






'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()

@skip_mps
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = StableDiffusionPanoramaPipeline
							a          = TEXT_TO_IMAGE_PARAMS
							a          = TEXT_TO_IMAGE_BATCH_PARAMS
							a          = TEXT_TO_IMAGE_IMAGE_PARAMS
							a          = TEXT_TO_IMAGE_IMAGE_PARAMS




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														torch.manual_seed(0  )
														lowerCamelCase__      :			Any 						= UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,							layers_per_block=1    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,							cross_attention_dim=32    ,							)
														lowerCamelCase__      :			Dict 						= DDIMScheduler()
														torch.manual_seed(0  )
														lowerCamelCase__      :			int 						= 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  )
														lowerCamelCase__      :			Any 						= 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    ,							)
														lowerCamelCase__      :			Any 						= CLIPTextModel(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

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




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]=0  ):
														lowerCamelCase__      :			List[str] 						= torch.manual_seed(UpperCamelCase__  )
														lowerCamelCase__      :			str 						= {
														    """prompt""": """a photo of the dolomites""",
														    """generator""": generator,
														    # Setting height and width to None to prevent OOMs on CPU.
														    """height""": None,
														    """width""": None,
														    """num_inference_steps""": 1,
														    """guidance_scale""": 6.0,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			int 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			str 						= self.get_dummy_components()
														lowerCamelCase__      :			str 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			int 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			str 						= image[0, -3:, -3:, -1]
														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Tuple 						= np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       Any  ):
														super().test_inference_batch_consistent(batch_sizes=[1, 2]  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														super().test_inference_batch_single_identical(batch_size=2    ,							expected_max_diff=3.25e-3  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Union[str, Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Optional[Any] 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= """french fries"""
														lowerCamelCase__      :			Any 						= sd_pipe(**UpperCamelCase__    ,							negative_prompt=UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= output.images
														lowerCamelCase__      :			Optional[Any] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Optional[Any] 						= np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			List[Any] 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= sd_pipe(**UpperCamelCase__    ,							view_batch_size=2  )
														lowerCamelCase__      :			Optional[Any] 						= output.images
														lowerCamelCase__      :			str 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Any 						= np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Tuple 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Dict 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= EulerAncestralDiscreteScheduler(
														    beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule="""scaled_linear"""  )
														lowerCamelCase__      :			Dict 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Optional[int] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Dict 						= np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Dict 						= self.get_dummy_components()
														lowerCamelCase__      :			Optional[Any] 						= PNDMScheduler(
														    beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule="""scaled_linear"""    ,							skip_prk_steps=UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Optional[int] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			str 						= np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2



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




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Any=0  ):
														lowerCamelCase__      :			List[str] 						= torch.manual_seed(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= {
														    """prompt""": """a photo of the dolomites""",
														    """generator""": generator,
														    """num_inference_steps""": 3,
														    """guidance_scale""": 7.5,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			str 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			List[Any] 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Union[str, Any] 						= self.get_inputs()
														lowerCamelCase__      :			List[Any] 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Dict 						= image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2_048, 3)

														lowerCamelCase__      :			List[Any] 						= np.array(
														    [
														        0.36_968_392,
														        0.27_025_372,
														        0.32_446_766,
														        0.28_379_387,
														        0.36_363_274,
														        0.30_733_347,
														        0.27_100_027,
														        0.27_054_125,
														        0.25_536_096,
														    ]  )

														assert np.abs(expected_slice - image_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2-base"""    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			int 						= LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
														pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Union[str, Any] 						= self.get_inputs()
														lowerCamelCase__      :			Optional[Any] 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Any 						= image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2_048, 3)

														lowerCamelCase__      :			str 						= np.array(
														    [
														        [
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														        ]
														    ]  )

														assert np.abs(expected_slice - image_slice  ).max() < 1e-3




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= 0

														def callback_fn(UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       torch.FloatTensor  ) -> None:
																					lowerCamelCase__      :			int 						= True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												lowerCamelCase__      :			Any 						= latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												lowerCamelCase__      :			str 						= latents[0, -3:, -3:, -1]

																												lowerCamelCase__      :			Union[str, Any] 						= np.array(
																												    [
																												        0.18_681_869,
																												        0.33_907_816,
																												        0.5_361_276,
																												        0.14_432_865,
																												        -0.02_856_611,
																												        -0.73_941_123,
																												        0.23_397_987,
																												        0.47_322_682,
																												        -0.37_823_164,
																												    ]  )
																												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2
																					elif step == 2:
																												lowerCamelCase__      :			List[Any] 						= latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												lowerCamelCase__      :			Tuple 						= latents[0, -3:, -3:, -1]

																												lowerCamelCase__      :			int 						= np.array(
																												    [
																												        0.18_539_645,
																												        0.33_987_248,
																												        0.5_378_559,
																												        0.14_437_142,
																												        -0.02_455_261,
																												        -0.7_338_317,
																												        0.23_990_755,
																												        0.47_356_272,
																												        -0.3_786_505,
																												    ]  )

