


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
 from huggingface_hub import snapshot_download
 from pyctcdecode import BeamSearchDecoderCTC

 from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
 from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
 from transformers import WavaVecaForCTC







@require_pyctcdecode
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
       snake_case_	=				dict(zip(__A		,				range(len(__A ) ) ) )

       snake_case_	=				{
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       snake_case_	=				{
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       snake_case_	=				tempfile.mkdtemp()
       snake_case_	=				os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file'''] )
       snake_case_	=				os.path.join(self.tmpdirname		,				__A )
       with open(self.vocab_file		,				'''w'''		,				encoding='''utf-8''' ) as fp:
             fp.write(json.dumps(__A ) + '''\n''' )

       with open(self.feature_extraction_file		,				'''w'''		,				encoding='''utf-8''' ) as fp:
             fp.write(json.dumps(__A ) + '''\n''' )

       # load decoder from hub
       snake_case_	=				'''hf-internal-testing/ngram-beam-search-decoder'''



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       snake_case_	=				self.add_kwargs_tokens_map.copy()
       kwargs.update(__A )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,				**__A )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,				**__A )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,				**__A )



 def 						UpperCamelCase__					(     self ):
       shutil.rmtree(self.tmpdirname )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       processor.save_pretrained(self.tmpdirname )
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

       # tokenizer
       self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer		,				__A )

       # feature extractor
       self.assertEqual(processor.feature_extractor.to_json_string()		,				feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor		,				__A )

       # decoder
       self.assertEqual(processor.decoder._alphabet.labels		,				decoder._alphabet.labels )
       self.assertEqual(
           processor.decoder.model_container[decoder._model_key]._unigram_set		,				decoder.model_container[decoder._model_key]._unigram_set		,				)
       self.assertIsInstance(processor.decoder		,				__A )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WavaVecaProcessorWithLM(
           tokenizer=self.get_tokenizer()		,				feature_extractor=self.get_feature_extractor()		,				decoder=self.get_decoder() )
       processor.save_pretrained(self.tmpdirname )

       # make sure that error is thrown when decoder alphabet doesn't match
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained(
           self.tmpdirname		,				alpha=5.0		,				beta=3.0		,				score_boundary=-7.0		,				unk_score_offset=3 )

       # decoder
       self.assertEqual(processor.language_model.alpha		,				5.0 )
       self.assertEqual(processor.language_model.beta		,				3.0 )
       self.assertEqual(processor.language_model.score_boundary		,				-7.0 )
       self.assertEqual(processor.language_model.unk_score_offset		,				3 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_tokenizer()
       # add token to trigger raise
       tokenizer.add_tokens(['''xx'''] )
       with self.assertRaisesRegex(__A		,				'''include''' ):
             WavaVecaProcessorWithLM(
                 tokenizer=__A		,				feature_extractor=self.get_feature_extractor()		,				decoder=self.get_decoder() )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				floats_list((3, 10_00) )

       snake_case_	=				feature_extractor(__A		,				return_tensors='''np''' )
       snake_case_	=				processor(__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 ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				'''This is a test string'''

       snake_case_	=				processor(text=__A )

       snake_case_	=				tokenizer(__A )

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=(2, 10, 16)		,				_UpperCAmelCase=77 ):
       np.random.seed(__A )
       return np.random.rand(*__A )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				self._get_dummy_logits(shape=(10, 16)		,				seed=13 )

       snake_case_	=				processor.decode(__A )

       snake_case_	=				decoder.decode_beams(__A )[0]

       self.assertEqual(decoded_decoder[0]		,				decoded_processor.text )
       self.assertEqual('''</s> <s> </s>'''		,				decoded_processor.text )
       self.assertEqual(decoded_decoder[-2]		,				decoded_processor.logit_score )
       self.assertEqual(decoded_decoder[-1]		,				decoded_processor.lm_score )



 @parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				self._get_dummy_logits()

       # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
       #       otherwise, the LM won't be available to the pool's sub-processes.
       # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
       if pool_context is None:
             snake_case_	=				processor.batch_decode(__A )
       else:
             with get_context(__A ).Pool() as pool:
                   snake_case_	=				processor.batch_decode(__A		,				__A )

       snake_case_	=				list(__A )

       with get_context('''fork''' ).Pool() as p:
             snake_case_	=				decoder.decode_beams_batch(__A		,				__A )

       snake_case_	,   snake_case_	,   snake_case_	=				[], [], []
       for beams in decoded_beams:
             texts_decoder.append(beams[0][0] )
             logit_scores_decoder.append(beams[0][-2] )
             lm_scores_decoder.append(beams[0][-1] )

       self.assertListEqual(__A		,				decoded_processor.text )
       self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']		,				decoded_processor.text )
       self.assertListEqual(__A		,				decoded_processor.logit_score )
       self.assertListEqual(__A		,				decoded_processor.lm_score )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				self._get_dummy_logits()

       snake_case_	=				15
       snake_case_	=				-20.0
       snake_case_	=				-4.0

       snake_case_	=				processor.batch_decode(
           __A		,				beam_width=__A		,				beam_prune_logp=__A		,				token_min_logp=__A		,				)
       snake_case_	=				decoded_processor_out.text

       snake_case_	=				list(__A )

       with get_context('''fork''' ).Pool() as pool:
             snake_case_	=				decoder.decode_beams_batch(
                 __A		,				__A		,				beam_width=__A		,				beam_prune_logp=__A		,				token_min_logp=__A		,				)

       snake_case_	=				[d[0][0] for d in decoded_decoder_out]
       snake_case_	=				[d[0][2] for d in decoded_decoder_out]
       snake_case_	=				[d[0][3] for d in decoded_decoder_out]

       self.assertListEqual(__A		,				__A )
       self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']		,				__A )

       self.assertTrue(np.array_equal(__A		,				decoded_processor_out.logit_score ) )
       self.assertTrue(np.allclose([-20.054, -18.447]		,				__A		,				atol=1E-3 ) )

       self.assertTrue(np.array_equal(__A		,				decoded_processor_out.lm_score ) )
       self.assertTrue(np.allclose([-15.554, -13.9_474]		,				__A		,				atol=1E-3 ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

       snake_case_	=				self._get_dummy_logits()

       snake_case_	=				2.0
       snake_case_	=				5.0
       snake_case_	=				-20.0
       snake_case_	=				True

       snake_case_	=				processor.batch_decode(
           __A		,				alpha=__A		,				beta=__A		,				unk_score_offset=__A		,				lm_score_boundary=__A		,				)
       snake_case_	=				decoded_processor_out.text

       snake_case_	=				list(__A )
       decoder.reset_params(
           alpha=__A		,				beta=__A		,				unk_score_offset=__A		,				lm_score_boundary=__A		,				)

       with get_context('''fork''' ).Pool() as pool:
             snake_case_	=				decoder.decode_beams_batch(
                 __A		,				__A		,				)

       snake_case_	=				[d[0][0] for d in decoded_decoder_out]

       self.assertListEqual(__A		,				__A )
       self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']		,				__A )
       snake_case_	=				processor.decoder.model_container[processor.decoder._model_key]
       self.assertEqual(lm_model.alpha		,				2.0 )
       self.assertEqual(lm_model.beta		,				5.0 )
       self.assertEqual(lm_model.unk_score_offset		,				-20.0 )
       self.assertEqual(lm_model.score_boundary		,				__A )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )

       snake_case_	=				processor.decoder.model_container[processor.decoder._model_key]
       snake_case_	=				Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

       snake_case_	=				os.listdir(__A )
       snake_case_	=				['''alphabet.json''', '''language_model''']

       downloaded_decoder_files.sort()
       expected_decoder_files.sort()

       # test that only decoder relevant files from
       # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
       # are downloaded and none of the rest (e.g. README.md, ...)
       self.assertListEqual(__A		,				__A )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				snapshot_download('''hf-internal-testing/processor_with_lm''' )

       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained(__A )

       snake_case_	=				processor.decoder.model_container[processor.decoder._model_key]
       snake_case_	=				Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

       snake_case_	=				os.listdir(__A )
       snake_case_	=				os.listdir(__A )

       local_decoder_files.sort()
       expected_decoder_files.sort()

