import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
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


_SCREAMING_SNAKE_CASE:    Dict     						=							random.Random()






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=1.0				,						lowerCamelCase_=None				,						lowerCamelCase_=None	):



			"""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

@require_torch
class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					List[str]=400	,       __SCREAMING_SNAKE_CASE     :					str=2_000	,       __SCREAMING_SNAKE_CASE     :					Any=1	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=0.0	,       __SCREAMING_SNAKE_CASE     :					Tuple=24_000	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=True	,       ):






					"""simple docstring"""
					_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



		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					}







		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=False	,       __SCREAMING_SNAKE_CASE     :					Dict=False ):






					"""simple docstring"""
					def _flatten(__SCREAMING_SNAKE_CASE     :					Union[str, Any] ):
								return list(itertools.chain(*__SCREAMING_SNAKE_CASE ) )

					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(__SCREAMING_SNAKE_CASE ) for x in audio_inputs]

					return audio_inputs

@require_torch
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   EncodecFeatureExtractor



		def 					_a					(      self     :					Union[str, Any] ):






					"""simple docstring"""
					_snake_case														=   EnCodecFeatureExtractionTester(self )



		def 					_a					(      self     :					Union[str, Any] ):






					"""simple docstring"""
					# 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(800	,       1_400	,       200 )]
					_snake_case														=   [np.asarray(__SCREAMING_SNAKE_CASE ) for audio_input in audio_inputs]

					# Test not batched input
					_snake_case														=   feat_extract(audio_inputs[0]	,       return_tensors="np" ).input_values
					_snake_case														=   feat_extract(np_audio_inputs[0]	,       return_tensors="np" ).input_values
					self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       atol=1e-3 ) )

					# Test batched
					_snake_case														=   feat_extract(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="np" ).input_values
					_snake_case														=   feat_extract(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="np" ).input_values
					for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
								self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       atol=1e-3 ) )



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
					_snake_case														=   np.random.rand(100 ).astype(np.floataa )
					_snake_case														=   np_audio_inputs.tolist()

					for inputs in [py_audio_inputs, np_audio_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 					_a					(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					int ):






					"""simple docstring"""
					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(__SCREAMING_SNAKE_CASE ) )[:num_samples]["audio"]

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



		def 					_a					(      self     :					str ):






					"""simple docstring"""
					# fmt: off
					_snake_case														=   torch.tensor(
					    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
					     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
					     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
					     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
					     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
					     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
					# fmt: on
					_snake_case														=   self._load_datasamples(1 )
					_snake_case														=   EncodecFeatureExtractor()
					_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(input_values.shape	,       (1, 1, 93_680) )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]	,       __SCREAMING_SNAKE_CASE	,       atol=1e-6 ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					# fmt: off
					_snake_case														=   torch.tensor(
					    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
					     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
					     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
					     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
					     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
					     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
					# fmt: on
					_snake_case														=   self._load_datasamples(1 )
					_snake_case														=   [np.tile(input_audio[0][None]	,       reps=(2, 1) )]
					input_audio[0][1] *= 0.5
					_snake_case														=   EncodecFeatureExtractor(feature_size=2 )
					_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(input_values.shape	,       (1, 2, 93_680) )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]	,       __SCREAMING_SNAKE_CASE	,       atol=1e-6 ) )
					self.assertTrue(torch.allclose(input_values[0, 1, :30]	,       EXPECTED_INPUT_VALUES * 0.5	,       atol=1e-6 ) )







		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					_snake_case														=   self._load_datasamples(2 )
					# would be easier if the stride was like
					_snake_case														=   EncodecFeatureExtractor(feature_size=1	,       chunk_length_s=1	,       overlap=0.0_1 )

					# pad and trunc raise an error ?
					with self.assertRaisesRegex(
					    __SCREAMING_SNAKE_CASE	,       "^Both padding and truncation were set. Make sure you only set one.$"	,       ):
								_snake_case														=   feature_extractor(
								    __SCREAMING_SNAKE_CASE	,       padding="max_length"	,       truncation=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values

					# truncate to chunk
					_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       truncation=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (2, 1, 71_520) )  # 2 chunks

					# force truncate to max_length
					_snake_case														=   feature_extractor(
					    __SCREAMING_SNAKE_CASE	,       truncation=__SCREAMING_SNAKE_CASE	,       max_length=48_000	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (2, 1, 48_000) )

					# pad to chunk
					_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(padded_outputs.shape	,       (2, 1, 95_280) )

					# pad to chunk
					_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (2, 1, 95_280) )

					# force pad to max length
					_snake_case														=   feature_extractor(
					    __SCREAMING_SNAKE_CASE	,       padding="max_length"	,       max_length=100_000	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (2, 1, 100_000) )

					# force no pad
					with self.assertRaisesRegex(
					    __SCREAMING_SNAKE_CASE	,       "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,       ):
								_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values

					_snake_case														=   feature_extractor(input_audio[0]	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (1, 1, 93_680) )

					# no pad if no chunk_length_s
					_snake_case														=   None
					with self.assertRaisesRegex(
					    __SCREAMING_SNAKE_CASE	,       "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,       ):
								_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values

					_snake_case														=   feature_extractor(input_audio[0]	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (1, 1, 93_680) )

					# no pad if no overlap
					_snake_case														=   2
					_snake_case														=   None
					with self.assertRaisesRegex(
					    __SCREAMING_SNAKE_CASE	,       "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,       ):
								_snake_case														=   feature_extractor(__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values

					_snake_case														=   feature_extractor(input_audio[0]	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).input_values
					self.assertEquals(truncated_outputs.shape	,       (1, 1, 93_680) )


from __future__ import annotations

# Define the Gravitational Constant G and the function
_SCREAMING_SNAKE_CASE:    int     						=							6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   mass_a * mass_a

			if (force, mass_a, mass_a, distance).count(0	) != 1:
						raise ValueError("One and only one argument must be 0"	)
			if force < 0:
						raise ValueError("Gravitational force can not be negative"	)
			if distance < 0:
						raise ValueError("Distance can not be negative"	)
			if mass_a < 0 or mass_a < 0:
						raise ValueError("Mass can not be negative"	)
			if force == 0:
						_snake_case														=   GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
						return {"force": force}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_1": mass_a}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_2": mass_a}
			elif distance == 0:
						_snake_case														=   (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"	)


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()

