction_class(**self.feat_extract_dict    )

        with tempfile.TemporaryDirectory() as tmpdirname:
             lowercase_			:       List[Any]									=			feat_extract_first.save_pretrained(__UpperCamelCase    )[0]
             check_json_file_has_correct_format(__UpperCamelCase    )
             lowercase_			:       Optional[Any]									=			self.feature_extraction_class.from_pretrained(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			feat_extract_first.to_dict()
        lowercase_			:       int									=			feat_extract_second.to_dict()
        lowercase_			:       List[Any]									=			feat_extract_first.mel_filters
        lowercase_			:       Tuple									=			feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase    )    )
        self.assertEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Any									=			self.feature_extraction_class(**self.feat_extract_dict    )

        with tempfile.TemporaryDirectory() as tmpdirname:
             lowercase_			:       List[Any]									=			os.path.join(__UpperCamelCase  ,'feat_extract.json'    )
             feat_extract_first.to_json_file(__UpperCamelCase    )
             lowercase_			:       Dict									=			self.feature_extraction_class.from_json_file(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			feat_extract_first.to_dict()
        lowercase_			:       Optional[Any]									=			feat_extract_second.to_dict()
        lowercase_			:       List[str]									=			feat_extract_first.mel_filters
        lowercase_			:       Tuple									=			feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase    )    )
        self.assertEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        # create three inputs of length 800, 1000, and 1200
        lowercase_			:       Union[str, Any]									=			[floats_list((1, x)    )[0] for x in range(800  ,1400  ,200    )]
        lowercase_			:       str									=			[np.asarray(__UpperCamelCase    ) for speech_input in speech_inputs]

        # Test feature size
        lowercase_			:       Tuple									=			feature_extractor(__UpperCamelCase  ,padding='max_length'  ,return_tensors='np'    ).input_features
        self.assertTrue(input_features.ndim == 3    )
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

        # Test not batched input
        lowercase_			:       List[str]									=			feature_extractor(speech_inputs[0]  ,return_tensors='np'    ).input_features
        lowercase_			:       List[str]									=			feature_extractor(np_speech_inputs[0]  ,return_tensors='np'    ).input_features
        self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase  ,atol=1e-3    )    )

        # Test batched
        lowercase_			:       Dict									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        lowercase_			:       List[str]									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        for enc_seq_a, enc_seq_a in zip(__UpperCamelCase  ,__UpperCamelCase    ):
             self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase  ,atol=1e-3    )    )

        # Test 2-D numpy arrays are batched.
        lowercase_			:       List[Any]									=			[floats_list((1, x)    )[0] for x in (800, 800, 800)]
        lowercase_			:       Optional[int]									=			np.asarray(__UpperCamelCase    )
        lowercase_			:       List[str]									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        lowercase_			:       Dict									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        for enc_seq_a, enc_seq_a in zip(__UpperCamelCase  ,__UpperCamelCase    ):
             self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase  ,atol=1e-3    )    )

        # Test truncation required
        lowercase_			:       List[str]									=			[floats_list((1, x)    )[0] for x in range(200  ,(feature_extractor.n_samples + 500)  ,200    )]
        lowercase_			:       Union[str, Any]									=			[np.asarray(__UpperCamelCase    ) for speech_input in speech_inputs]

        lowercase_			:       Union[str, Any]									=			[x[: feature_extractor.n_samples] for x in speech_inputs]
        lowercase_			:       int									=			[np.asarray(__UpperCamelCase    ) for speech_input in speech_inputs_truncated]

        lowercase_			:       Tuple									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        lowercase_			:       Optional[Any]									=			feature_extractor(__UpperCamelCase  ,return_tensors='np'    ).input_features
        for enc_seq_a, enc_seq_a in zip(__UpperCamelCase  ,__UpperCamelCase    ):
             self.assertTrue(np.allclose(__UpperCamelCase  ,__UpperCamelCase  ,atol=1e-3    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        import torch

        lowercase_			:       Union[str, Any]									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowercase_			:       List[str]									=			np.random.rand(100  ,32    ).astype(np.floataa    )
        lowercase_			:       Any									=			np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
             lowercase_			:       Optional[Any]									=			feature_extractor.pad([{'input_features': inputs}]  ,return_tensors='np'    )
             self.assertTrue(np_processed.input_features.dtype == np.floataa    )
             lowercase_			:       Union[str, Any]									=			feature_extractor.pad([{'input_features': inputs}]  ,return_tensors='pt'    )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			load_dataset('hf-internal-testing/librispeech_asr_dummy'  ,'clean'  ,split='validation'    )
        # automatic decoding with librispeech
        lowercase_			:       int									=			ds.sort('id'    ).select(range(__UpperCamelCase    )    )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			torch.tensor(
            [
                0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
            ]    )
        # fmt: on

        lowercase_			:       Optional[int]									=			self._load_datasamples(1    )
        lowercase_			:       Dict									=			WhisperFeatureExtractor()
        lowercase_			:       str									=			feature_extractor(__UpperCamelCase  ,return_tensors='pt'    ).input_features
        self.assertEqual(input_features.shape  ,(1, 80, 3000)    )
        self.assertTrue(torch.allclose(input_features[0, 0, :30]  ,__UpperCamelCase  ,atol=1e-4    )    )







   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowercase_			:       List[Any]									=			self._load_datasamples(1    )[0]
        lowercase_			:       Tuple									=			((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
        lowercase_			:       Tuple									=			feat_extract.zero_mean_unit_var_norm([audio]  ,attention_mask=__UpperCamelCase    )[0]

        self.assertTrue(np.all(np.mean(__UpperCamelCase    ) < 1e-3    )    )
        self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase    ) - 1    ) < 1e-3    )    )

