


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase      =   random.Random()

if is_torch_available():
 import torch






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  Optional[int]:




      """simple docstring"""

      if rng is None:
            snake_case_	=				global_rng

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

      return values







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=20_00		,				_UpperCAmelCase=1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=1_60_00		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				min_seq_length
       snake_case_	=				max_seq_length
       snake_case_	=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       snake_case_	=				feature_size
       snake_case_	=				padding_value
       snake_case_	=				sampling_rate
       snake_case_	=				return_attention_mask
       snake_case_	=				do_normalize



 def 						UpperCamelCase__					(     self ):
       return {
           "feature_size": self.feature_size,
           "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 ):
       def _flatten(_UpperCAmelCase ):
             return list(itertools.chain(*_UpperCAmelCase ) )

       if equal_length:
             snake_case_	=				floats_list((self.batch_size, self.max_seq_length) )
       else:
             # make sure that inputs increase in size
             snake_case_	=				[
                 _flatten(floats_list((x, self.feature_size) ) )
                 for x in range(self.min_seq_length		,				self.max_seq_length		,				self.seq_length_diff )
             ]

       if numpify:
             snake_case_	=				[np.asarray(_UpperCAmelCase ) for x in speech_inputs]

       return speech_inputs







@require_torch
@require_torchaudio
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  ASTFeatureExtractor



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ASTFeatureExtractionTester(self )



 def 						UpperCamelCase__					(     self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				[np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]

       # Test not batched input
       snake_case_	=				feat_extract(speech_inputs[0]		,				return_tensors='''np''' ).input_values
       snake_case_	=				feat_extract(np_speech_inputs[0]		,				return_tensors='''np''' ).input_values
       self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )

       # Test batched
       snake_case_	=				feat_extract(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors='''np''' ).input_values
       snake_case_	=				feat_extract(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors='''np''' ).input_values
       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.
       snake_case_	=				[floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
       snake_case_	=				np.asarray(_UpperCAmelCase )
       snake_case_	=				feat_extract(_UpperCAmelCase		,				return_tensors='''np''' ).input_values
       snake_case_	=				feat_extract(_UpperCAmelCase		,				return_tensors='''np''' ).input_values
       for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       import torch

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				np.random.rand(1_00 ).astype(np.floataa )
       snake_case_	=				np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             snake_case_	=				feature_extractor.pad([{'''input_values''': inputs}]		,				return_tensors='''np''' )
             self.assertTrue(np_processed.input_values.dtype == np.floataa )
             snake_case_	=				feature_extractor.pad([{'''input_values''': inputs}]		,				return_tensors='''pt''' )
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       from datasets import load_dataset

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

       return [x["array"] for x in speech_samples]





 @require_torch
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				torch.tensor(
           [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
            -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
            -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
            -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869] )
       # fmt: on

       snake_case_	=				self._load_datasamples(1 )
       snake_case_	=				ASTFeatureExtractor()
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''pt''' ).input_values
       self.assertEquals(input_values.shape		,				(1, 10_24, 1_28) )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]		,				_UpperCAmelCase		,				atol=1E-4 ) )



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()
       snake_case_	=				value_function
       snake_case_	=				unet
       snake_case_	=				scheduler
       snake_case_	=				env
       snake_case_	=				env.get_dataset()
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].mean()
             except:  # noqa: E722
                   pass
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].std()
             except:  # noqa: E722
                   pass
       snake_case_	=				env.observation_space.shape[0]
       snake_case_	=				env.action_space.shape[0]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if type(_UpperCAmelCase ) is dict:
             return {k: self.to_torch(_UpperCAmelCase ) for k, v in x_in.items()}
       elif torch.is_tensor(_UpperCAmelCase ):
             return x_in.to(self.unet.device )
       return torch.tensor(_UpperCAmelCase		,				device=self.unet.device )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				x.shape[0]
       snake_case_	=				None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             snake_case_	=				torch.full((batch_size,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

             # TODO: verify deprecation of this kwarg
             snake_case_	=				self.scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				predict_epsilon=_UpperCAmelCase )['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
             snake_case_	=				self.to_torch(_UpperCAmelCase )
       return x, y





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

       snake_case_	=				{0: self.to_torch(_UpperCAmelCase )}
       snake_case_	=				(batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       snake_case_	=				randn_tensor(_UpperCAmelCase		,				device=self.unet.device )
       snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
       snake_case_	=				self.to_torch(_UpperCAmelCase )

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # sort output trajectories by value
       snake_case_	=				y.argsort(0		,				descending=_UpperCAmelCase ).squeeze()
       snake_case_	=				x[sorted_idx]
       snake_case_	=				sorted_values[:, :, : self.action_dim]
       snake_case_	=				actions.detach().cpu().numpy()
       snake_case_	=				self.de_normalize(_UpperCAmelCase		,				key='''actions''' )

       # select the action with the highest value
       if y is not None:
             snake_case_	=				0
       else:
             # if we didn't run value guiding, select a random action
             snake_case_	=				np.random.randint(0		,				_UpperCAmelCase )

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions