




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase				:		int     =   random.Random()

if is_torch_available():
 import torch

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]=1.0	,							lowercase	:       Union[str, Any]=None	,							lowercase	:       Optional[int]=None     ):


      '''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







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  Optional[int]=7						,							A_					:  Tuple=400						,							A_					:  Union[str, Any]=2000						,							A_					:  Optional[int]=1						,							A_					:  Optional[int]=0.0						,							A_					:  Any=16000						,							A_					:  Dict=True						,							A_					:  Dict=True						,							)   ->	Tuple:

       """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
       lowerCamelCase_		     =    do_normalize

 def 				a__ (				self					:  Tuple  )   ->	str:

       """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,
           "do_normalize": self.do_normalize,
       }





 def 				a__ (				self					:  Any						,							A_					:  Union[str, Any]=False						,							A_					:  Dict=False  )   ->	Tuple:

       """simple docstring"""






       def _flatten(A_					:  int  ):
             return list(itertools.chain(*A_  )  )

       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(A_  ) for x in speech_inputs]

       return speech_inputs







@require_torch
@require_torchaudio
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						ASTFeatureExtractor

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    ASTFeatureExtractionTester(self  )

 def 				a__ (				self					:  List[str]  )   ->	Union[str, 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(A_  ) for speech_input in speech_inputs]

       # Test not batched input
       lowerCamelCase_		     =    feat_extract(speech_inputs[0]						,							return_tensors='np'  ).input_values
       lowerCamelCase_		     =    feat_extract(np_speech_inputs[0]						,							return_tensors='np'  ).input_values
       self.assertTrue(np.allclose(A_						,							A_						,							atol=1E-3  )  )

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

       # Test 2-D numpy arrays are batched.
       lowerCamelCase_		     =    [floats_list((1, x)  )[0] for x in (800, 800, 800)]
       lowerCamelCase_		     =    np.asarray(A_  )
       lowerCamelCase_		     =    feat_extract(A_						,							return_tensors='np'  ).input_values
       lowerCamelCase_		     =    feat_extract(A_						,							return_tensors='np'  ).input_values
       for enc_seq_a, enc_seq_a in zip(A_						,							A_  ):
             self.assertTrue(np.allclose(A_						,							A_						,							atol=1E-3  )  )

 @require_torch
 def 				a__ (				self					:  int  )   ->	Optional[Any]:

       """simple docstring"""






       import torch

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

       for inputs in [py_speech_inputs, np_speech_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 				a__ (				self					:  List[Any]						,							A_					:  Optional[int]  )   ->	str:

       """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(A_  )  )[:num_samples]['audio']

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





 @require_torch
 def 				a__ (				self					:  Optional[int]  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    torch.tensor(
           [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
            -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
            -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
            -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]  )
       # fmt: on

       lowerCamelCase_		     =    self._load_datasamples(1  )
       lowerCamelCase_		     =    ASTFeatureExtractor()
       lowerCamelCase_		     =    feature_extractor(A_						,							return_tensors='pt'  ).input_values
       self.assertEquals(input_values.shape						,							(1, 1024, 128)  )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]						,							A_						,							atol=1E-4  )  )









from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



