


'''simple docstring'''







import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


A				:					Tuple    						=			'''true'''






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[str]=82  ,lowerCamelCase					:      Optional[Any]=16       ):
							set_seed(42       )
							_A					:     int     = RegressionModel()
							_A					:     Any     = deepcopy(_lowerCamelCase       )
							_A					:     List[str]     = RegressionDataset(length=_lowerCamelCase       )
							_A					:     Any     = DataLoader(_lowerCamelCase  ,batch_size=_lowerCamelCase       )
							model.to(accelerator.device       )
							_A					:     List[Any]     = accelerator.prepare(_lowerCamelCase  ,_lowerCamelCase       )
							return model, ddp_model, dataloader






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Optional[int]=False       ):
							_A					:     Optional[Any]     = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'       )
							_A					:     List[Any]     = load_dataset('glue'  ,'mrpc'  ,split='validation'       )

							def tokenize_function(lowerCamelCase					:      List[Any]       ):
														_A					:     List[Any]     = tokenizer(examples['sentence1']  ,examples['sentence2']  ,truncation=_lowerCamelCase  ,max_length=_lowerCamelCase       )
														return outputs

							with accelerator.main_process_first():
														_A					:     Union[str, Any]     = dataset.map(
														    _lowerCamelCase  ,batched=_lowerCamelCase  ,remove_columns=['idx', 'sentence1', 'sentence2']  ,)

							_A					:     int     = tokenized_datasets.rename_column('label'  ,'labels'       )

							def collate_fn(lowerCamelCase					:      Union[str, Any]       ):
														if use_longest:
																					return tokenizer.pad(_lowerCamelCase  ,padding='longest'  ,return_tensors='pt'       )
														return tokenizer.pad(_lowerCamelCase  ,padding='max_length'  ,max_length=128  ,return_tensors='pt'       )

							return DataLoader(_lowerCamelCase  ,shuffle=_lowerCamelCase  ,collate_fn=_lowerCamelCase  ,batch_size=16       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      str       ):
							_A					:     Dict     = Accelerator(dispatch_batches=_lowerCamelCase  ,split_batches=_lowerCamelCase       )
							_A					:     List[str]     = get_dataloader(_lowerCamelCase  ,not dispatch_batches       )
							_A					:     int     = AutoModelForSequenceClassification.from_pretrained(
							    'hf-internal-testing/mrpc-bert-base-cased'  ,return_dict=_lowerCamelCase       )
							_A					:     Any     = accelerator.prepare(_lowerCamelCase  ,_lowerCamelCase       )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Optional[Any]       ):
							_A					:     Tuple     = []
							for batch in dataloader:
														_A					:     Dict     = batch.values()
														with torch.no_grad():
																					_A					:     Optional[Any]     = model(_lowerCamelCase       )
																					_A					:     int     = accelerator.gather_for_metrics((logit, target)       )
																					logits_and_targets.append((logit, target)       )
							_A					:     str     = [], []
							for logit, targ in logits_and_targets:
														logits.append(_lowerCamelCase       )
														targs.append(_lowerCamelCase       )
							_A					:     Optional[Any]     = torch.cat(_lowerCamelCase       ), torch.cat(_lowerCamelCase       )
							return logits, targs






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      Optional[int]=82  ,lowerCamelCase					:      Optional[Any]=False  ,lowerCamelCase					:      Dict=False  ,lowerCamelCase					:      List[Any]=16       ):
							_A					:     Optional[int]     = get_basic_setup(_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase       )
							_A					:     Optional[Any]     = generate_predictions(_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase       )
							assert (
							    len(_lowerCamelCase       ) == num_samples
							), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowerCamelCase       )}'






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any = False  ,lowerCamelCase					:      Any = False       ):
							_A					:     Union[str, Any]     = evaluate.load('glue'  ,'mrpc'       )
							_A					:     Dict     = get_mrpc_setup(_lowerCamelCase  ,_lowerCamelCase       )
							# First do baseline
							_A					:     Optional[Any]     = setup["""no"""]
							model.to(_lowerCamelCase       )
							model.eval()
							for batch in dataloader:
														batch.to(_lowerCamelCase       )
														with torch.inference_mode():
																					_A					:     List[Any]     = model(**_lowerCamelCase       )
														_A					:     Tuple     = outputs.logits.argmax(dim=-1       )
														metric.add_batch(predictions=_lowerCamelCase  ,references=batch['labels']       )
							_A					:     Optional[int]     = metric.compute()

