


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCAmelCase       :       Optional[Any]       	= logging.get_logger(__name__)  # pylint: disable=invalid-name



class       UpperCAmelCase_		(							__snake_case):
       '''simple docstring'''
       __UpperCamelCase					:   List[Any]							=    ["safety_checker", "feature_extractor"]






       def __init__(   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE = True       ,    ):




              """simple docstring"""




              super().__init__()
              if hasattr(scheduler.config       ,    '''steps_offset'''  ) and scheduler.config.steps_offset != 1:
                     UpperCamelCase			:		Union[str, Any]      		=						(
                         f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
                         f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
                         "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                         " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                         " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                         " file"
                     )
                     deprecate('''steps_offset!=1'''       ,    '''1.0.0'''       ,    lowercase_       ,    standard_warn=lowercase_  )
                     UpperCamelCase			:		List[str]      		=						dict(scheduler.config  )
                     UpperCamelCase			:		Any      		=						1
                     UpperCamelCase			:		Dict      		=						FrozenDict(lowercase_  )

              self.register_modules(
                  vae=lowercase_       ,    text_encoder=lowercase_       ,    tokenizer=lowercase_       ,    unet=lowercase_       ,    scheduler=lowercase_       ,    safety_checker=lowercase_       ,    feature_extractor=lowercase_       ,    )
              self.register_to_config(requires_safety_checker=lowercase_  )






       @property
       def        _lowercase      (   self  ):




              """simple docstring"""




              return {k: getattr(self       ,    lowercase_  ) for k in self.config.keys() if not k.startswith('''_'''  )}






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE = "auto"  ):




              """simple docstring"""




              if slice_size == "auto":
                     # half the attention head size is usually a good trade-off between
                     # speed and memory
                     UpperCamelCase			:		str      		=						self.unet.config.attention_head_dim // 2
              self.unet.set_attention_slice(lowercase_  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              self.enable_attention_slicing(lowercase_  )






       @torch.no_grad()
       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE = 0.8       ,    __SCREAMING_SNAKE_CASE = 50       ,    __SCREAMING_SNAKE_CASE = 7.5       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    __SCREAMING_SNAKE_CASE = 0.0       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = "pil"       ,    __SCREAMING_SNAKE_CASE = True       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    ):




              """simple docstring"""




              return StableDiffusionInpaintPipelineLegacy(**self.components  )(
                  prompt=lowercase_       ,    image=lowercase_       ,    mask_image=lowercase_       ,    strength=lowercase_       ,    num_inference_steps=lowercase_       ,    guidance_scale=lowercase_       ,    negative_prompt=lowercase_       ,    num_images_per_prompt=lowercase_       ,    eta=lowercase_       ,    generator=lowercase_       ,    output_type=lowercase_       ,    return_dict=lowercase_       ,    callback=lowercase_       ,    )






       @torch.no_grad()
       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE = 0.8       ,    __SCREAMING_SNAKE_CASE = 50       ,    __SCREAMING_SNAKE_CASE = 7.5       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    __SCREAMING_SNAKE_CASE = 0.0       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = "pil"       ,    __SCREAMING_SNAKE_CASE = True       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    **__SCREAMING_SNAKE_CASE       ,    ):




              """simple docstring"""




              return StableDiffusionImgaImgPipeline(**self.components  )(
                  prompt=lowercase_       ,    image=lowercase_       ,    strength=lowercase_       ,    num_inference_steps=lowercase_       ,    guidance_scale=lowercase_       ,    negative_prompt=lowercase_       ,    num_images_per_prompt=lowercase_       ,    eta=lowercase_       ,    generator=lowercase_       ,    output_type=lowercase_       ,    return_dict=lowercase_       ,    callback=lowercase_       ,    callback_steps=lowercase_       ,    )






       @torch.no_grad()
       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE = 512       ,    __SCREAMING_SNAKE_CASE = 512       ,    __SCREAMING_SNAKE_CASE = 50       ,    __SCREAMING_SNAKE_CASE = 7.5       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    __SCREAMING_SNAKE_CASE = 0.0       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = "pil"       ,    __SCREAMING_SNAKE_CASE = True       ,    __SCREAMING_SNAKE_CASE = None       ,    __SCREAMING_SNAKE_CASE = 1       ,    ):




              """simple docstring"""




              return StableDiffusionPipeline(**self.components  )(
                  prompt=lowercase_       ,    height=lowercase_       ,    width=lowercase_       ,    num_inference_steps=lowercase_       ,    guidance_scale=lowercase_       ,    negative_prompt=lowercase_       ,    num_images_per_prompt=lowercase_       ,    eta=lowercase_       ,    generator=lowercase_       ,    latents=lowercase_       ,    output_type=lowercase_       ,    return_dict=lowercase_       ,    callback=lowercase_       ,    callback_steps=lowercase_       ,    )


import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize("shards_per_node"					,			[1, 2, 3]						)
def 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						def gen(__lowercase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       Dict:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@pytest.mark.parametrize("streaming"					,			[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)

@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    True
						_snake_case						:     Optional[int]				=    get_torch_dist_unique_port()
						_snake_case						:     List[str]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     List[str]				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     Tuple				=    F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
						_snake_case						:     Any				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)