
"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


_UpperCAmelCase :			Any              =	random.Random()


def 				A	( lowercase   ,	lowercase=1.0   ,	lowercase=None   ,	lowercase=None			)       ->  Optional[Any]:
		'''simple docstring'''

		if rng is None:
				UpperCamelCase          =					global_rng

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

		return values


@require_torch
class     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=7    ,				A_=400    ,				A_=2_000    ,				A_=1    ,				A_=0.0    ,				A_=16_000    ,				A_=True    ,				A_=80    ,				A_=16    ,				A_=64    ,				A_="hann_window"    ,				A_=80    ,				A_=7_600    ,				A_=1e-10    ,				A_=True    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					min_seq_length
				UpperCamelCase          =					max_seq_length
				UpperCamelCase          =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCamelCase          =					feature_size
				UpperCamelCase          =					padding_value
				UpperCamelCase          =					sampling_rate
				UpperCamelCase          =					do_normalize
				UpperCamelCase          =					num_mel_bins
				UpperCamelCase          =					hop_length
				UpperCamelCase          =					win_length
				UpperCamelCase          =					win_function
				UpperCamelCase          =					fmin
				UpperCamelCase          =					fmax
				UpperCamelCase          =					mel_floor
				UpperCamelCase          =					return_attention_mask



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return {
				    "feature_size": self.feature_size,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "do_normalize": self.do_normalize,
				    "num_mel_bins": self.num_mel_bins,
				    "hop_length": self.hop_length,
				    "win_length": self.win_length,
				    "win_function": self.win_function,
				    "fmin": self.fmin,
				    "fmax": self.fmax,
				    "mel_floor": self.mel_floor,
				    "return_attention_mask": self.return_attention_mask,
				}



		def __UpperCamelCase			(			self    ,				A_=False    ,				A_=False	)  -> Tuple:
				"""simple docstring"""
				def _flatten(A_	):
						return list(itertools.chain(*__A	)	)

				if equal_length:
						UpperCamelCase          =					floats_list((self.batch_size, self.max_seq_length)	)
				else:
						# make sure that inputs increase in size
						UpperCamelCase          =					[
						    _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:
						UpperCamelCase          =					[np.asarray(__A	) for x in speech_inputs]

				return speech_inputs



		def __UpperCamelCase			(			self    ,				A_=False    ,				A_=False	)  -> str:
				"""simple docstring"""
				if equal_length:
						UpperCamelCase          =					[floats_list((self.max_seq_length, self.num_mel_bins)	) for _ in range(self.batch_size	)]
				else:
						# make sure that inputs increase in size
						UpperCamelCase          =					[
						    floats_list((x, self.num_mel_bins)	)
						    for x in range(self.min_seq_length    ,				self.max_seq_length    ,				self.seq_length_diff	)
						]

				if numpify:
						UpperCamelCase          =					[np.asarray(__A	) for x in speech_inputs]

				return speech_inputs






@require_torch
class     lowercase (		lowerCamelCase__  ,       unittest.TestCase       ):
		__lowercase	:	Union[str, Any]     					=    SpeechTaFeatureExtractor



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					SpeechTaFeatureExtractionTester(self	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Tuple:
				"""simple docstring"""
				self.assertTrue(np.all(np.mean(__A    ,				axis=0	) < 1e-3	)	)
				self.assertTrue(np.all(np.abs(np.var(__A    ,				axis=0	) - 1	) < 1e-3	)	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				# Tests that all call wrap to encode_plus and batch_encode_plus
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				# create three inputs of length 800, 1000, and 1200
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]
				UpperCamelCase          =					[np.asarray(__A	) for speech_input in speech_inputs]

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

				# Test batched
				UpperCamelCase          =					feat_extract(__A    ,				return_tensors='np'	).input_values
				UpperCamelCase          =					feat_extract(__A    ,				return_tensors='np'	).input_values
				for enc_seq_a, enc_seq_a in zip(__A    ,				__A	):
						self.assertTrue(np.allclose(__A    ,				__A    ,				atol=1e-3	)	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]

