

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class     a__  (    snake_case							,		snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				StableUnCLIPPipeline
			__lowerCamelCase       				=				TEXT_TO_IMAGE_PARAMS
			__lowerCamelCase       				=				TEXT_TO_IMAGE_BATCH_PARAMS
			__lowerCamelCase       				=				TEXT_TO_IMAGE_IMAGE_PARAMS
			__lowerCamelCase       				=				TEXT_TO_IMAGE_IMAGE_PARAMS

			# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	32
							A__						=	embedder_hidden_size

							# prior components

							torch.manual_seed(0				)
							A__						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

							torch.manual_seed(0				)
							A__						=	CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0					,					eos_token_id=2					,					hidden_size=lowercase					,					projection_dim=lowercase					,					intermediate_size=37					,					layer_norm_eps=1e-05					,					num_attention_heads=4					,					num_hidden_layers=5					,					pad_token_id=1					,					vocab_size=1000					,					)				)

							torch.manual_seed(0				)
							A__						=	PriorTransformer(
							    num_attention_heads=2					,					attention_head_dim=12					,					embedding_dim=lowercase					,					num_layers=1					,					)

							torch.manual_seed(0				)
							A__						=	DDPMScheduler(
							    variance_type="fixed_small_log"					,					prediction_type="sample"					,					num_train_timesteps=1000					,					clip_sample=lowercase					,					clip_sample_range=5.0					,					beta_schedule="squaredcos_cap_v2"					,					)

							# regular denoising components

							torch.manual_seed(0				)
							A__						=	StableUnCLIPImageNormalizer(embedding_dim=lowercase				)
							A__						=	DDPMScheduler(beta_schedule="squaredcos_cap_v2"				)

							torch.manual_seed(0				)
							A__						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

							torch.manual_seed(0				)
							A__						=	CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0					,					eos_token_id=2					,					hidden_size=lowercase					,					projection_dim=32					,					intermediate_size=37					,					layer_norm_eps=1e-05					,					num_attention_heads=4					,					num_hidden_layers=5					,					pad_token_id=1					,					vocab_size=1000					,					)				)

							torch.manual_seed(0				)
							A__						=	UNetaDConditionModel(
							    sample_size=32					,					in_channels=4					,					out_channels=4					,					down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")					,					up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")					,					block_out_channels=(32, 64)					,					attention_head_dim=(2, 4)					,					class_embed_type="projection"					,					projection_class_embeddings_input_dim=embedder_projection_dim * 2					,					cross_attention_dim=lowercase					,					layers_per_block=1					,					upcast_attention=lowercase					,					use_linear_projection=lowercase					,					)

							torch.manual_seed(0				)
							A__						=	DDIMScheduler(
							    beta_schedule="scaled_linear"					,					beta_start=0.0_0085					,					beta_end=0.012					,					prediction_type="v_prediction"					,					set_alpha_to_one=lowercase					,					steps_offset=1					,					)

							torch.manual_seed(0				)
							A__						=	AutoencoderKL()

							A__						=	{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=0				)     ->  Optional[int]:
							'''simple docstring'''






							if str(lowercase				).startswith("mps"				):
											A__						=	torch.manual_seed(lowercase				)
							else:
											A__						=	torch.Generator(device=lowercase				).manual_seed(lowercase				)
							A__						=	{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=lowercase				)

@slow
@require_torch_gpu
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"				)

							A__						=	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,					torch_dtype=torch.floataa				)
							pipe.to(lowercase				)
							pipe.set_progress_bar_config(disable=lowercase				)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							A__						=	torch.Generator(device="cpu"				).manual_seed(0				)
							A__						=	pipe("anime turle"					,					generator=lowercase					,					output_type="np"				)

							A__						=	output.images[0]

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

							assert_mean_pixel_difference(lowercase					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							A__						=	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,					torch_dtype=torch.floataa				)
							A__						=	pipe.to(lowercase				)
							pipe.set_progress_bar_config(disable=lowercase				)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							A__						=	pipe(
							    "anime turtle"					,					prior_num_inference_steps=2					,					num_inference_steps=2					,					output_type="np"					,					)

							A__						=	torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9



# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# XXX: run_asr is premature and doesn't save any results
								# so all we check for now is that the process didn't fail
								pass







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

								if fpaa:
															args.extend(["--fp16"]						)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__UpperCamelCase	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

								__UpperCamelCase	:   Optional[int]			   =					launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(a     ,   env=self.get_env()						)

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
								# - it won't be able to handle that
								# 2. for now testing with just 2 gpus max (since some quality tests may give different
								# results with mode gpus because we use very little data)
								__UpperCamelCase	:   List[Any]			   =					min(2     ,   get_gpu_count()						) if distributed else 1
								return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()