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    snake_case       (   UpperCamelCase_    ):




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




       """simple docstring"""
       super().__init__()
       self.register_modules(transformer=a_  ,			vae=a_  ,			scheduler=a_					)

       # create a imagenet -> id dictionary for easier use
       SCREAMING_SNAKE_CASE__					:					Dict      =		{}
       if idalabel is not None:
            for key, value in idalabel.items():
                 for label in value.split(','					):
                      SCREAMING_SNAKE_CASE__					:					List[Any]      =		int(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		dict(sorted(self.labels.items()					)					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						Union[str, List[str]]					)->							List[int]:




       """simple docstring"""

       if not isinstance(a_  ,			a_					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		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  :						Optional[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"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.config.in_channels

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)  ,			generator=a_  ,			device=self.device  ,			dtype=self.transformer.dtype  ,			)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.cat([latents] * 2					) if guidance_scale > 1 else latents

       SCREAMING_SNAKE_CASE__					:					int      =		torch.tensor(a_  ,			device=self.device					).reshape(-1					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.tensor([1000] * batch_size  ,			device=self.device					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		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:
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		latent_model_input[: len(a_					) // 2]
                 SCREAMING_SNAKE_CASE__					:					Dict      =		torch.cat([half, half]  ,			dim=0					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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+)
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		latent_model_input.device.type == 'mps'
                 if isinstance(a_  ,			a_					):
                      SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.floataa if is_mps else torch.floataa
                 else:
                      SCREAMING_SNAKE_CASE__					:					int      =		torch.intaa if is_mps else torch.intaa
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.tensor([timesteps]  ,			dtype=a_  ,			device=latent_model_input.device					)
            elif len(timesteps.shape					) == 0:
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		timesteps[None].to(latent_model_input.device					)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            SCREAMING_SNAKE_CASE__					:					List[str]      =		timesteps.expand(latent_model_input.shape[0]					)
            # predict noise model_output
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer(
                a_  ,			timestep=a_  ,			class_labels=a_					).sample

            # perform guidance
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.split(a_  ,			len(a_					) // 2  ,			dim=0					)

                 SCREAMING_SNAKE_CASE__					:					str      =		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                 SCREAMING_SNAKE_CASE__					:					Any      =		torch.cat([half_eps, half_eps]  ,			dim=0					)

                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.cat([eps, rest]  ,			dim=1					)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		torch.split(a_  ,			a_  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					int      =		noise_pred

            # compute previous image: x_t -> x_t-1
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.scheduler.step(a_  ,			a_  ,			a_					).prev_sample

       if guidance_scale > 1:
            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		latent_model_input.chunk(2  ,			dim=0					)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		latent_model_input

       SCREAMING_SNAKE_CASE__					:					List[str]      =		1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.vae.decode(a_					).sample

       SCREAMING_SNAKE_CASE__					:					List[str]      =		(samples / 2 + 0.5).clamp(0  ,			1					)

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

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.numpy_to_pil(a_					)

       if not return_dict:
            return (samples,)

       return ImagePipelineOutput(images=a_					)








'''simple docstring'''

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

a							   =				random.Random()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1.0				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					List[str]:





			"""simple docstring"""







			if rng is None:
						snake_case:    Dict      							=global_rng

			snake_case:    Tuple      							=[]
			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    ):


							def __init__(				self   :						int      ,						a_   :						Any      ,						a_   :						str=7      ,						a_   :						Tuple=4_0_0      ,						a_   :						List[Any]=2_0_0_0      ,						a_   :						str=2_0_4_8      ,						a_   :						List[str]=1_2_8      ,						a_   :						int=1      ,						a_   :						Tuple=5_1_2      ,						a_   :						Dict=3_0      ,						a_   :						Optional[int]=4_4_1_0_0      ,						)			->					Union[str, Any]:
										snake_case:    Union[str, Any]      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    Union[str, Any]      							=min_seq_length
										snake_case:    List[str]      							=max_seq_length
										snake_case:    str      							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case:    int      							=spectrogram_length
										snake_case:    List[str]      							=feature_size
										snake_case:    Dict      							=num_audio_channels
										snake_case:    int      							=hop_length
										snake_case:    List[Any]      							=chunk_length
										snake_case:    Optional[Any]      							=sampling_rate


