



import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class 	lowerCamelCase__ (		unittest.TestCase   ):


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()


						@property
						def   _lowerCamelCase			( self						:  str			):
													a__:       str											=1
													a__:       Optional[Any]											=3
													a__:       int											=(3_2, 3_2)

													a__:       Union[str, Any]											=floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0			)			).to(_a			)
													return image


						@property
						def   _lowerCamelCase			( self						:  Optional[Any]			):
													torch.manual_seed(0			)
													a__:       str											=UNetaDConditionModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       sample_size=3_2      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,       cross_attention_dim=3_2      ,       )
													return model


						@property
						def   _lowerCamelCase			( self						:  int			):
													torch.manual_seed(0			)
													a__:       Dict											=AutoencoderKL(
													    block_out_channels=[3_2, 6_4]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       )
													return model


						@property
						def   _lowerCamelCase			( self						:  Optional[Any]			):
													torch.manual_seed(0			)
													a__:       Dict											=CLIPTextConfig(
													    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=3_2      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )
													return CLIPTextModel(_a			)


						@property
						def   _lowerCamelCase			( self						:  Dict			):
													def extract(*_a						:  List[str]      ,       **_a						:  Dict			):


																				class 	lowerCamelCase__ :


																										def __init__( self						:  Dict			):
																																	a__:       Dict											=torch.ones([0]			)







																										def   _lowerCamelCase			( self						:  int      ,       _a						:  Optional[Any]			):
																																	self.pixel_values.to(_a			)
																																	return self


																				return Out()

													return extract


						def   _lowerCamelCase			( self						:  int			):
													a__:       List[Any]											="cpu"  # ensure determinism for the device-dependent torch.Generator
													a__:       int											=self.dummy_cond_unet
													a__:       Optional[Any]											=DDIMScheduler(
													    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="scaled_linear"      ,       clip_sample=_a      ,       set_alpha_to_one=_a      ,       )

													a__:       Optional[Any]											=self.dummy_vae
													a__:       Optional[Any]											=self.dummy_text_encoder
													a__:       str											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													# make sure here that pndm scheduler skips prk
													a__:       int											=StableDiffusionPipeline(
													    unet=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       safety_checker=_a      ,       feature_extractor=self.dummy_extractor      ,       )
													a__:       Union[str, Any]											=sd_pipe.to(_a			)
													sd_pipe.set_progress_bar_config(disable=_a			)

													a__:       Optional[int]											="A painting of a squirrel eating a burger"

													a__:       Tuple											=torch.Generator(device=_a			).manual_seed(0			)
													a__:       List[Any]											=sd_pipe([prompt]      ,       generator=_a      ,       guidance_scale=6.0      ,       num_inference_steps=2      ,       output_type="np"			)
													a__:       List[Any]											=output.images

													a__:       List[Any]											=torch.Generator(device=_a			).manual_seed(0			)
													a__:       str											=sd_pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=6.0      ,       num_inference_steps=2      ,       output_type="np"      ,       return_dict=_a      ,       )[0]

													a__:       Optional[int]											=image[0, -3:, -3:, -1]
													a__:       Any											=image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 6_4, 6_4, 3)
													a__:       Union[str, Any]											=np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]			)

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


						def   _lowerCamelCase			( self						:  int			):
													a__:       Optional[Any]											="cpu"  # ensure determinism for the device-dependent torch.Generator
													a__:       Optional[int]											=self.dummy_cond_unet
													a__:       Optional[Any]											=PNDMScheduler(skip_prk_steps=_a			)
													a__:       Optional[int]											=self.dummy_vae
													a__:       str											=self.dummy_text_encoder
													a__:       Optional[int]											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													# make sure here that pndm scheduler skips prk
													a__:       Dict											=StableDiffusionPipeline(
													    unet=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       safety_checker=_a      ,       feature_extractor=self.dummy_extractor      ,       )
													a__:       Optional[Any]											=sd_pipe.to(_a			)
													sd_pipe.set_progress_bar_config(disable=_a			)

