




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




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



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      1
								_lowerCAmelCase    :    Optional[Any]										=      3
								_lowerCAmelCase    :    Tuple										=      (32, 32)

								_lowerCAmelCase    :    int										=      floats_tensor((batch_size, num_channels) + sizes		,	rng=random.Random(0      )      ).to(_UpperCAmelCase      )
								return image



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								torch.manual_seed(0      )
								_lowerCAmelCase    :    Optional[int]										=      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		,	)
								return model



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




								torch.manual_seed(0      )
								_lowerCAmelCase    :    str										=      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		,	)
								return model



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								torch.manual_seed(0      )
								_lowerCAmelCase    :    List[Any]										=      RobertaSeriesConfig(
								    hidden_size=32		,	project_dim=32		,	intermediate_size=37		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=5006		,	)
								return RobertaSeriesModelWithTransformation(_UpperCAmelCase      )



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    str:





								'''simple docstring'''




								def extract(*_UpperCAmelCase      :		str		,	**_UpperCAmelCase      :		Optional[Any]      ):
												class        __snake_case  :



																def __init__(      self      :		Optional[Any]      )  ->    str:





																				'''simple docstring'''




																				_lowerCAmelCase    :    Any										=      torch.ones([0]      )



																def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int]      )  ->    List[Any]:





																				'''simple docstring'''




																				self.pixel_values.to(_UpperCAmelCase      )
																				return self






												return Out()

								return extract



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
								_lowerCAmelCase    :    Optional[int]										=      self.dummy_cond_unet
								_lowerCAmelCase    :    Union[str, Any]										=      PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_vae
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_text_encoder
								_lowerCAmelCase    :    List[str]										=      XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""      )
								_lowerCAmelCase    :    Union[str, Any]										=      77

								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_image.to(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      init_image / 2 + 0.5

								# make sure here that pndm scheduler skips prk
								_lowerCAmelCase    :    Any										=      AltDiffusionImgaImgPipeline(
								    unet=_UpperCAmelCase		,	scheduler=_UpperCAmelCase		,	vae=_UpperCAmelCase		,	text_encoder=_UpperCAmelCase		,	tokenizer=_UpperCAmelCase		,	safety_checker=_UpperCAmelCase		,	feature_extractor=self.dummy_extractor		,	)
								_lowerCAmelCase    :    List[str]										=      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,	do_normalize=_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      alt_pipe.to(_UpperCAmelCase      )
								alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      """A painting of a squirrel eating a burger"""
								_lowerCAmelCase    :    Union[str, Any]										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
								_lowerCAmelCase    :    int										=      alt_pipe(
								    [prompt]		,	generator=_UpperCAmelCase		,	guidance_scale=6.0		,	num_inference_steps=2		,	output_type="""np"""		,	image=_UpperCAmelCase		,	)

								_lowerCAmelCase    :    Optional[Any]										=      output.images

								_lowerCAmelCase    :    Dict										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
								_lowerCAmelCase    :    str										=      alt_pipe(
								    [prompt]		,	generator=_UpperCAmelCase		,	guidance_scale=6.0		,	num_inference_steps=2		,	output_type="""np"""		,	image=_UpperCAmelCase		,	return_dict=_UpperCAmelCase		,	)[0]

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

								assert image.shape == (1, 32, 32, 3)
								_lowerCAmelCase    :    Union[str, Any]										=      np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5E-3



				@unittest.skipIf(torch_device != """cuda"""		,	"""This test requires a GPU"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.dummy_cond_unet
								_lowerCAmelCase    :    Dict										=      PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      self.dummy_vae
								_lowerCAmelCase    :    str										=      self.dummy_text_encoder
								_lowerCAmelCase    :    str										=      XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""      )
								_lowerCAmelCase    :    Any										=      77

								_lowerCAmelCase    :    Any										=      self.dummy_image.to(_UpperCAmelCase      )

								# put models in fp16
								_lowerCAmelCase    :    str										=      unet.half()
								_lowerCAmelCase    :    int										=      vae.half()
								_lowerCAmelCase    :    Optional[Any]										=      bert.half()