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Optional[Any]=False      ,						a_   :						Tuple=False			)			->					Optional[int]:
										def _flatten(a_   :						Dict			):
													return list(itertools.chain(*a_			)			)

										if equal_length:
													snake_case:    Union[str, Any]      							=[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
										else:
													# make sure that inputs increase in size
													snake_case:    Union[str, Any]      							=[
													    floats_list((x, self.feature_size)			)
													    for x in range(self.min_seq_length      ,						self.max_seq_length      ,						self.seq_length_diff			)
													]
										if numpify:
													snake_case:    Any      							=[np.asarray(a_			) for x in speech_inputs]
										return speech_inputs






@require_torch
@require_torchaudio
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Optional[Any]        =				TvltFeatureExtractor


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Union[str, Any]      							=TvltFeatureExtractionTester(self			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					str:
										snake_case:    int      							=self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(a_      ,						'spectrogram_length'			)			)
										self.assertTrue(hasattr(a_      ,						'feature_size'			)			)
										self.assertTrue(hasattr(a_      ,						'num_audio_channels'			)			)
										self.assertTrue(hasattr(a_      ,						'hop_length'			)			)
										self.assertTrue(hasattr(a_      ,						'chunk_length'			)			)
										self.assertTrue(hasattr(a_      ,						'sampling_rate'			)			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[int]:
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    Optional[Any]      							=feat_extract_first.save_pretrained(a_			)[0]
													check_json_file_has_correct_format(a_			)
													snake_case:    str      							=self.feature_extraction_class.from_pretrained(a_			)

										snake_case:    Optional[int]      							=feat_extract_first.to_dict()
										snake_case:    List[str]      							=feat_extract_second.to_dict()
										snake_case:    Optional[int]      							=dict_first.pop('mel_filters'			)
										snake_case:    List[Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    Any      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    int      							=os.path.join(a_      ,						'feat_extract.json'			)
													feat_extract_first.to_json_file(a_			)
													snake_case:    Union[str, Any]      							=self.feature_extraction_class.from_json_file(a_			)

										snake_case:    Optional[Any]      							=feat_extract_first.to_dict()
										snake_case:    Optional[Any]      							=feat_extract_second.to_dict()
										snake_case:    Union[str, Any]      							=dict_first.pop('mel_filters'			)
										snake_case:    Union[str, Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# Initialize feature_extractor
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										# create three inputs of length 800, 1000, and 1200
										snake_case:    Any      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Optional[Any]      							=[np.asarray(a_			) for speech_input in speech_inputs]

										# Test not batched input
										snake_case:    List[Any]      							=feature_extractor(np_speech_inputs[0]      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test batched
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test audio masking
										snake_case:    Optional[Any]      							=feature_extractor(
										    a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0      ,						mask_audio=a_			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test 2-D numpy arrays are batched.
										snake_case:    List[Any]      							=[floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										snake_case:    List[str]      							=np.asarray(a_			)
										snake_case:    str      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values
										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						str			)			->					Union[str, Any]:
										snake_case:    Dict      							=load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,						'clean'      ,						split='validation'			)
										# automatic decoding with librispeech
										snake_case:    Optional[int]      							=ds.sort('id'			).select(range(a_			)			)[:num_samples]['audio']

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										snake_case:    Tuple      							=self._load_datasamples(1			)
										snake_case:    Any      							=TvltFeatureExtractor()
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='pt'			).audio_values

										self.assertEquals(audio_values.shape      ,						(1, 1, 1_9_2, 1_2_8)			)

										snake_case:    int      							=torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]			)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,						a_      ,						atol=1E-4			)			)




