






"""simple docstring"""



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


_UpperCamelCase						:    Optional[Any]		=						random.Random()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Union[str, Any]=1.0						,   __snake_case     :    Optional[int]=None						,   __snake_case     :    Dict=None       ):





  '''simple docstring'''





  if rng is None:
    lowercase				       =			global_rng

  lowercase				       =			[]
  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 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=2_0_0_0				,						_lowerCamelCase=1				,						_lowerCamelCase=0.0				,						_lowerCamelCase=2_4_0_0_0				,						_lowerCamelCase=True				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			min_seq_length
      lowercase				       =			max_seq_length
      lowercase				       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      lowercase				       =			feature_size
      lowercase				       =			padding_value
      lowercase				       =			sampling_rate
      lowercase				       =			return_attention_mask







    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,
      }







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=False				,						_lowerCamelCase=False			):
      def _flatten(_lowerCamelCase			):
        return list(itertools.chain(*_lowerCamelCase			)			)

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

      return audio_inputs


@require_torch
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=EncodecFeatureExtractor







    def      UpperCamelCase_ (     self			):
      lowercase				       =			EnCodecFeatureExtractionTester(self			)







    def      UpperCamelCase_ (     self			):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      lowercase				       =			[floats_list((1, x)			)[0] for x in range(8_0_0				,						1_4_0_0				,						2_0_0			)]
      lowercase				       =			[np.asarray(_lowerCamelCase			) for audio_input in audio_inputs]

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      lowercase				       =			np.random.rand(1_0_0			).astype(np.floataa			)
      lowercase				       =			np_audio_inputs.tolist()

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      from datasets import load_dataset

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

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







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			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
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			EncodecFeatureExtractor()
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 1, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			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
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			[np.tile(input_audio[0][None]				,						reps=(2, 1)			)]
      input_audio[0][1] *= 0.5
      lowercase				       =			EncodecFeatureExtractor(feature_size=2			)
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 2, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)
      self.assertTrue(torch.allclose(input_values[0, 1, :3_0]				,						EXPECTED_INPUT_VALUES * 0.5				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self._load_datasamples(2			)
      # would be easier if the stride was like
      lowercase				       =			EncodecFeatureExtractor(feature_size=1				,						chunk_length_s=1				,						overlap=0.0_1			)

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

      # truncate to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						truncation=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 7_1_5_2_0)			)  # 2 chunks

      # force truncate to max_length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						truncation=_lowerCamelCase				,						max_length=4_8_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 4_8_0_0_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(padded_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # force pad to max length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						padding='max_length'				,						max_length=1_0_0_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 1_0_0_0_0_0)			)

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

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

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

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

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

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Optional[Any]				   =      StableDiffusionParadigmsPipeline
						__A:						Optional[int]				   =      TEXT_TO_IMAGE_PARAMS
						__A:						Dict				   =      TEXT_TO_IMAGE_BATCH_PARAMS
						__A:						Any				   =      TEXT_TO_IMAGE_IMAGE_PARAMS
						__A:						List[Any]				   =      TEXT_TO_IMAGE_IMAGE_PARAMS

						def 						a__ (					self		:			Optional[Any]			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		UNetaDConditionModel(
													    block_out_channels=(32, 64)	,						layers_per_block=2	,						sample_size=32	,						in_channels=4	,						out_channels=4	,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,						cross_attention_dim=32	,						attention_head_dim=(2, 4)	,						use_linear_projection=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Dict	       =		DDIMParallelScheduler(
													    beta_start=0.0_00_85	,						beta_end=0.0_12	,						beta_schedule="scaled_linear"	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		AutoencoderKL(
													    block_out_channels=[32, 64]	,						in_channels=3	,						out_channels=3	,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,						latent_channels=4	,						sample_size=1_28	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		CLIPTextConfig(
													    bos_token_id=0	,						eos_token_id=2	,						hidden_size=32	,						intermediate_size=37	,						layer_norm_eps=1E-05	,						num_attention_heads=4	,						num_hidden_layers=5	,						pad_token_id=1	,						vocab_size=10_00	,						hidden_act="gelu"	,						projection_dim=5_12	,						)
													_UpperCAmelCase  :			Tuple	       =		CLIPTextModel(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													_UpperCAmelCase  :			Optional[int]	       =		{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int=0			):
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Tuple	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[int]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			List[str]	       =		self.get_dummy_components()
													_UpperCAmelCase  :			List[Any]	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			str	       =		np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Dict	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		DDPMParallelScheduler()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			Any	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			str	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_single_identical(batch_size=2	,						expected_max_diff=3E-3			)






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			int	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Tuple	       =		self.get_dummy_components()
													_UpperCAmelCase  :			Dict	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Optional[int]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		"french fries"
													_UpperCAmelCase  :			Tuple	       =		sd_pipe(**_lowerCamelCase	,						negative_prompt=_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		output.images
													_UpperCAmelCase  :			Union[str, Any]	       =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







@slow
@require_torch_gpu
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]=0			):
													_UpperCAmelCase  :			Union[str, Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs






						def 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			str	       =		"stabilityai/stable-diffusion-2-base"
													_UpperCAmelCase  :			List[str]	       =		DDIMParallelScheduler.from_pretrained(_lowerCamelCase	,						subfolder="scheduler"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		StableDiffusionParadigmsPipeline.from_pretrained(_lowerCamelCase	,						scheduler=_lowerCamelCase	,						safety_checker=_lowerCamelCase			)
													pipe.to(_lowerCamelCase			)
													pipe.set_progress_bar_config(disable=_lowerCamelCase			)
													pipe.enable_attention_slicing()

													_UpperCAmelCase  :			Optional[Any]	       =		self.get_inputs()
													_UpperCAmelCase  :			List[str]	       =		pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)

													_UpperCAmelCase  :			Union[str, Any]	       =		np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

													assert np.abs(expected_slice - image_slice			).max() < 1E-2