																												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2

														lowerCamelCase__      :			Optional[Any] 						= False

														lowerCamelCase__      :			Any 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			List[Any] 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Any 						= self.get_inputs()
														pipe(**UpperCamelCase__    ,							callback=UpperCamelCase__    ,							callback_steps=1  )
														assert callback_fn.has_been_called
														assert number_of_steps == 3




							def 	lowerCamelCase_       (							self:       Any  ):
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														lowerCamelCase__      :			str 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			Optional[Any] 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing(1  )
														pipe.enable_sequential_cpu_offload()

														lowerCamelCase__      :			int 						= self.get_inputs()
														lowerCamelCase__      :			Optional[Any] 						= pipe(**UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= torch.cuda.max_memory_allocated()
														# make sure that less than 5.2 GB is allocated
														assert mem_bytes < 5.5 * 10**9









'''simple docstring'''







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class _lowercase			(       _lowercase	):
							a          = """"""
							a          = (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							a          = None  # compression type in fsspec. ex: "gzip"
							a          = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




							def __init__(							self:       str    ,							UpperCamelCase__:       str = ""    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							**UpperCamelCase__:       List[Any]  ):
														super().__init__(self    ,							**UpperCamelCase__  )
														# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
														lowerCamelCase__      :			List[Any] 						= fsspec.open(
														    UpperCamelCase__    ,							mode="""rb"""    ,							protocol=UpperCamelCase__    ,							compression=self.compression    ,							client_kwargs={
														        """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
														        """trust_env""": True,  # Enable reading proxy env variables.
														        **(target_options or {}).pop("""client_kwargs"""    ,							{}  ),  # To avoid issues if it was already passed.
														    }    ,							**(target_options or {})    ,							)
														lowerCamelCase__      :			str 						= os.path.basename(self.file.path.split("""::"""  )[0]  )
														lowerCamelCase__      :			Union[str, Any] 						= (
														    self.compressed_name[: self.compressed_name.rindex("""."""  )]
														    if """.""" in self.compressed_name
														    else self.compressed_name
														)
														lowerCamelCase__      :			Tuple 						= None




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(UpperCamelCase__  ).lstrip("""/"""  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.dir_cache is None:
																					lowerCamelCase__      :			Dict 						= {**self.file.fs.info(self.file.path  ), """name""": self.uncompressed_name}
																					lowerCamelCase__      :			int 						= {f["""name"""]: f}




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str  ):
														return self.file.open().read()




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= self._strip_protocol(UpperCamelCase__  )
														if mode != "rb":
																					raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''  )
														return self.file.open()



class _lowercase			(       _lowercase	):
							a          = """bz2"""
							a          = """bz2"""
							a          = """.bz2"""



class _lowercase			(       _lowercase	):
							a          = """gzip"""
							a          = """gzip"""
							a          = """.gz"""



class _lowercase			(       _lowercase	):
							a          = """lz4"""
							a          = """lz4"""
							a          = """.lz4"""



class _lowercase			(       _lowercase	):
							a          = """xz"""
							a          = """xz"""
							a          = """.xz"""



class _lowercase			(       _lowercase	):
							a          = """zstd"""
							a          = """zstd"""
							a          = """.zst"""




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							UpperCamelCase__:       int = DEFAULT_BLOCK_SIZE    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(
														    fo=UpperCamelCase__    ,							mode=UpperCamelCase__    ,							target_protocol=UpperCamelCase__    ,							target_options=UpperCamelCase__    ,							block_size=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
														#
														# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
														#     out.close = close
														# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
														#
														# see https://github.com/intake/filesystem_spec/issues/725
														lowerCamelCase__      :			Tuple 						= self.file.__enter__

														class _lowercase			:




																					def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Any  ):
																												lowerCamelCase__      :			Optional[int] 						= file_




																					def __enter__(							self:       List[Any]  ):
																												self._file.__enter__()
																												return self




																					def __exit__(							self:       Any    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
																												self._file.__exit__(*UpperCamelCase__    ,							**UpperCamelCase__  )




																					def __iter__(							self:       Any  ):
																												return iter(self._file  )




																					def 	lowerCamelCase_       (							self:       List[Any]  ):
																												return next(self._file  )




																					def __getattr__(							self:       List[str]    ,							UpperCamelCase__:       Dict  ):
																												return getattr(self._file    ,							UpperCamelCase__  )

														def fixed_enter(*UpperCamelCase__:       Union[str, Any]    ,							**UpperCamelCase__:       List[str]  ):
																					return WrappedFile(_enter(*UpperCamelCase__    ,							**UpperCamelCase__  )  )

														lowerCamelCase__      :			Optional[Any] 						= fixed_enter