				UpperCamelCase          =					['''longest''', '''max_length''', '''do_not_pad''']
				UpperCamelCase          =					[None, 1_600, None]
				for max_length, padding in zip(__A    ,				__A	):
						UpperCamelCase          =					feat_extract(__A    ,				padding=__A    ,				max_length=__A    ,				return_tensors='np'	)
						UpperCamelCase          =					processed.input_values

						self._check_zero_mean_unit_variance(input_values[0][:800]	)
						self.assertTrue(input_values[0][800:].sum() < 1e-6	)
						self._check_zero_mean_unit_variance(input_values[1][:1_000]	)
						self.assertTrue(input_values[0][1_000:].sum() < 1e-6	)
						self._check_zero_mean_unit_variance(input_values[2][:1_200]	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				UpperCamelCase          =					range(800    ,				1_400    ,				200	)
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in lengths]

				UpperCamelCase          =					['''longest''', '''max_length''', '''do_not_pad''']
				UpperCamelCase          =					[None, 1_600, None]

				for max_length, padding in zip(__A    ,				__A	):
						UpperCamelCase          =					feat_extract(__A    ,				max_length=__A    ,				padding=__A	)
						UpperCamelCase          =					processed.input_values

						self._check_zero_mean_unit_variance(input_values[0][:800]	)
						self._check_zero_mean_unit_variance(input_values[1][:1_000]	)
						self._check_zero_mean_unit_variance(input_values[2][:1_200]	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]
				UpperCamelCase          =					feat_extract(
				    __A    ,				truncation=__A    ,				max_length=1_000    ,				padding='max_length'    ,				return_tensors='np'	)
				UpperCamelCase          =					processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :800]	)
				self._check_zero_mean_unit_variance(input_values[1]	)
				self._check_zero_mean_unit_variance(input_values[2]	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]
				UpperCamelCase          =					feat_extract(
				    __A    ,				truncation=__A    ,				max_length=1_000    ,				padding='longest'    ,				return_tensors='np'	)
				UpperCamelCase          =					processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :800]	)
				self._check_zero_mean_unit_variance(input_values[1, :1_000]	)
				self._check_zero_mean_unit_variance(input_values[2]	)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertTrue(input_values.shape == (3, 1_000)	)

				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]
				UpperCamelCase          =					feat_extract(
				    __A    ,				truncation=__A    ,				max_length=2_000    ,				padding='longest'    ,				return_tensors='np'	)
				UpperCamelCase          =					processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :800]	)
				self._check_zero_mean_unit_variance(input_values[1, :1_000]	)
				self._check_zero_mean_unit_variance(input_values[2]	)

				# make sure that if max_length > longest -> then pad to longest
				self.assertTrue(input_values.shape == (3, 1_200)	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				UpperCamelCase          =					np.random.rand(100	).astype(np.floataa	)
				UpperCamelCase          =					np_speech_inputs.tolist()

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



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				# Tests that all call wrap to encode_plus and batch_encode_plus
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				# create three inputs of length 800, 1000, and 1200
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in range(800    ,				1_400    ,				200	)]
				UpperCamelCase          =					[np.asarray(__A	) for speech_input in speech_inputs]

				# Test feature size
				UpperCamelCase          =					feature_extractor(audio_target=__A    ,				padding=__A    ,				return_tensors='np'	).input_values
				self.assertTrue(input_values.ndim == 3	)
				self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins	)

				# Test not batched input
				UpperCamelCase          =					feature_extractor(speech_inputs[0]    ,				return_tensors='np'	).input_values
				UpperCamelCase          =					feature_extractor(np_speech_inputs[0]    ,				return_tensors='np'	).input_values
				self.assertTrue(np.allclose(__A    ,				__A    ,				atol=1e-3	)	)

				# Test batched
				UpperCamelCase          =					feature_extractor(__A    ,				return_tensors='np'	).input_values
				UpperCamelCase          =					feature_extractor(__A    ,				return_tensors='np'	).input_values
				for enc_seq_a, enc_seq_a in zip(__A    ,				__A	):
						self.assertTrue(np.allclose(__A    ,				__A    ,				atol=1e-3	)	)

				# Test 2-D numpy arrays are batched.
				UpperCamelCase          =					[floats_list((1, x)	)[0] for x in (800, 800, 800)]
				UpperCamelCase          =					np.asarray(__A	)
				UpperCamelCase          =					feature_extractor(__A    ,				return_tensors='np'	).input_values
				UpperCamelCase          =					feature_extractor(__A    ,				return_tensors='np'	).input_values
				for enc_seq_a, enc_seq_a in zip(__A    ,				__A	):
						self.assertTrue(np.allclose(__A    ,				__A    ,				atol=1e-3	)	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target()
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
				UpperCamelCase          =					feat_extract.model_input_names[0]

