



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _A	(     __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,unittest.TestCase   ):
       UpperCamelCase__  :   str						 =						StableDiffusionSAGPipeline
       UpperCamelCase__  :   Dict						 =						TEXT_TO_IMAGE_PARAMS
       UpperCamelCase__  :   List[Any]						 =						TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCamelCase__  :   Optional[int]						 =						TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase__  :   List[Any]						 =						TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase__  :   int						 =						False



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    UNetaDConditionModel(
               block_out_channels=(32, 64)					,		layers_per_block=2					,		sample_size=32					,		in_channels=4					,		out_channels=4					,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,		cross_attention_dim=32					,		)
           __a							=    DDIMScheduler(
               beta_start=0.0_00_85					,		beta_end=0.0_12					,		beta_schedule='''scaled_linear'''					,		clip_sample=__UpperCAmelCase					,		set_alpha_to_one=__UpperCAmelCase					,		)
           torch.manual_seed(0)
           __a							=    AutoencoderKL(
               block_out_channels=[32, 64]					,		in_channels=3					,		out_channels=3					,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,		latent_channels=4					,		)
           torch.manual_seed(0)
           __a							=    CLIPTextConfig(
               bos_token_id=0					,		eos_token_id=2					,		hidden_size=32					,		intermediate_size=37					,		layer_norm_eps=1E-05					,		num_attention_heads=4					,		num_hidden_layers=5					,		pad_token_id=1					,		vocab_size=1_000					,		)
           __a							=    CLIPTextModel(__UpperCAmelCase)
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           __a							=    {
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }
           return components



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]=0):


           '''simple docstring'''

           if str(__UpperCAmelCase).startswith('''mps'''):
               __a							=    torch.manual_seed(__UpperCAmelCase)
           else:
               __a							=    torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
           __a							=    {
               """prompt""": """.""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 1.0,
               """sag_scale""": 1.0,
               """output_type""": """numpy""",
           }
           return inputs





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           super().test_inference_batch_single_identical(expected_max_diff=3E-3)


@slow
@require_torch_gpu
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
           __a							=    sag_pipe.to(__UpperCAmelCase)
           sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

           __a							=    """."""
           __a							=    torch.manual_seed(0)
           __a							=    sag_pipe(
               [prompt]					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type='''np''')

           __a							=    output.images

           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
           __a							=    sag_pipe.to(__UpperCAmelCase)
           sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

           __a							=    """."""
           __a							=    torch.manual_seed(0)
           __a							=    sag_pipe(
               [prompt]					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type='''np''')

           __a							=    output.images

           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
           __a							=    sag_pipe.to(__UpperCAmelCase)
           sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

           __a							=    """."""
           __a							=    torch.manual_seed(0)
           __a							=    sag_pipe(
               [prompt]					,		width=768					,		height=512					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type='''np'''					,		)

           __a							=    output.images

           assert image.shape == (1, 512, 768, 3)










import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					NestedDataStructureLike[PathLike]					,		__SCREAMING_SNAKE_CASE							:					Optional[NamedSplit] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[Features] = None					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					Any					,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE					,		split=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		cache_dir=__SCREAMING_SNAKE_CASE					,		keep_in_memory=__SCREAMING_SNAKE_CASE					,		streaming=__SCREAMING_SNAKE_CASE					,		num_proc=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    field
           __a							=    path_or_paths if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a							=    Json(
               cache_dir=__SCREAMING_SNAKE_CASE					,		data_files=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		field=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           if self.streaming:
               __a							=    self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a							=    None
               __a							=    None
               __a							=    None
               __a							=    None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE					,		download_mode=__SCREAMING_SNAKE_CASE					,		verification_mode=__SCREAMING_SNAKE_CASE					,		base_path=__SCREAMING_SNAKE_CASE					,		num_proc=self.num_proc					,		)
               __a							=    self.builder.as_dataset(
                   split=self.split					,		verification_mode=__SCREAMING_SNAKE_CASE					,		in_memory=self.keep_in_memory)
           return dataset


class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dataset					,		__SCREAMING_SNAKE_CASE							:					Union[PathLike, BinaryIO]					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

           __a							=    dataset
           __a							=    path_or_buf
           __a							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           __a							=    num_proc
           __a							=    '''utf-8'''
           __a							=    to_json_kwargs



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.to_json_kwargs.pop('''path_or_buf'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.to_json_kwargs.pop('''orient'''					,		'''records''')
           __a							=    self.to_json_kwargs.pop('''lines'''					,		True if orient == '''records''' else False)
           __a							=    self.to_json_kwargs.pop('''index'''					,		False if orient in ['''split''', '''table'''] else True)
           __a							=    self.to_json_kwargs.pop('''compression'''					,		__SCREAMING_SNAKE_CASE)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

           if isinstance(self.path_or_buf					,		(str, bytes, os.PathLike)):
               with fsspec.open(self.path_or_buf					,		'''wb'''					,		compression=__SCREAMING_SNAKE_CASE) as buffer:
                   __a							=    self._write(file_obj=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           else:
               if compression:
                   raise NotImplementedError(
                       F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                       ''' was passed. Please provide a local path instead.''')
               __a							=    self._write(
                   file_obj=self.path_or_buf					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           return written



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a	,       __a							=    args

           __a							=    query_table(
               table=self.dataset.data					,		key=slice(__SCREAMING_SNAKE_CASE					,		offset + self.batch_size)					,		indices=self.dataset._indices					,		)
           __a							=    batch.to_pandas().to_json(
               path_or_buf=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           if not json_str.endswith('''\n'''):
               json_str += "\n"
           return json_str.encode(self.encoding)





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					BinaryIO					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		):


           '''simple docstring'''

           __a							=    0

           if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0					,		len(self.dataset)					,		self.batch_size)					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                   __a							=    self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(__SCREAMING_SNAKE_CASE)
           else:
               __a	,       __a							=    len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json					,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)]					,		)					,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                       written += file_obj.write(__SCREAMING_SNAKE_CASE)

           return written