							# Then do distributed
							_A					:     Optional[int]     = setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					_A					:     Union[str, Any]     = model(**_lowerCamelCase       )
														_A					:     Optional[Any]     = outputs.logits.argmax(dim=-1       )
														_A					:     List[str]     = batch["""labels"""]
														_A					:     Optional[Any]     = accelerator.gather_for_metrics((preds, references)       )
														metric.add_batch(predictions=_lowerCamelCase  ,references=_lowerCamelCase       )
							_A					:     Union[str, Any]     = metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]  ,distributed[key]       ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'






def 				lowerCAmelCase__	(    ):
							_A					:     str     = Accelerator(split_batches=_lowerCamelCase  ,dispatch_batches=_lowerCamelCase       )
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print('**Testing gather_for_metrics**'       )
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'       )
																												test_mrpc(_lowerCamelCase  ,_lowerCamelCase       )
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('**Test torch metrics**'       )
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					_A					:     Tuple     = Accelerator(split_batches=_lowerCamelCase  ,dispatch_batches=_lowerCamelCase       )
																					if accelerator.is_local_main_process:
																												print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'       )
																					test_torch_metrics(_lowerCamelCase  ,99       )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('**Test last batch is not dropped when perfectly divisible**'       )
							_A					:     Optional[Any]     = Accelerator()
							test_torch_metrics(_lowerCamelCase  ,512       )
							accelerator.state._reset_state()






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
							main()


if __name__ == "__main__":
			main()










'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   KandinskyVaaPriorPipeline
    a													=   ["prompt"]
    a													=   ["prompt", "negative_prompt"]
    a													=   [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a													=   False


    @property
    def        A   (  self      :							List[str]):
           return 32


    @property
    def        A   (  self      :							List[Any]):
           return 32


    @property
    def        A   (  self      :							Dict):
           return self.time_input_dim


    @property
    def        A   (  self      :							Tuple):
           return self.time_input_dim * 4


    @property
    def        A   (  self      :							Optional[int]):
           return 100


    @property
    def        A   (  self      :							Dict):
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
           return tokenizer


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Any     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							List[Any]):
           torch.manual_seed(0)

           _A					:     Optional[Any]     = {
               'num_attention_heads': 2,
               'attention_head_dim': 12,
               'embedding_dim': self.text_embedder_hidden_size,
               'num_layers': 1,
           }

           _A					:     Any     = PriorTransformer(**SCREAMING_SNAKE_CASE)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           _A					:     str     = nn.Parameter(torch.ones(model.clip_std.shape))
           return model


    @property
    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[Any]     = CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size				,							image_size=224				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_channels=3				,							num_hidden_layers=5				,							patch_size=14				,							)

           _A					:     Union[str, Any]     = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
           return model


    @property
    def        A   (  self      :							int):
           _A					:     Optional[Any]     = CLIPImageProcessor(
               crop_size=224				,							do_center_crop=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_resize=SCREAMING_SNAKE_CASE				,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,							resample=3				,							size=224				,							)

           return image_processor


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.dummy_prior
           _A					:     Dict     = self.dummy_image_encoder
           _A					:     Dict     = self.dummy_text_encoder
           _A					:     str     = self.dummy_tokenizer
           _A					:     Optional[Any]     = self.dummy_image_processor

           _A					:     Optional[Any]     = UnCLIPScheduler(
               variance_type='fixed_small_log'				,							prediction_type='sample'				,							num_train_timesteps=1000				,							clip_sample=SCREAMING_SNAKE_CASE				,							clip_sample_range=10.0				,							)

           _A					:     Dict     = {
               'prior': prior,
               'image_encoder': image_encoder,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'scheduler': scheduler,
               'image_processor': image_processor,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'horse',
               'generator': generator,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


    def        A   (  self      :							List[Any]):
           _A					:     str     = 'cpu'

           _A					:     Tuple     = self.get_dummy_components()

           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe.to(SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
           _A					:     str     = output.image_embeds

           _A					:     Optional[int]     = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     Optional[int]     = image[0, -10:]
           _A					:     int     = image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           _A					:     Dict     = np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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


    @skip_mps
    def        A   (  self      :							Any):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[int]     = True
           _A					:     Tuple     = False

           self._test_inference_batch_single_identical(
               test_max_difference=SCREAMING_SNAKE_CASE				,							relax_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[Any]     = False

           self._test_attention_slicing_forward_pass(
               test_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)






