





'''simple docstring'''







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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str    ,							lowercase						:      str    ,							lowercase						:      PreTrainedTokenizer    ,							lowercase						:      int    ,							lowercase						:      Optional[int] = None    ,							)	->	List[str]:
				_a								= {}

				if train_file is not None:
								_a								= [train_file]
				if eval_file is not None:
								_a								= [eval_file]
				if test_file is not None:
								_a								= [test_file]

				_a								= datasets.load_dataset("csv"    ,							data_files=lowercase						)
				_a								= list(ds[list(files.keys()						)[0]].features.keys()						)
				_a								= features_name.pop(lowercase						)
				_a								= list(set(ds[list(files.keys()						)[0]][label_name]						)						)
				_a								= {label: i for i, label in enumerate(lowercase						)}
				_a								= tokenizer.model_input_names
				_a								= {}

				if len(lowercase						) == 1:
								for k in files.keys():
												_a								= ds[k].map(
												    lambda lowercase						: tokenizer.batch_encode_plus(
												        example[features_name[0]]    ,							truncation=lowercase    ,							max_length=lowercase    ,							padding="max_length"						)    ,							batched=lowercase    ,							)
				elif len(lowercase						) == 2:
								for k in files.keys():
												_a								= ds[k].map(
												    lambda lowercase						: tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])    ,							truncation=lowercase    ,							max_length=lowercase    ,							padding="max_length"    ,							)    ,							batched=lowercase    ,							)

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												_a								= {k: v for k, v in ex.items() if k in input_names}
												_a								= labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												_a								= {k: v for k, v in ex.items() if k in input_names}
												_a								= labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												_a								= {k: v for k, v in ex.items() if k in input_names}
												_a								= labelaid[ex[label_name]]
												yield (d, label)

				_a								= (
				    tf.data.Dataset.from_generator(
				        lowercase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))    ,							)
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								_a								= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

				_a								= (
				    tf.data.Dataset.from_generator(
				        lowercase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))    ,							)
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								_a								= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

				_a								= (
				    tf.data.Dataset.from_generator(
				        lowercase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))    ,							)
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								_a								= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

				return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase_					:    Optional[Any]      	=			logging.getLogger(__name__)
@dataclass
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =field(metadata={'help': 'Which column contains the label'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the training file'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the development file'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the test file'}      )
				__a            =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.'
				        )
				    }       ,							)
				__a            =field(
				    default=lowerCamelCase_       ,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


@dataclass
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				__a            =field(
				    default=lowerCamelCase_       ,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
				__a            =field(
				    default=lowerCamelCase_       ,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'Set this flag to use fast tokenization.'}      )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__a            =field(
				    default=lowerCamelCase_       ,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}       ,							)






def  _lowerCamelCase				( )	->	Any:
				# 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.
				_a								= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
				_a	,					_a	,					_a								= 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    ,							)
				logger.info(
				    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1						)}, '
				    F'16-bits training: {training_args.fpaa}'						)
				logger.info(F'Training/evaluation parameters {training_args}'						)

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

				_a								= 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    ,							)

				_a	,					_a	,					_a	,					_a								= get_tfds(
				    train_file=data_args.train_file    ,							eval_file=data_args.dev_file    ,							test_file=data_args.test_file    ,							tokenizer=lowercase    ,							label_column_id=data_args.label_column_id    ,							max_seq_length=data_args.max_seq_length    ,							)

				_a								= AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,							num_labels=len(lowercase						)    ,							labelaid=lowercase    ,							idalabel={id: label for label, id in labelaid.items()}    ,							finetuning_task="text-classification"    ,							cache_dir=model_args.cache_dir    ,							)

				with training_args.strategy.scope():
								_a								= TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path    ,							from_pt=bool(".bin" in model_args.model_name_or_path						)    ,							config=lowercase    ,							cache_dir=model_args.cache_dir    ,							)

				def compute_metrics(lowercase						:      EvalPrediction						) -> Dict:
								_a								= np.argmax(p.predictions    ,							axis=1						)

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				_a								= TFTrainer(
				    model=lowercase    ,							args=lowercase    ,							train_dataset=lowercase    ,							eval_dataset=lowercase    ,							compute_metrics=lowercase    ,							)

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir						)

				# Evaluation
				_a								= {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"						)
								_a								= trainer.evaluate()
								_a								= os.path.join(training_args.output_dir    ,							"eval_results.txt"						)

								with open(lowercase    ,							"w"						) as writer:
												logger.info("***** Eval results *****"						)

												for key, value in result.items():
																logger.info(F'  {key} = {value}'						)
																writer.write(F'{key} = {value}\n'						)

												results.update(lowercase						)

				return results


if __name__ == "__main__":
					main()

'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()




class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Tuple ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    @property
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   3
         UpperCAmelCase_           =   (32, 32)

         UpperCAmelCase_           =   floats_tensor((batch_size, num_channels) + sizes   ,			rng=random.Random(0 ) ).to(__a )
         return image



    @property
    def 					_lowercase						(self    :    int ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   UNetaDConditionModel(
             block_out_channels=(32, 32, 64)   ,			layers_per_block=2   ,			sample_size=32   ,			in_channels=7   ,			out_channels=4   ,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")   ,			up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")   ,			cross_attention_dim=32   ,			attention_head_dim=8   ,			use_linear_projection=__a   ,			only_cross_attention=(True, True, False)   ,			num_class_embeds=100   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Any ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   AutoencoderKL(
             block_out_channels=[32, 32, 64]   ,			in_channels=3   ,			out_channels=3   ,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]   ,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]   ,			latent_channels=4   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Optional[Any] ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   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=1000   ,			hidden_act="gelu"   ,			projection_dim=512   ,			)
         return CLIPTextModel(__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   output.images

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			return_dict=__a   ,			)[0]

         UpperCAmelCase_           =   image[0, -3:, -3:, -1]
         UpperCAmelCase_           =   image_from_tuple[0, -3:, -3:, -1]

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         UpperCAmelCase_           =   np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )

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



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   sd_pipe(
             2 * [prompt]   ,			image=2 * [low_res_image]   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_images_per_prompt=2   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2






    @unittest.skipIf(torch_device != "cuda"   ,			"This test requires a GPU" )
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # put models in fp16, except vae as it overflows in fp16
         UpperCAmelCase_           =   unet.half()
         UpperCAmelCase_           =   text_encoder.half()

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_inference_steps=2   ,			output_type="np"   ,			).images

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)




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



    def 					_lowercase						(self    :    List[str] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(__a )
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat_fp16.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 5E-1






    def 					_lowercase						(self    :    List[Any] ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing(1 )
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			num_inference_steps=5   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9




