



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__		:					Any							=logging.getLogger(__name__)






def      __lowercase       (			a__							,       a__       )    ->			Optional[Any]:
							return (preds == labels).mean()

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,   )







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()	)}	)
						UpperCamelCase__      :			str      =  field(metadata={'''help''': '''Should contain the data files for the task.'''}	)
						UpperCamelCase__      :			int      =  field(
						    default=128   ,   metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated, sequences shorter will be padded.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			bool      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







def      __lowercase       (			)    ->			Optional[int]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir       )
							    and os.listdir(training_args.output_dir       )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'       )

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,       )
							logger.warning(
							    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,       training_args.local_rank							,       training_args.device							,       training_args.n_gpu							,       bool(training_args.local_rank != -1       )							,       training_args.fpaa							,       )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank       ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'							,       a__       )

							# Set seed
							set_seed(training_args.seed       )

							try:
														__SCREAMING_SNAKE_CASE									= processors[data_args.task_name]()
														__SCREAMING_SNAKE_CASE									= processor.get_labels()
														__SCREAMING_SNAKE_CASE									= len(a__       )
							except KeyError:
														raise ValueError('Task not found: %s' % (data_args.task_name)       )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=a__							,       finetuning_task=data_args.task_name							,       cache_dir=model_args.cache_dir							,       )
							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       cache_dir=model_args.cache_dir							,       )
							__SCREAMING_SNAKE_CASE									= AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path       )							,       config=a__							,       cache_dir=model_args.cache_dir							,       )

							# Get datasets
							__SCREAMING_SNAKE_CASE									= (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,       tokenizer=a__							,       task=data_args.task_name							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.train							,       )
							    if training_args.do_train
							    else None
							)
							__SCREAMING_SNAKE_CASE									= (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,       tokenizer=a__							,       task=data_args.task_name							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.dev							,       )
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(a__       ) -> Dict:
														__SCREAMING_SNAKE_CASE									= np.argmax(p.predictions							,       axis=1       )
														return {"acc": simple_accuracy(a__							,       p.label_ids       )}

							# Data collator
							__SCREAMING_SNAKE_CASE									= DataCollatorWithPadding(a__							,       pad_to_multiple_of=8       ) if training_args.fpaa else None

							# Initialize our Trainer
							__SCREAMING_SNAKE_CASE									= Trainer(
							    model=a__							,       args=a__							,       train_dataset=a__							,       eval_dataset=a__							,       compute_metrics=a__							,       data_collator=a__							,       )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
							__SCREAMING_SNAKE_CASE									= {}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'       )

														__SCREAMING_SNAKE_CASE									= trainer.evaluate()

														__SCREAMING_SNAKE_CASE									= os.path.join(training_args.output_dir							,       'eval_results.txt'       )
														if trainer.is_world_master():
																					with open(a__							,       'w'       ) as writer:
																												logger.info('***** Eval results *****'       )
																												for key, value in result.items():
																																			logger.info('  %s = %s'							,       a__							,       a__       )
																																			writer.write('%s = %s\n' % (key, value)       )

																												results.update(a__       )

							return results






def      __lowercase       (			a__       )    ->			List[str]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()











import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  CycleDiffusionPipeline
						UpperCamelCase__      :			int      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''negative_prompt''',
						    '''height''',
						    '''width''',
						    '''negative_prompt_embeds''',
						}
						UpperCamelCase__      :			Union[str, Any]      =  PipelineTesterMixin.required_optional_params - {'''latents'''}
						UpperCamelCase__      :			List[str]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}	)
						UpperCamelCase__      :			Optional[int]      =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase__      :			str      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= 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				,							)
													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='scaled_linear'				,							num_train_timesteps=1_000				,							clip_sample=_A				,							set_alpha_to_one=_A				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= 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				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__SCREAMING_SNAKE_CASE									= CLIPTextModel(_A		)
													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

													__SCREAMING_SNAKE_CASE									= {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vae': vae,
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'safety_checker': None,
													    'feature_extractor': None,
													}
													return components






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 32, 32)				,							rng=random.Random(_A		)		).to(_A		)
													__SCREAMING_SNAKE_CASE									= image / 2 + 0.5
													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'An astronaut riding an elephant',
													    'source_prompt': 'An astronaut riding a horse',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 2,
													    'eta': 0.1,
													    'strength': 0.8,
													    'guidance_scale': 3,
													    'source_guidance_scale': 1,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'  # ensure determinism for the device-dependent torch.Generator

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]		)

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






						@unittest.skipIf(torch_device != 'cuda'				,							'This test requires a GPU'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(_A				,							'half'		):
																											__SCREAMING_SNAKE_CASE									= module.half()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]		)

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






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_local()






						@unittest.skip('non-deterministic pipeline'		)
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_inference_batch_single_identical()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_optional_components()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_attention_slicing_forward_pass()







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(
													    _A				,							scheduler=_A				,							safety_checker=_A				,							torch_dtype=torch.floataa				,							revision='fp16'		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image		).max() < 5e-1






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(_A				,							scheduler=_A				,							safety_checker=_A		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													assert np.abs(image - expected_image		).max() < 2e-2