				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

				self.assertTrue(all(len(__A	) == len(__A	) for x, y in zip(__A    ,				processed_features[input_name]	)	)	)

				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=__A	)
				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}    ,				tensor_type='np'	)

				UpperCamelCase          =					processed_features[input_name]

				if len(batch_features_input.shape	) < 3:
						UpperCamelCase          =					batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=__A	)
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
				UpperCamelCase          =					feat_extract.model_input_names[0]

				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}    ,				tensor_type='pt'	)

				UpperCamelCase          =					processed_features[input_name]

				if len(batch_features_input.shape	) < 3:
						UpperCamelCase          =					batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target()
				UpperCamelCase          =					feat_extract.model_input_names[0]

				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

				UpperCamelCase          =					feat_extract.num_mel_bins  # hack!

				UpperCamelCase          =					feat_extract.pad(__A    ,				padding='longest'    ,				return_tensors='np'	)[input_name]
				UpperCamelCase          =					feat_extract.pad(__A    ,				padding='longest'    ,				return_tensors='pt'	)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1e-2	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.feat_extract_dict
				UpperCamelCase          =					True
				UpperCamelCase          =					self.feature_extraction_class(**__A	)
				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target()
				UpperCamelCase          =					[len(__A	) for x in speech_inputs]
				UpperCamelCase          =					feat_extract.model_input_names[0]

				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

				UpperCamelCase          =					feat_extract.num_mel_bins  # hack!

				UpperCamelCase          =					feat_extract.pad(__A    ,				padding='longest'    ,				return_tensors='np'	)
				self.assertIn('attention_mask'    ,				__A	)
				self.assertListEqual(list(processed.attention_mask.shape	)    ,				list(processed[input_name].shape[:2]	)	)
				self.assertListEqual(processed.attention_mask.sum(-1	).tolist()    ,				__A	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.feat_extract_dict
				UpperCamelCase          =					True
				UpperCamelCase          =					self.feature_extraction_class(**__A	)
				UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_target()
				UpperCamelCase          =					[len(__A	) for x in speech_inputs]
				UpperCamelCase          =					feat_extract.model_input_names[0]

				UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)
				UpperCamelCase          =					min(__A	)

				UpperCamelCase          =					feat_extract.num_mel_bins  # hack!

				UpperCamelCase          =					feat_extract.pad(
				    __A    ,				padding='max_length'    ,				max_length=__A    ,				truncation=__A    ,				return_tensors='np'	)
				self.assertIn('attention_mask'    ,				__A	)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape	)    ,				[processed_pad[input_name].shape[0], max_length]	)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()    ,				[max_length for x in speech_inputs]	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				from datasets import load_dataset

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

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



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase          =					torch.tensor(
				    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
				     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
				     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
				     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
				     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
				     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]	)
				# fmt: on

				UpperCamelCase          =					self._load_datasamples(1	)
				UpperCamelCase          =					SpeechTaFeatureExtractor()
				UpperCamelCase          =					feature_extractor(__A    ,				return_tensors='pt'	).input_values
				self.assertEquals(input_values.shape    ,				(1, 93_680)	)
				self.assertTrue(torch.allclose(input_values[0, :30]    ,				__A    ,				atol=1e-6	)	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase          =					torch.tensor(
				    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
				     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
				     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
				     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]	)
				# fmt: on

				UpperCamelCase          =					self._load_datasamples(1	)
				UpperCamelCase          =					SpeechTaFeatureExtractor()
				UpperCamelCase          =					feature_extractor(audio_target=__A    ,				return_tensors='pt'	).input_values
				self.assertEquals(input_values.shape    ,				(1, 366, 80)	)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,				__A    ,				atol=1e-4	)	)


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase :			Optional[Any]              =	{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
					_UpperCAmelCase :			int              =	"hopper-medium-v2"
					_UpperCAmelCase :			Tuple              =	gym.make(env_name)

					_UpperCAmelCase :			Any              =	ValueGuidedRLPipeline.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase :			Optional[Any]              =	env.reset()
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Dict              =	1_000
					_UpperCAmelCase :			Tuple              =	[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase :			int              =	pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase :			List[Any]              =	env.step(denorm_actions)
															_UpperCAmelCase :			int              =	env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase :			Union[str, Any]              =	next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')
