



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_		:  List[Any]										=  random.Random()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[Any]=1.0			,       UpperCAmelCase__			:Any=None			,       UpperCAmelCase__			:Dict=None       ):






 '''simple docstring'''

 if rng is None:
  a							=		global_rng

 a							=		[]
 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      :							Any				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=7				, __lowerCAmelCase      :							Optional[Any]=400				, __lowerCAmelCase      :							Dict=2000				, __lowerCAmelCase      :							Dict=1				, __lowerCAmelCase      :							List[str]=0.0				, __lowerCAmelCase      :							Tuple=2_4000				, __lowerCAmelCase      :							Dict=True				, )							->					Optional[Any]:


    """simple docstring"""






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

   def 		A				(   self      :							str				)							->					Union[str, 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				, __lowerCAmelCase      :							Dict=False				, __lowerCAmelCase      :							Union[str, Any]=False				)							->					List[Any]:


    """simple docstring"""






    def _flatten(__lowerCAmelCase      :							Dict				):
     return list(itertools.chain(*__lowerCAmelCase				)				)

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

    return audio_inputs



@require_torch
class 		_lowercase		(							UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      EncodecFeatureExtractor

   def 		A				(   self      :							List[Any]				)							->					int:


    """simple docstring"""






    a							=		EnCodecFeatureExtractionTester(self				)

   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






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

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

    # Test batched
    a							=		feat_extract(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="np"				).input_values
    a							=		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 		A				(   self      :							Union[str, Any]				)							->					Union[str, Any]:


    """simple docstring"""






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

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

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							str				)							->					int:


    """simple docstring"""






    from datasets import load_dataset

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

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

   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






    a							=		torch.tensor(
        [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
         3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
         2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
         4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
         7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
         4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]				)
    # fmt: on
    a							=		self._load_datasamples(1				)
    a							=		EncodecFeatureExtractor()
    a							=		feature_extractor(__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(input_values.shape				, (1, 1, 9_3680)				)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]				, __lowerCAmelCase				, atol=1E-6				)				)

   def 		A				(   self      :							Dict				)							->					Optional[Any]:


    """simple docstring"""






    a							=		torch.tensor(
        [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
         3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
         2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
         4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
         7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
         4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]				)
    # fmt: on
    a							=		self._load_datasamples(1				)
    a							=		[np.tile(input_audio[0][None]				, reps=(2, 1)				)]
    input_audio[0][1] *= 0.5
    a							=		EncodecFeatureExtractor(feature_size=2				)
    a							=		feature_extractor(__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(input_values.shape				, (1, 2, 9_3680)				)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]				, __lowerCAmelCase				, atol=1E-6				)				)
    self.assertTrue(torch.allclose(input_values[0, 1, :30]				, EXPECTED_INPUT_VALUES * 0.5				, atol=1E-6				)				)




   def 		A				(   self      :							int				)							->					List[str]:


    """simple docstring"""






    a							=		self._load_datasamples(2				)
    # would be easier if the stride was like
    a							=		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.$"				, ):
     a							=		feature_extractor(
         __lowerCAmelCase				, padding="max_length"				, truncation=__lowerCAmelCase				, return_tensors="pt"				).input_values

    # truncate to chunk
    a							=		feature_extractor(__lowerCAmelCase				, truncation=__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (2, 1, 7_1520)				)  # 2 chunks

    # force truncate to max_length
    a							=		feature_extractor(
        __lowerCAmelCase				, truncation=__lowerCAmelCase				, max_length=4_8000				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (2, 1, 4_8000)				)

    # pad to chunk
    a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(padded_outputs.shape				, (2, 1, 9_5280)				)

    # pad to chunk
    a							=		feature_extractor(__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (2, 1, 9_5280)				)

    # force pad to max length
    a							=		feature_extractor(
        __lowerCAmelCase				, padding="max_length"				, max_length=10_0000				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (2, 1, 10_0000)				)

    # 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.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

    a							=		feature_extractor(input_audio[0]				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (1, 1, 9_3680)				)

    # no pad if no chunk_length_s
    a							=		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.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

    a							=		feature_extractor(input_audio[0]				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (1, 1, 9_3680)				)

    # no pad if no overlap
    a							=		2
    a							=		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.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

    a							=		feature_extractor(input_audio[0]				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(truncated_outputs.shape				, (1, 1, 9_3680)				)









'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             super().__init__()

             self.register_modules(vae=_UpperCAmelCase  ,		text_encoder=_UpperCAmelCase  ,		tokenizer=_UpperCAmelCase  ,		unet=_UpperCAmelCase  ,		scheduler=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with torch.no_grad():
                    UpperCAmelCase__	:   Dict												=	self.vae.encode(tfms.ToTensor()(_UpperCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                    UpperCAmelCase__	:   int												=	0.1_82_15 * latent.latent_dist.sample()
             return latent




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	(1 / 0.1_82_15) * latent
             with torch.no_grad():
                    UpperCAmelCase__	:   Optional[Any]												=	self.vae.decode(_UpperCAmelCase						).sample
             UpperCAmelCase__	:   List[str]												=	(img / 2 + 0.5).clamp(0  ,		1						)
             UpperCAmelCase__	:   List[Any]												=	img.detach().cpu().permute(0  ,		2  ,		3  ,		1						).numpy()
             UpperCAmelCase__	:   Union[str, Any]												=	(img * 255).round().astype('''uint8'''						)
             return Image.fromarray(img[0]						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 _UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   Tuple												=	self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 ''''''  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   List[Any]												=	self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 0.3  ,		_UpperCAmelCase = 0.6  ,		_UpperCAmelCase = 0.5  ,		_UpperCAmelCase = 42  ,		_UpperCAmelCase = 50  ,		_UpperCAmelCase = 7.5  ,		):
             UpperCAmelCase__	:   Any												=	steps - int(kmin * steps						)
             UpperCAmelCase__	:   Any												=	steps - int(kmax * steps						)

             UpperCAmelCase__	:   List[str]												=	self.prep_text(_UpperCAmelCase						)

             self.scheduler.set_timesteps(_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[str]												=	img.size
             UpperCAmelCase__	:   str												=	self.encode(_UpperCAmelCase						)

             torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)  ,		).to(self.device						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler.add_noise(
                 _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=self.scheduler.timesteps[tmax]  ,		)

             UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

             UpperCAmelCase__	:   Dict												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]						)

             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	self.unet(
                        _UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	pred.chunk(2						)
             UpperCAmelCase__	:   List[str]												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		_UpperCAmelCase						).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  UpperCAmelCase__	:   Dict												=	self.scheduler.add_noise(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=_UpperCAmelCase  ,		)

                                  UpperCAmelCase__	:   Union[str, Any]												=	(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  UpperCAmelCase__	:   Tuple												=	torch.cat([input] * 2						)

                           else:  # content generation phase
                                  UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

                           UpperCAmelCase__	:   str												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		_UpperCAmelCase						)

                           with torch.no_grad():
                                  UpperCAmelCase__	:   Any												=	self.unet(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

                           UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	pred.chunk(2						)
                           UpperCAmelCase__	:   str												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return self.decode(_UpperCAmelCase						)