






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


a_							=      random.Random()


def     _a					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			int=1.0      ,				UpperCamelCase_   :			List[str]=None      ,				UpperCamelCase_   :			Optional[int]=None				)	->				str:
						"""simple docstring"""







						if rng is None:
												lowerCAmelCase__	 =  global_rng

						lowerCAmelCase__	 =  []
						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						,      __UpperCAmelCase						,      __UpperCAmelCase=7						,      __UpperCAmelCase=400						,      __UpperCAmelCase=2000						,      __UpperCAmelCase=1						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=24000						,      __UpperCAmelCase=True						,      )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  batch_size
							lowerCAmelCase__	 =  min_seq_length
							lowerCAmelCase__	 =  max_seq_length
							lowerCAmelCase__	 =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase__	 =  feature_size
							lowerCAmelCase__	 =  padding_value
							lowerCAmelCase__	 =  sampling_rate
							lowerCAmelCase__	 =  return_attention_mask

	def 						UpperCAmelCase					(							self    )->   int:







							'''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 						UpperCAmelCase					(							self						,      __UpperCAmelCase=False						,      __UpperCAmelCase=False    )->   Any:







							'''simple docstring'''





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

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

							return audio_inputs





@require_torch
class        lowercase__       (     _UpperCAmelCase,   unittest.TestCase		):
	a_             =EncodecFeatureExtractor

	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  EnCodecFeatureExtractionTester(self    )

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase__	 =  [floats_list((1, x)    )[0] for x in range(800						,      1400						,      200    )]
							lowerCAmelCase__	 =  [np.asarray(__UpperCAmelCase    ) for audio_input in audio_inputs]

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

							# Test batched
							lowerCAmelCase__	 =  feat_extract(__UpperCAmelCase						,      padding=__UpperCAmelCase						,      return_tensors="np"    ).input_values
							lowerCAmelCase__	 =  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    )    )

	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							lowerCAmelCase__	 =  np.random.rand(100    ).astype(np.floataa    )
							lowerCAmelCase__	 =  np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
													lowerCAmelCase__	 =  feature_extractor.pad([{"input_values": inputs}]						,      return_tensors="np"    )
													self.assertTrue(np_processed.input_values.dtype == np.floataa    )
													lowerCAmelCase__	 =  feature_extractor.pad([{"input_values": inputs}]						,      return_tensors="pt"    )
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							from datasets import load_dataset

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

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

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







							'''simple docstring'''





							lowerCAmelCase__	 =  torch.tensor(
							    [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
							     3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
							     2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
							     4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
							     7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
							     4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]    )
							# fmt: on
							lowerCAmelCase__	 =  self._load_datasamples(1    )
							lowerCAmelCase__	 =  EncodecFeatureExtractor()
							lowerCAmelCase__	 =  feature_extractor(__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(input_values.shape						,      (1, 1, 93680)    )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]						,      __UpperCAmelCase						,      atol=1E-6    )    )

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  torch.tensor(
							    [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
							     3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
							     2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
							     4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
							     7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
							     4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]    )
							# fmt: on
							lowerCAmelCase__	 =  self._load_datasamples(1    )
							lowerCAmelCase__	 =  [np.tile(input_audio[0][None]						,      reps=(2, 1)    )]
							input_audio[0][1] *= 0.5
							lowerCAmelCase__	 =  EncodecFeatureExtractor(feature_size=2    )
							lowerCAmelCase__	 =  feature_extractor(__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(input_values.shape						,      (1, 2, 93680)    )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]						,      __UpperCAmelCase						,      atol=1E-6    )    )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]						,      EXPECTED_INPUT_VALUES * 0.5						,      atol=1E-6    )    )


	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._load_datasamples(2    )
							# would be easier if the stride was like
							lowerCAmelCase__	 =  EncodecFeatureExtractor(feature_size=1						,      chunk_length_s=1						,      overlap=0.01    )

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

							# truncate to chunk
							lowerCAmelCase__	 =  feature_extractor(__UpperCAmelCase						,      truncation=__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (2, 1, 71520)    )  # 2 chunks

							# force truncate to max_length
							lowerCAmelCase__	 =  feature_extractor(
							    __UpperCAmelCase						,      truncation=__UpperCAmelCase						,      max_length=48000						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (2, 1, 48000)    )

							# pad to chunk
							lowerCAmelCase__	 =  feature_extractor(__UpperCAmelCase						,      padding=__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(padded_outputs.shape						,      (2, 1, 95280)    )

							# pad to chunk
							lowerCAmelCase__	 =  feature_extractor(__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (2, 1, 95280)    )

							# force pad to max length
							lowerCAmelCase__	 =  feature_extractor(
							    __UpperCAmelCase						,      padding="max_length"						,      max_length=100000						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (2, 1, 100000)    )

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

							lowerCAmelCase__	 =  feature_extractor(input_audio[0]						,      padding=__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (1, 1, 93680)    )

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

							lowerCAmelCase__	 =  feature_extractor(input_audio[0]						,      padding=__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (1, 1, 93680)    )

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

							lowerCAmelCase__	 =  feature_extractor(input_audio[0]						,      padding=__UpperCAmelCase						,      return_tensors="pt"    ).input_values
							self.assertEquals(truncated_outputs.shape						,      (1, 1, 93680)    )














import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