								# make sure here that pndm scheduler skips prk
								_lowerCAmelCase    :    Optional[Any]										=      AltDiffusionImgaImgPipeline(
								    unet=_UpperCAmelCase		,	scheduler=_UpperCAmelCase		,	vae=_UpperCAmelCase		,	text_encoder=_UpperCAmelCase		,	tokenizer=_UpperCAmelCase		,	safety_checker=_UpperCAmelCase		,	feature_extractor=self.dummy_extractor		,	)
								_lowerCAmelCase    :    str										=      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,	do_normalize=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      alt_pipe.to(_UpperCAmelCase      )
								alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

								_lowerCAmelCase    :    Tuple										=      """A painting of a squirrel eating a burger"""
								_lowerCAmelCase    :    List[str]										=      torch.manual_seed(0      )
								_lowerCAmelCase    :    Tuple										=      alt_pipe(
								    [prompt]		,	generator=_UpperCAmelCase		,	num_inference_steps=2		,	output_type="""np"""		,	image=_UpperCAmelCase		,	).images

								assert image.shape == (1, 32, 32, 3)



				@unittest.skipIf(torch_device != """cuda"""		,	"""This test requires a GPU"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/img2img/sketch-mountains-input.jpg"""      )
								# resize to resolution that is divisible by 8 but not 16 or 32
								_lowerCAmelCase    :    Tuple										=      init_image.resize((760, 504)      )

								_lowerCAmelCase    :    Union[str, Any]										=      """BAAI/AltDiffusion"""
								_lowerCAmelCase    :    List[Any]										=      AltDiffusionImgaImgPipeline.from_pretrained(
								    _UpperCAmelCase		,	safety_checker=_UpperCAmelCase		,	)
								pipe.to(_UpperCAmelCase      )
								pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
								pipe.enable_attention_slicing()

								_lowerCAmelCase    :    Optional[int]										=      """A fantasy landscape, trending on artstation"""

								_lowerCAmelCase    :    Dict										=      torch.manual_seed(0      )
								_lowerCAmelCase    :    str										=      pipe(
								    prompt=_UpperCAmelCase		,	image=_UpperCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	generator=_UpperCAmelCase		,	output_type="""np"""		,	)
								_lowerCAmelCase    :    Optional[Any]										=      output.images[0]

								_lowerCAmelCase    :    str										=      image[255:258, 383:386, -1]

								assert image.shape == (504, 760, 3)
								_lowerCAmelCase    :    Optional[int]										=      np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]      )

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






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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/img2img/sketch-mountains-input.jpg"""      )
								_lowerCAmelCase    :    int										=      init_image.resize((768, 512)      )
								_lowerCAmelCase    :    Tuple										=      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""      )

								_lowerCAmelCase    :    List[Any]										=      """BAAI/AltDiffusion"""
								_lowerCAmelCase    :    int										=      AltDiffusionImgaImgPipeline.from_pretrained(
								    _UpperCAmelCase		,	safety_checker=_UpperCAmelCase		,	)
								pipe.to(_UpperCAmelCase      )
								pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
								pipe.enable_attention_slicing()

								_lowerCAmelCase    :    List[Any]										=      """A fantasy landscape, trending on artstation"""

								_lowerCAmelCase    :    List[str]										=      torch.manual_seed(0      )
								_lowerCAmelCase    :    Optional[Any]										=      pipe(
								    prompt=_UpperCAmelCase		,	image=_UpperCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	generator=_UpperCAmelCase		,	output_type="""np"""		,	)
								_lowerCAmelCase    :    int										=      output.images[0]

								assert image.shape == (512, 768, 3)
								# img2img is flaky across GPUs even in fp32, so using MAE here
								assert np.abs(expected_image - image      ).max() < 1E-2







import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


_lowerCamelCase      :  Optional[Any]      						=						TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


_lowerCamelCase      :  Optional[int]      						=						[]
_lowerCamelCase      :  Tuple      						=						[]

_lowerCamelCase      :  List[str]      						=						{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

_lowerCamelCase      :  Optional[Any]      						=						[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": F'''🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results''',
            "emoji": True,
        },
    }
]

