
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 		a__						(      lowerCAmelCase_,			unittest.TestCase						):
   __lowerCAmelCase            =				KandinskyImgaImgPipeline
   __lowerCAmelCase            =				["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
   __lowerCAmelCase            =				[
       """prompt""",
       """negative_prompt""",
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
   ]
   __lowerCAmelCase            =				[
       """generator""",
       """height""",
       """width""",
       """strength""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   __lowerCAmelCase            =				False
   @property
   def 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return self.time_input_dim
   @property
   def 			__magic_name__			(			self				):
          return self.time_input_dim * 4
   @property
   def 			__magic_name__			(			self				):
          return 100
   @property
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"				)
          return tokenizer
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				Tuple       		=							MCLIPConfig(
              numDims=self.cross_attention_dim	,    transformerDimensions=self.text_embedder_hidden_size	,    hidden_size=self.text_embedder_hidden_size	,    intermediate_size=37	,    num_attention_heads=4	,    num_hidden_layers=5	,    vocab_size=1_005	,    )

          lowercase    :				Any       		=							MultilingualCLIP(__a				)
          lowercase    :				int       		=							text_encoder.eval()

          return text_encoder
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)

          lowercase    :				Dict       		=							{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          lowercase    :				str       		=							UNetaDConditionModel(**__a				)
          return model
   @property
   def 			__magic_name__			(			self				):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				Union[str, Any]       		=							VQModel(**self.dummy_movq_kwargs				)
          return model
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.dummy_text_encoder
          lowercase    :				Any       		=							self.dummy_tokenizer
          lowercase    :				Union[str, Any]       		=							self.dummy_unet
          lowercase    :				List[Any]       		=							self.dummy_movq

          lowercase    :				Dict       		=							{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.0_0_0_8_5,
              'beta_end': 0.0_1_2,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          lowercase    :				Dict       		=							DDIMScheduler(**__a				)

          lowercase    :				Optional[int]       		=							{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components
   def 			__magic_name__			(			self	,    _a	,    _a=0				):
          lowercase    :				Optional[Any]       		=							floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(__a				)				).to(__a				)
          lowercase    :				Union[str, Any]       		=							floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(seed + 1				)				).to(__a				)
          # create init_image
          lowercase    :				Optional[Any]       		=							floats_tensor((1, 3, 64, 64)	,    rng=random.Random(__a				)				).to(__a				)
          lowercase    :				Optional[Any]       		=							image.cpu().permute(0	,    2	,    3	,    1				)[0]
          lowercase    :				List[Any]       		=							Image.fromarray(np.uinta(__a				)				).convert("RGB"				).resize((256, 256)				)

          if str(__a				).startswith("mps"				):
                 lowercase    :				str       		=							torch.manual_seed(__a				)
          else:
                 lowercase    :				Union[str, Any]       		=							torch.Generator(device=__a				).manual_seed(__a				)
          lowercase    :				Optional[int]       		=							{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							'cpu'

          lowercase    :				Any       		=							self.get_dummy_components()

          lowercase    :				List[Any]       		=							self.pipeline_class(**__a				)
          lowercase    :				Optional[int]       		=							pipe.to(__a				)

          pipe.set_progress_bar_config(disable=__a				)

          lowercase    :				Tuple       		=							pipe(**self.get_dummy_inputs(__a				)				)
          lowercase    :				int       		=							output.images

          lowercase    :				int       		=							pipe(
              **self.get_dummy_inputs(__a				)	,    return_dict=__a	,    )[0]

          lowercase    :				Optional[Any]       		=							image[0, -3:, -3:, -1]
          lowercase    :				List[Any]       		=							image_from_tuple[0, -3:, -3:, -1]

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

          lowercase    :				int       		=							np.array(
              [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]				)
          assert (
              np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
          ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
          ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""


@slow
@require_torch_gpu
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinsky/kandinsky_img2img_frog.npy"				)

          lowercase    :				List[str]       		=							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
          lowercase    :				Optional[Any]       		=							'A red cartoon frog, 4k'

          lowercase    :				int       		=							KandinskyPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-prior"	,    torch_dtype=torch.floataa				)
          pipe_prior.to(__a				)

          lowercase    :				Union[str, Any]       		=							KandinskyImgaImgPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1"	,    torch_dtype=torch.floataa				)
          lowercase    :				Optional[int]       		=							pipeline.to(__a				)

          pipeline.set_progress_bar_config(disable=__a				)

          lowercase    :				List[Any]       		=							torch.Generator(device="cpu"				).manual_seed(0				)
          lowercase    :				List[str]       		=							pipe_prior(
              __a	,    generator=__a	,    num_inference_steps=5	,    negative_prompt=""	,    ).to_tuple()

          lowercase    :				Union[str, Any]       		=							pipeline(
              __a	,    image=__a	,    image_embeds=__a	,    negative_image_embeds=__a	,    generator=__a	,    num_inference_steps=100	,    height=768	,    width=768	,    strength=0.2	,    output_type="np"	,    )

          lowercase    :				Optional[int]       		=							output.images[0]

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

          assert_mean_pixel_difference(__a	,    __a				)






