


"""simple docstring"""







import gc
import unittest

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

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

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


enable_full_determinism()




class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Optional[int]    					=			LDMTextToImagePipeline
	snake_case__				:  Union[str, Any]    					=			TEXT_TO_IMAGE_PARAMS - {
	    "negative_prompt",
	    "negative_prompt_embeds",
	    "cross_attention_kwargs",
	    "prompt_embeds",
	}
	snake_case__				:  Union[str, Any]    					=			PipelineTesterMixin.required_optional_params - {
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__				:  int    					=			TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case__				:  str    					=			False




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Any:
						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				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			,			)
						__SCREAMING_SNAKE_CASE						   =				DDIMScheduler(
						    beta_start=0.00_085			,			beta_end=0.012			,			beta_schedule="scaled_linear"			,			clip_sample=UpperCAmelCase__			,			set_alpha_to_one=UpperCAmelCase__			,			)
						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				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			,			)
						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				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			,			)
						__SCREAMING_SNAKE_CASE						   =				CLIPTextModel(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

						__SCREAMING_SNAKE_CASE						   =				{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Optional[Any]			,			UpperCAmelCase__			:				Optional[int]=0   )       ->	Union[str, Any]:
						if str(UpperCAmelCase__   ).startswith("mps"   ):
											__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(UpperCAmelCase__   )
						else:
											__SCREAMING_SNAKE_CASE						   =				torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				"cpu"  # ensure determinism for the device-dependent torch.Generator

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_components()
						__SCREAMING_SNAKE_CASE						   =				LDMTextToImagePipeline(**UpperCAmelCase__   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_inputs(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe(**UpperCAmelCase__   ).images
						__SCREAMING_SNAKE_CASE						   =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_6, 1_6, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]   )

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




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	int:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Tuple=torch.floataa			,			UpperCAmelCase__			:				List[Any]=0   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				np.random.RandomState(UpperCAmelCase__   ).standard_normal((1, 4, 3_2, 3_2)   )
						__SCREAMING_SNAKE_CASE						   =				torch.from_numpy(UpperCAmelCase__   ).to(device=UpperCAmelCase__			,			dtype=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




	def   UpperCAmelCase_      ( self			:				str   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"   ).to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_inputs(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe(**UpperCAmelCase__   ).images
						__SCREAMING_SNAKE_CASE						   =				image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]   )
						__SCREAMING_SNAKE_CASE						   =				np.abs(expected_slice - image_slice   ).max()
						assert max_diff < 1E-3




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				List[Any]=torch.floataa			,			UpperCAmelCase__			:				Optional[Any]=0   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				np.random.RandomState(UpperCAmelCase__   ).standard_normal((1, 4, 3_2, 3_2)   )
						__SCREAMING_SNAKE_CASE						   =				torch.from_numpy(UpperCAmelCase__   ).to(device=UpperCAmelCase__			,			dtype=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 5_0,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"   ).to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_inputs(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe(**UpperCAmelCase__   ).images[0]

						__SCREAMING_SNAKE_CASE						   =				load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"   )
						__SCREAMING_SNAKE_CASE						   =				np.abs(expected_image - image   ).max()
						assert max_diff < 1E-3




"""simple docstring"""







# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


a__				:							Tuple						=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''




def    UpperCAmelCase__       (lowerCAmelCase_=None   ):




					'''simple docstring'''


					if subparsers is not None:
										__SCREAMING_SNAKE_CASE						   =				subparsers.add_parser("tpu-config"  ,						description=_description   )
					else:
										__SCREAMING_SNAKE_CASE						   =				argparse.ArgumentParser("Accelerate tpu-config command"  ,						description=_description   )
					# Core arguments
					__SCREAMING_SNAKE_CASE						   =				parser.add_argument_group(
					    "Config Arguments"  ,						"Arguments that can be configured through `accelerate config`."   )
					config_args.add_argument(
					    "--config_file"  ,						type=lowerCAmelCase_  ,						default=lowerCAmelCase_  ,						help="Path to the config file to use for accelerate."  ,						)
					config_args.add_argument(
					    "--tpu_name"  ,						default=lowerCAmelCase_  ,						help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."  ,						)
					config_args.add_argument(
					    "--tpu_zone"  ,						default=lowerCAmelCase_  ,						help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."  ,						)
					__SCREAMING_SNAKE_CASE						   =				parser.add_argument_group("TPU Arguments"  ,						"Arguments for options ran inside the TPU."   )
					pod_args.add_argument(
					    "--use_alpha"  ,						action="store_true"  ,						help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."  ,						)
					pod_args.add_argument(
					    "--command_file"  ,						default=lowerCAmelCase_  ,						help="The path to the file containing the commands to run on the pod on startup."  ,						)
					pod_args.add_argument(
					    "--command"  ,						action="append"  ,						nargs="+"  ,						help="A command to run on the pod. Can be passed multiple times."  ,						)
					pod_args.add_argument(
					    "--install_accelerate"  ,						action="store_true"  ,						help="Whether to install accelerate on the pod. Defaults to False."  ,						)
					pod_args.add_argument(
					    "--accelerate_version"  ,						default="latest"  ,						help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."  ,						)
					pod_args.add_argument(
					    "--debug"  ,						action="store_true"  ,						help="If set, will print the command that would be run instead of running it."   )

					if subparsers is not None:
										parser.set_defaults(func=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				load_config_from_file(args.config_file   )
										if not args.command_file and defaults.command_file is not None and not args.command:
															__SCREAMING_SNAKE_CASE						   =				defaults.command_file
										if not args.command and defaults.commands is not None:
															__SCREAMING_SNAKE_CASE						   =				defaults.commands
										if not args.tpu_name:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_name
										if not args.tpu_zone:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_zone
					if args.accelerate_version == "dev":
										__SCREAMING_SNAKE_CASE						   =				"git+https://github.com/huggingface/accelerate.git"
					elif args.accelerate_version == "latest":
										__SCREAMING_SNAKE_CASE						   =				"accelerate -U"
					elif isinstance(parse(args.accelerate_version   )  ,						lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				f"""accelerate=={args.accelerate_version}"""

					if not args.command_file and not args.command:
										raise ValueError("You must specify either a command file or a command to run on the pod."   )

					if args.command_file:
										with open(args.command_file  ,						"r"   ) as f:
															__SCREAMING_SNAKE_CASE						   =				[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]  ,						lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					__SCREAMING_SNAKE_CASE						   =				["cd /usr/share"]
					if args.install_accelerate:
										new_cmd += [f"""pip install {args.accelerate_version}"""]
					new_cmd += args.command
					__SCREAMING_SNAKE_CASE						   =				"; ".join(lowerCAmelCase_   )

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					__SCREAMING_SNAKE_CASE						   =				["gcloud"]
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f"""Running {' '.join(lowerCAmelCase_   )}"""   )
										return
					subprocess.run(lowerCAmelCase_   )
					print("Successfully setup pod."   )




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tpu_command_parser()
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args()

					tpu_command_launcher(lowerCAmelCase_   )