       # test that both decoder form hub and local files in cache are the same
       self.assertListEqual(__A		,				__A )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
       snake_case_	=				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )

       snake_case_	=				floats_list((3, 10_00) )

       snake_case_	=				processor_wavaveca(__A		,				return_tensors='''np''' )
       snake_case_	=				processor_auto(__A		,				return_tensors='''np''' )

       for key in input_wavaveca.keys():
             self.assertAlmostEqual(input_wavaveca[key].sum()		,				input_auto[key].sum()		,				delta=1E-2 )

       snake_case_	=				self._get_dummy_logits()

       snake_case_	=				processor_wavaveca.batch_decode(__A )
       snake_case_	=				processor_auto.batch_decode(__A )

       self.assertListEqual(decoded_wavaveca.text		,				decoded_auto.text )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_feature_extractor()
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_decoder()

       snake_case_	=				WavaVecaProcessorWithLM(tokenizer=__A		,				feature_extractor=__A		,				decoder=__A )

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



 @staticmethod
 def 						UpperCamelCase__					(     _UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				[d[key] for d in offsets]
       return retrieved_list



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
       snake_case_	=				self._get_dummy_logits()[0]

       snake_case_	=				processor.decode(__A		,				output_word_offsets=__A )
       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )		,				4 )
       self.assertTrue('''text''' in outputs )
       self.assertTrue('''word_offsets''' in outputs )
       self.assertTrue(isinstance(__A		,				__A ) )

       self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']		,				'''word''' ) )		,				outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']		,				'''word''' )		,				['''<s>''', '''<s>''', '''</s>'''] )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']		,				'''start_offset''' )		,				[0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']		,				'''end_offset''' )		,				[1, 3, 5] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
       snake_case_	=				self._get_dummy_logits()

       snake_case_	=				processor.batch_decode(__A		,				output_word_offsets=__A )

       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )		,				4 )
       self.assertTrue('''text''' in outputs )
       self.assertTrue('''word_offsets''' in outputs )
       self.assertTrue(isinstance(__A		,				__A ) )

       self.assertListEqual(
           [''' '''.join(self.get_from_offsets(__A		,				'''word''' ) ) for o in outputs['''word_offsets''']]		,				outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]		,				'''word''' )		,				['''<s>''', '''<s>''', '''</s>'''] )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]		,				'''start_offset''' )		,				[0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]		,				'''end_offset''' )		,				[1, 3, 5] )





 @slow
 @require_torch
 @require_torchaudio
 def 						UpperCamelCase__					(     self ):
       import torch

       snake_case_	=				load_dataset('''common_voice'''		,				'''en'''		,				split='''train'''		,				streaming=__A )
       snake_case_	=				ds.cast_column('''audio'''		,				datasets.Audio(sampling_rate=1_60_00 ) )
       snake_case_	=				iter(__A )
       snake_case_	=				next(__A )

       snake_case_	=				AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
       snake_case_	=				WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )

       # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
       snake_case_	=				processor(sample['''audio''']['''array''']		,				return_tensors='''pt''' ).input_values

       with torch.no_grad():
             snake_case_	=				model(__A ).logits.cpu().numpy()

       snake_case_	=				processor.decode(logits[0]		,				output_word_offsets=__A )

       snake_case_	=				model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
       snake_case_	=				[
           {
               '''start_time''': d['''start_offset'''] * time_offset,
               '''end_time''': d['''end_offset'''] * time_offset,
               '''word''': d['''word'''],
           }
           for d in output['''word_offsets''']
       ]

       snake_case_	=				'''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

       # output words
       self.assertEqual(''' '''.join(self.get_from_offsets(__A		,				'''word''' ) )		,				__A )
       self.assertEqual(''' '''.join(self.get_from_offsets(__A		,				'''word''' ) )		,				output.text )

       # output times
       snake_case_	=				torch.tensor(self.get_from_offsets(__A		,				'''start_time''' ) )
       snake_case_	=				torch.tensor(self.get_from_offsets(__A		,				'''end_time''' ) )

       # fmt: off
       snake_case_	=				torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599] )
       snake_case_	=				torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94] )
       # fmt: on

       self.assertTrue(torch.allclose(__A		,				__A		,				atol=0.01 ) )
       self.assertTrue(torch.allclose(__A		,				__A		,				atol=0.01 ) )



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