'''simple docstring'''



import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase	:						Dict       =	16
__lowercase	:						List[Any]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					return int(x / 2**20   )





class 							__UpperCamelCase				:




	def __enter__(   self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__a			:       Optional[int]					    =  torch.cuda.memory_allocated()
						return self





	def __exit__(   self						,				*__a							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						__a			:       Dict					    =  torch.cuda.memory_allocated()
						__a			:       List[Any]					    =  torch.cuda.max_memory_allocated()
						__a			:       Tuple					    =  bamb(self.end - self.begin							)
						__a			:       Tuple					    =  bamb(self.peak - self.begin							)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16						,      _SCREAMING_SNAKE_CASE      :   str = "bert-base-cased"						,      _SCREAMING_SNAKE_CASE      :   int = 320						,      _SCREAMING_SNAKE_CASE      :   int = 160						,      ):
					__a			:       int					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  load_dataset(
					    'glue'						,      'mrpc'						,      split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}   )

					def tokenize_function(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# max_length=None => use the model max length (it's actually the default)
										__a			:       Any					    =  tokenizer(examples['sentence1']						,      examples['sentence2']						,      truncation=_SCREAMING_SNAKE_CASE						,      max_length=_SCREAMING_SNAKE_CASE   )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a			:       List[str]					    =  datasets.map(
					    _SCREAMING_SNAKE_CASE						,      batched=_SCREAMING_SNAKE_CASE						,      remove_columns=['idx', 'sentence1', 'sentence2']						,      load_from_cache_file=_SCREAMING_SNAKE_CASE   )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__a			:       Tuple					    =  tokenized_datasets.rename_column('label'						,      'labels'   )

					def collate_fn(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=128						,      return_tensors='pt'   )
										return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='longest'						,      return_tensors='pt'   )

					# Instantiate dataloaders.
					__a			:       int					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  DataLoader(
					    tokenized_datasets['validation']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					# Initialize accelerator
					__a			:       str					    =  Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a			:       Dict					    =  config['lr']
					__a			:       str					    =  int(config['num_epochs']   )
					__a			:       Optional[int]					    =  int(config['seed']   )
					__a			:       Any					    =  int(config['batch_size']   )
					__a			:       List[str]					    =  args.model_name_or_path

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  get_dataloaders(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      args.n_train						,      args.n_val   )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a			:       Optional[int]					    =  AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE   )

					# Instantiate optimizer
					__a			:       Optional[Any]					    =  (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a			:       Optional[Any]					    =  optimizer_cls(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					if accelerator.state.deepspeed_plugin is not None:
										__a			:       int					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__a			:       Union[str, Any]					    =  1
					__a			:       Tuple					    =  (len(_SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										__a			:       str					    =  get_linear_schedule_with_warmup(
										    optimizer=_SCREAMING_SNAKE_CASE						,      num_warmup_steps=0						,      num_training_steps=_SCREAMING_SNAKE_CASE						,      )
					else:
										__a			:       List[Any]					    =  DummyScheduler(_SCREAMING_SNAKE_CASE						,      total_num_steps=_SCREAMING_SNAKE_CASE						,      warmup_num_steps=0   )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					__a    ,				__a    ,				__a    ,				__a    ,				__a			:       Optional[Any]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# We need to keep track of how many total steps we have iterated over
					__a			:       Union[str, Any]					    =  0
					# We also need to keep track of the stating epoch so files are named properly
					__a			:       Dict					    =  0

					# Now we train the model
					__a			:       str					    =  {}
					for epoch in range(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[Any]					    =  model(**_SCREAMING_SNAKE_CASE   )
																				__a			:       str					    =  outputs.loss
																				__a			:       str					    =  loss / gradient_accumulation_steps
																				accelerator.backward(_SCREAMING_SNAKE_CASE   )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

																				overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin   )   )   )
										accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used   )   )
										accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked   )   )
										accelerator.print(
										    'Total Peak Memory consumed during the train (max): {}'.format(
										        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
										__a			:       List[Any]					    =  tracemalloc.peaked + bamb(tracemalloc.begin   )
										if args.peak_memory_upper_bound is not None:
															assert (
															    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
															), "Peak memory usage exceeded the upper bound"

					accelerator.wait_for_everyone()
					if accelerator.is_main_process:
										with open(os.path.join(args.output_dir						,      'peak_memory_utilization.json'   )						,      'w'   ) as f:
															json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							():
					__a			:       int					    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
					parser.add_argument(
					    '--model_name_or_path'						,      type=_SCREAMING_SNAKE_CASE						,      default='bert-base-cased'						,      help='Path to pretrained model or model identifier from huggingface.co/models.'						,      required=_SCREAMING_SNAKE_CASE						,      )
					parser.add_argument(
					    '--output_dir'						,      type=_SCREAMING_SNAKE_CASE						,      default='.'						,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,      )
					parser.add_argument(
					    '--peak_memory_upper_bound'						,      type=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_SNAKE_CASE						,      help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'						,      )
					parser.add_argument(
					    '--n_train'						,      type=_SCREAMING_SNAKE_CASE						,      default=320						,      help='Number of training examples to use.'						,      )
					parser.add_argument(
					    '--n_val'						,      type=_SCREAMING_SNAKE_CASE						,      default=160						,      help='Number of validation examples to use.'						,      )
					parser.add_argument(
					    '--num_epochs'						,      type=_SCREAMING_SNAKE_CASE						,      default=1						,      help='Number of train epochs.'						,      )
					__a			:       List[str]					    =  parser.parse_args()
					__a			:       List[Any]					    =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()