													a__:       str											="A painting of a squirrel eating a burger"
													a__:       Any											=torch.Generator(device=_a			).manual_seed(0			)
													a__:       str											=sd_pipe([prompt]      ,       generator=_a      ,       guidance_scale=6.0      ,       num_inference_steps=2      ,       output_type="np"			)

													a__:       Tuple											=output.images

													a__:       Tuple											=torch.Generator(device=_a			).manual_seed(0			)
													a__:       Optional[Any]											=sd_pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=6.0      ,       num_inference_steps=2      ,       output_type="np"      ,       return_dict=_a      ,       )[0]

													a__:       Dict											=image[0, -3:, -3:, -1]
													a__:       Optional[int]											=image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 6_4, 6_4, 3)
													a__:       Tuple											=np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]			)

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


						def   _lowerCamelCase			( self						:  List[Any]			):
													a__:       Union[str, Any]											=StableDiffusionPipeline.from_pretrained(
													    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"      ,       safety_checker=_a			)
													assert isinstance(_a      ,       _a			)
													assert isinstance(pipe.scheduler      ,       _a			)
													assert pipe.safety_checker is None

													a__:       int											=pipe("example prompt"      ,       num_inference_steps=2			).images[0]
													assert image is not None

													# check that there's no error when saving a pipeline with one of the models being None
													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(_a			)
																				a__:       Optional[int]											=StableDiffusionPipeline.from_pretrained(_a			)

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													a__:       List[Any]											=pipe("example prompt"      ,       num_inference_steps=2			).images[0]
													assert image is not None







						@unittest.skipIf(torch_device != "cuda"      ,       "This test requires a GPU"			)
						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       Any											=self.dummy_cond_unet
													a__:       Dict											=PNDMScheduler(skip_prk_steps=_a			)
													a__:       str											=self.dummy_vae
													a__:       Union[str, Any]											=self.dummy_text_encoder
													a__:       Dict											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													# put models in fp16
													a__:       List[str]											=unet.half()
													a__:       Union[str, Any]											=vae.half()
													a__:       int											=bert.half()

													# make sure here that pndm scheduler skips prk
													a__:       List[str]											=StableDiffusionPipeline(
													    unet=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       safety_checker=_a      ,       feature_extractor=self.dummy_extractor      ,       )
													a__:       Optional[int]											=sd_pipe.to(_a			)
													sd_pipe.set_progress_bar_config(disable=_a			)

													a__:       str											="A painting of a squirrel eating a burger"
													a__:       Optional[int]											=sd_pipe([prompt]      ,       num_inference_steps=2      ,       output_type="np"			).images

													assert image.shape == (1, 6_4, 6_4, 3)


@nightly
@require_torch_gpu
class 	lowerCamelCase__ (		unittest.TestCase   ):


						def   _lowerCamelCase			( self						:  Any			):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()


						def   _lowerCamelCase			( self						:  int			):
													a__:       Any											="cuda"
													a__:       Optional[Any]											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
													a__:       int											=pipe.to(_a			)
													pipe.set_progress_bar_config(disable=_a			)

													a__:       List[str]											="a photo of a cat"
													a__:       Union[str, Any]											={
													    "editing_prompt": ["sunglasses"],
													    "reverse_editing_direction": [False],
													    "edit_warmup_steps": 1_0,
													    "edit_guidance_scale": 6,
													    "edit_threshold": 0.9_5,
													    "edit_momentum_scale": 0.5,
													    "edit_mom_beta": 0.6,
													}

													a__:       Union[str, Any]											=3
													a__:       Tuple											=7

