


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			:			int			      =					random.Random()


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		int=1.0				,      SCREAMING_SNAKE_CASE_   :		List[str]=None				,      SCREAMING_SNAKE_CASE_   :		List[Any]=None							):






       """simple docstring"""





       if rng is None:
              UpperCamelCase     :						Optional[int] 						=		global_rng

       UpperCamelCase     :						List[str] 						=		[]
       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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=7			,       __SCREAMING_SNAKE_CASE=400			,       __SCREAMING_SNAKE_CASE=2_000			,       __SCREAMING_SNAKE_CASE=1			,       __SCREAMING_SNAKE_CASE=0.0			,       __SCREAMING_SNAKE_CASE=24_000			,       __SCREAMING_SNAKE_CASE=True			,       ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		parent
              UpperCamelCase     :						Optional[int] 						=		batch_size
              UpperCamelCase     :						List[str] 						=		min_seq_length
              UpperCamelCase     :						Optional[Any] 						=		max_seq_length
              UpperCamelCase     :						Any 						=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
              UpperCamelCase     :						Tuple 						=		feature_size
              UpperCamelCase     :						int 						=		padding_value
              UpperCamelCase     :						Optional[int] 						=		sampling_rate
              UpperCamelCase     :						Optional[int] 						=		return_attention_mask






       def 			_lowercase (  self    ):




              """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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=False			,       __SCREAMING_SNAKE_CASE=False    ):




              """simple docstring"""




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

              if equal_length:
                     UpperCamelCase     :						Optional[Any] 						=		floats_list((self.batch_size, self.max_seq_length)    )
              else:
                     # make sure that inputs increase in size
                     UpperCamelCase     :						Tuple 						=		[
                         _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     :						Union[str, Any] 						=		[np.asarray(__SCREAMING_SNAKE_CASE    ) for x in audio_inputs]

              return audio_inputs




@require_torch
class  UpperCAmelCase_						(				_a,  unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Any				=       EncodecFeatureExtractor






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		EnCodecFeatureExtractionTester(self    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
              # create three inputs of length 800, 1000, and 1200
              UpperCamelCase     :						Optional[int] 						=		[floats_list((1, x)    )[0] for x in range(800			,       1_400			,       200    )]
              UpperCamelCase     :						Any 						=		[np.asarray(__SCREAMING_SNAKE_CASE    ) for audio_input in audio_inputs]

              # Test not batched input
              UpperCamelCase     :						List[str] 						=		feat_extract(audio_inputs[0]			,       return_tensors='''np'''    ).input_values
              UpperCamelCase     :						Any 						=		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
              UpperCamelCase     :						Optional[int] 						=		feat_extract(__SCREAMING_SNAKE_CASE			,       padding=__SCREAMING_SNAKE_CASE			,       return_tensors='''np'''    ).input_values
              UpperCamelCase     :						Optional[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
              UpperCamelCase     :						int 						=		np.random.rand(100    ).astype(np.floataa    )
              UpperCamelCase     :						List[str] 						=		np_audio_inputs.tolist()

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              from datasets import load_dataset

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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		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     :						int 						=		self._load_datasamples(1    )
              UpperCamelCase     :						Any 						=		EncodecFeatureExtractor()
              UpperCamelCase     :						Optional[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		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     :						Tuple 						=		self._load_datasamples(1    )
              UpperCamelCase     :						List[Any] 						=		[np.tile(input_audio[0][None]			,       reps=(2, 1)    )]
              input_audio[0][1] *= 0.5
              UpperCamelCase     :						List[Any] 						=		EncodecFeatureExtractor(feature_size=2    )
              UpperCamelCase     :						Optional[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self._load_datasamples(2    )
              # would be easier if the stride was like
              UpperCamelCase     :						List[str] 						=		EncodecFeatureExtractor(feature_size=1			,       chunk_length_s=1			,       overlap=0.01    )

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

              # truncate to chunk
              UpperCamelCase     :						List[Any] 						=		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
              UpperCamelCase     :						Any 						=		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
              UpperCamelCase     :						int 						=		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
              UpperCamelCase     :						str 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''    ).input_values
              self.assertEquals(truncated_outputs.shape			,       (2, 1, 95_280)    )

              # force pad to max length
              UpperCamelCase     :						Tuple 						=		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.$'''			,       ):
                     UpperCamelCase     :						Any 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       padding=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''    ).input_values

              UpperCamelCase     :						Tuple 						=		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
              UpperCamelCase     :						Union[str, Any] 						=		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.$'''			,       ):
                     UpperCamelCase     :						str 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       padding=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''    ).input_values

              UpperCamelCase     :						Optional[Any] 						=		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
              UpperCamelCase     :						List[Any] 						=		2
              UpperCamelCase     :						Dict 						=		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.$'''			,       ):
                     UpperCamelCase     :						Any 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       padding=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''    ).input_values

              UpperCamelCase     :						str 						=		feature_extractor(input_audio[0]			,       padding=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''    ).input_values
              self.assertEquals(truncated_outputs.shape			,       (1, 1, 93_680)    )





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		self.get_models()
              # Take the first (hypothetical) EMA step.
              ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 1

              # Take two more.
              for _ in range(2    ):
                     ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		self.get_models()
              # Since the `unet` is not being updated (i.e., backprop'd)
              # there won't be any difference between the `params` of `unet`
              # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
              ema_unet.step(unet.parameters()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # The above holds true even if we call `ema.step()` multiple times since
              # `unet` params are still not being updated.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Any 						=		self.get_models()
              # Here we simulate the parameter updates for `unet`. Since there might
              # be some parameters which are initialized to zero we take extra care to
              # initialize their values to something non-zero before the multiplication.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

              # Take the EMA step.
              ema_unet.step(unet_pseudo_updated_step_one.parameters()    )

              # Now the EMA'd parameters won't be equal to the original model parameters.
              UpperCamelCase     :						Any 						=		list(unet_pseudo_updated_step_one.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # Ensure this is the case when we take multiple EMA steps.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

              with tempfile.TemporaryDirectory() as tmpdir:
                     ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						List[str] 						=		loaded_unet.to(unet.device    )

              # Since no EMA step has been performed the outputs should match.
              UpperCamelCase     :						Optional[Any] 						=		unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample
              UpperCamelCase     :						List[str] 						=		loaded_unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample

              assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       atol=1e-4    )

