





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   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  List[str] ,					__lowercase    :  TransformeraDModel ,					__lowercase    :  AutoencoderKL ,					__lowercase    :  KarrasDiffusionSchedulers ,					__lowercase    :  Optional[Dict[int, str]] = None ,					):






						'''simple docstring'''







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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Union[str, List[str]]		):






						'''simple docstring'''







						if not isinstance(__lowercase ,					__lowercase		):
							UpperCAmelCase_								=     list(__lowercase		)

						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    :  Union[str, Any] ,					__lowercase    :  List[int] ,					__lowercase    :  float = 4.0 ,					__lowercase    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowercase    :  int = 50 ,					__lowercase    :  Optional[str] = "pil" ,					__lowercase    :  bool = True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     len(__lowercase		)
						UpperCAmelCase_								=     self.transformer.config.sample_size
						UpperCAmelCase_								=     self.transformer.config.in_channels

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

						UpperCAmelCase_								=     torch.tensor(__lowercase ,					device=self.device		).reshape(-1		)
						UpperCAmelCase_								=     torch.tensor([10_00] * batch_size ,					device=self.device		)
						UpperCAmelCase_								=     torch.cat([class_labels, class_null] ,					0		) if guidance_scale > 1 else class_labels

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

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

							UpperCAmelCase_								=     t
							if not torch.is_tensor(__lowercase		):
								# 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+)
								UpperCAmelCase_								=     latent_model_input.device.type == """mps"""
								if isinstance(__lowercase ,					__lowercase		):
									UpperCAmelCase_								=     torch.floataa if is_mps else torch.floataa
								else:
									UpperCAmelCase_								=     torch.intaa if is_mps else torch.intaa
								UpperCAmelCase_								=     torch.tensor([timesteps] ,					dtype=__lowercase ,					device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
								UpperCAmelCase_								=     timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCAmelCase_								=     timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							UpperCAmelCase_								=     self.transformer(
							    __lowercase ,					timestep=__lowercase ,					class_labels=__lowercase		).sample

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

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

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

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

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

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

						UpperCAmelCase_								=     1 / self.vae.config.scaling_factor * latents
						UpperCAmelCase_								=     self.vae.decode(__lowercase		).sample

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

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

						if output_type == "pil":
							UpperCAmelCase_								=     self.numpy_to_pil(__lowercase		)

						if not return_dict:
							return (samples,)

						return ImagePipelineOutput(images=__lowercase		)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader(A				,      features=A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      features=A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