													# no sega enabled
													a__:       List[Any]											=torch.Generator(_a			)
													generator.manual_seed(_a			)
													a__:       Optional[Any]											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       )

													a__:       int											=output.images
													a__:       List[Any]											=image[0, -3:, -3:, -1]
													a__:       Union[str, Any]											=[
													    0.3_4_6_7_3_1_1_3,
													    0.3_8_4_9_2_7_3_3,
													    0.3_7_5_9_7_3_5_2,
													    0.3_4_0_8_6_3_3_5,
													    0.3_5_6_5_0_7_4_8,
													    0.3_5_5_7_9_2_0_5,
													    0.3_3_8_4_7_6_3,
													    0.3_4_3_4_0_2_3_6,
													    0.3_5_7_3_2_7_1,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_a			)
													a__:       Any											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       **_a      ,       )

													a__:       Union[str, Any]											=output.images
													a__:       List[str]											=image[0, -3:, -3:, -1]
													a__:       List[str]											=[
													    0.4_1_8_8_7_8_2_6,
													    0.3_7_7_2_8_7_6_6,
													    0.3_0_1_3_8_2_7_2,
													    0.4_1_4_1_6_3_3_5,
													    0.4_1_6_6_4_9_8_5,
													    0.3_6_2_8_3_3_9_2,
													    0.3_6_1_9_1_2_4_6,
													    0.4_3_3_6_4_4_6_5,
													    0.4_3_0_0_1_7_3_2,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


						def   _lowerCamelCase			( self						:  int			):
													a__:       Optional[Any]											="cuda"
													a__:       Optional[Any]											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
													a__:       Optional[Any]											=pipe.to(_a			)
													pipe.set_progress_bar_config(disable=_a			)

													a__:       Optional[Any]											="an image of a crowded boulevard, realistic, 4k"
													a__:       Tuple											={
													    "editing_prompt": "crowd, crowded, people",
													    "reverse_editing_direction": True,
													    "edit_warmup_steps": 1_0,
													    "edit_guidance_scale": 8.3,
													    "edit_threshold": 0.9,
													    "edit_momentum_scale": 0.5,
													    "edit_mom_beta": 0.6,
													}

													a__:       Tuple											=9
													a__:       int											=7

													# no sega enabled
													a__:       int											=torch.Generator(_a			)
													generator.manual_seed(_a			)
													a__:       str											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       )

													a__:       List[Any]											=output.images
													a__:       List[Any]											=image[0, -3:, -3:, -1]
													a__:       int											=[
													    0.4_3_4_9_7_9_9_8,
													    0.9_1_8_1_4_0_6_5,
													    0.7_5_4_0_7_3_9,
													    0.5_5_5_8_0_2_0_5,
													    0.8_4_6_7_2_6_5,
													    0.5_3_8_9_6_9_1,
													    0.6_2_5_7_4_5_0_6,
													    0.5_8_8_9_7_7_6_3,
													    0.5_0_9_2_6_7_5_7,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_a			)
													a__:       str											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       **_a      ,       )

													a__:       Union[str, Any]											=output.images
													a__:       str											=image[0, -3:, -3:, -1]
													a__:       Optional[Any]											=[
													    0.3_0_8_9_7_1_9,
													    0.3_0_5_0_0_1_4_4,
													    0.2_9_0_1_6_0_4_2,
													    0.3_0_6_3_0_9_6_4,
													    0.3_2_5_6_8_7,
													    0.2_9_4_1_9_2_2_5,
													    0.2_9_0_8_0_9_1,
													    0.2_8_7_2_3_5_9_8,
													    0.2_7_6_9_6_2_9_4,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Any											="cuda"
													a__:       str											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
													a__:       Dict											=pipe.to(_a			)
													pipe.set_progress_bar_config(disable=_a			)

													a__:       Tuple											="a castle next to a river"
													a__:       Union[str, Any]											={
													    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
													    "reverse_editing_direction": False,
													    "edit_warmup_steps": [1_5, 1_8],
													    "edit_guidance_scale": 6,
													    "edit_threshold": [0.9, 0.8],
													    "edit_momentum_scale": 0.5,
													    "edit_mom_beta": 0.6,
													}

													a__:       Optional[Any]											=4_8
													a__:       Tuple											=7