_lowerCamelCase      :  str      						=						0
for log in Path().glob("*.log"):
	_lowerCamelCase      :  str      						=						0
	with open(log, "r") as f:
		for line in f:
			_lowerCamelCase      :  Optional[Any]      						=						json.loads(line)
			if line.get("nodeid", "") != "":
				_lowerCamelCase      :  str      						=						line["nodeid"]
				if line.get("duration", None) is not None:
					_lowerCamelCase      :  str      						=						F'''{line['duration']:.4f}'''
					if line.get("outcome", "") == "failed":
						section_num_failed += 1
						failed.append([test, duration, log.name.split("_")[0]])
						total_num_failed += 1
	group_info.append([str(log), section_num_failed, failed])
	_lowerCamelCase      :  Optional[int]      						=						[]
	log.unlink()

_lowerCamelCase      :  str      						=						""
_lowerCamelCase      :  Optional[Any]      						=						[]
if total_num_failed > 0:
	for name, num_failed, failed_tests in group_info:
		if num_failed > 0:
			if num_failed == 1:
				message += F"*{name[1:]}: {num_failed} failed test*\n"
			else:
				message += F"*{name[1:]}: {num_failed} failed tests*\n"
			_lowerCamelCase      :  Any      						=						[]
			_lowerCamelCase      :  Dict      						=						{}
			for test in failed_tests:
				_lowerCamelCase      :  Union[str, Any]      						=						test[0].split("::")
				_lowerCamelCase      :  str      						=						data[0].split("/")[-1]
				if data[0] not in filesafailed:
					_lowerCamelCase      :  List[str]      						=						[data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			_lowerCamelCase      :  List[Any]      						=						[test[0] for test in failed_table]
			_lowerCamelCase      :  Optional[Any]      						=						list(set(files))
			# Count number of instances in failed_tests
			_lowerCamelCase      :  Union[str, Any]      						=						[]
			for file in individual_files:
				table.append([file, len(filesafailed[file])])

			_lowerCamelCase      :  Optional[Any]      						=						tabulate(
			    table,
			    headers=["Test Location", "Num Failed"],
			    tablefmt=hf_table_format,
			    stralign="right",
			)
			message += F"\n```\n{failed_table}\n```"
			all_filesafailed.append(filesafailed)
	if len(message) > 3_0_0_0:
		_lowerCamelCase      :  List[str]      						=						"Too many failed tests, please see the full report in the Action results."
		_lowerCamelCase      :  Tuple      						=						len(err) + 1_0
		_lowerCamelCase      :  Union[str, Any]      						=						message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	_lowerCamelCase      :  List[str]      						=						"No failed tests! 🤗"
	print(F'''## {message}''')
	payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
	from slack_sdk import WebClient

	_lowerCamelCase      :  Optional[int]      						=						WebClient(token=os.environ["SLACK_API_TOKEN"])
	if message != "No failed tests! 🤗":
		_lowerCamelCase      :  int      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": message,
		    },
		}
		payload.append(md_report)
		_lowerCamelCase      :  List[Any]      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": "*For more details:*",
		    },
		    "accessory": {
		        "type": "button",
		        "text": {
		            "type": "plain_text",
		            "text": "Check Action results",
		            "emoji": True,
		        },
		        "url": F'''https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
		    },
		}
		payload.append(action_button)
		_lowerCamelCase      :  Union[str, Any]      						=						{
		    "type": "context",
		    "elements": [
		        {
		            "type": "plain_text",
		            "text": F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	_lowerCamelCase      :  int      						=						client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
	_lowerCamelCase      :  List[Any]      						=						response.data["ts"]
	for failed_file in all_filesafailed:
		for test_location, test_failures in failed_file.items():
			# Keep only the first instance of the test name
			_lowerCamelCase      :  Dict      						=						""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_lowerCamelCase      :  Optional[int]      						=						row[0]
				else:
					_lowerCamelCase      :  List[str]      						=						""

			_lowerCamelCase      :  int      						=						{
			    "type": "section",
			    "text": {
			        "type": "mrkdwn",
			        "text": F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```''',
			    },
			}

			client.chat_postMessage(
			    channel="#accelerate-ci-daily",
			    thread_ts=ts,
			    blocks=[payload],
			)