													# no sega enabled
													a__:       int											=torch.Generator(_a			)
													generator.manual_seed(_a			)
													a__:       Dict											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       )

													a__:       Dict											=output.images
													a__:       Tuple											=image[0, -3:, -3:, -1]
													a__:       str											=[
													    0.7_5_1_6_3_5_5_5,
													    0.7_6_0_3_7_1_4_5,
													    0.6_1_7_8_5,
													    0.9_1_8_9_6_7_3,
													    0.8_6_2_7_7_0_1,
													    0.8_5_1_8_9_6_9_4,
													    0.8_5_1_2_8_1_3,
													    0.8_7_0_1_2_0_7_6,
													    0.8_3_1_2_8_5_7,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_a			)
													a__:       str											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       **_a      ,       )

													a__:       Union[str, Any]											=output.images
													a__:       Optional[Any]											=image[0, -3:, -3:, -1]
													a__:       List[Any]											=[
													    0.7_3_5_5_3_3_6_5,
													    0.7_5_3_7_2_7_1,
													    0.7_4_3_4_1_9_0_5,
													    0.6_6_4_8_0_3_5_6,
													    0.6_4_7_2_9_2_5,
													    0.6_3_0_3_9_4_1_6,
													    0.6_4_8_1_2_9_0_5,
													    0.6_7_4_9_7_1_7,
													    0.6_5_1_7_1_0_2,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







						def   _lowerCamelCase			( self						:  List[Any]			):
													a__:       Optional[Any]											="cuda"
													a__:       int											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      ,       torch_dtype=torch.floataa			)
													a__:       List[Any]											=pipe.to(_a			)
													pipe.set_progress_bar_config(disable=_a			)

													a__:       Union[str, Any]											="a photo of a cat"
													a__:       int											={
													    "editing_prompt": ["sunglasses"],
													    "reverse_editing_direction": [False],
													    "edit_warmup_steps": 1_0,
													    "edit_guidance_scale": 6,
													    "edit_threshold": 0.9_5,
													    "edit_momentum_scale": 0.5,
													    "edit_mom_beta": 0.6,
													}

													a__:       Optional[int]											=3
													a__:       Optional[int]											=7

													# no sega enabled
													a__:       List[Any]											=torch.Generator(_a			)
													generator.manual_seed(_a			)
													a__:       Dict											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       )

													a__:       str											=output.images
													a__:       Any											=image[0, -3:, -3:, -1]
													a__:       Any											=[
													    0.3_4_8_8_7_6_9_5,
													    0.3_8_7_6_9_5_3,
													    0.3_7_5,
													    0.3_4_4_2_3_8_2_8,
													    0.3_5_8_1_5_4_3,
													    0.3_5_7_1_7_7_7_3,
													    0.3_3_8_3_7_8_9,
													    0.3_4_5_7_0_3_1_2,
													    0.3_5_9_3_7_5,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_a			)
													a__:       Dict											=pipe(
													    [prompt]      ,       generator=_a      ,       guidance_scale=_a      ,       num_inference_steps=5_0      ,       output_type="np"      ,       width=5_1_2      ,       height=5_1_2      ,       **_a      ,       )

													a__:       Tuple											=output.images
													a__:       int											=image[0, -3:, -3:, -1]
													a__:       Union[str, Any]											=[
													    0.4_2_2_8_5_1_5_6,
													    0.3_6_9_1_4_0_6_2,
													    0.2_9_0_7_7_1_4_8,
													    0.4_2_0_4_1_0_1_6,
													    0.4_1_9_1_8_9_4_5,
													    0.3_5_4_9_8_0_4_7,
													    0.3_6_1_8_1_6_4,
													    0.4_4_2_3_8_2_8,
													    0.4_3_1_1_5_2_3_4,
													]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)

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










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





