





import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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     (				UpperCamelCase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						KandinskyInpaintPipeline
							snake_case_							 =						["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							snake_case_							 =						[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							snake_case_							 =						[
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							snake_case_							 =						False

							@property
							def      A_  (      self				:     Dict    )	->List[str]:
													return 32

							@property
							def      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													return 32

							@property
							def      A_  (      self				:     List[str]    )	->int:
													return self.time_input_dim

							@property
							def      A_  (      self				:     Optional[int]    )	->List[str]:
													return self.time_input_dim * 4

							@property
							def      A_  (      self				:     Optional[int]    )	->int:
													return 1_00

							@property
							def      A_  (      self				:     Union[str, Any]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"    )
													return tokenizer

							@property
							def      A_  (      self				:     List[str]    )	->Tuple:
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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=10_05							,	)

													SCREAMING_SNAKE_CASE__     :				Tuple   =					MultilingualCLIP(__A    )
													SCREAMING_SNAKE_CASE__     :				int   =					text_encoder.eval()

													return text_encoder

							@property
							def      A_  (      self				:     List[str]    )	->Tuple:
													torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					{
													    "in_channels": 9,
													    # 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,
													}

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					UNetaDConditionModel(**__A    )
													return model

							@property
							def      A_  (      self				:     Any    )	->Optional[int]:
													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      A_  (      self				:     str    )	->Optional[Any]:
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				str   =					VQModel(**self.dummy_movq_kwargs    )
													return model

							def      A_  (      self				:     Any    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.dummy_text_encoder
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.dummy_tokenizer
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.dummy_unet
													SCREAMING_SNAKE_CASE__     :				str   =					self.dummy_movq

													SCREAMING_SNAKE_CASE__     :				Dict   =					DDIMScheduler(
													    num_train_timesteps=10_00							,	beta_schedule="linear"							,	beta_start=0.0_0085							,	beta_end=0.012							,	clip_sample=__A							,	set_alpha_to_one=__A							,	steps_offset=1							,	prediction_type="epsilon"							,	thresholding=__A							,	)

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "movq": movq,
													}

													return components

							def      A_  (      self				:     Dict							,	a				:     Optional[int]							,	a				:     Union[str, Any]=0    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Dict   =					floats_tensor((1, self.cross_attention_dim)							,	rng=random.Random(__A    )    ).to(__A    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					floats_tensor((1, self.cross_attention_dim)							,	rng=random.Random(seed + 1    )    ).to(__A    )
													# create init_image
													SCREAMING_SNAKE_CASE__     :				Any   =					floats_tensor((1, 3, 64, 64)							,	rng=random.Random(__A    )    ).to(__A    )
													SCREAMING_SNAKE_CASE__     :				str   =					image.cpu().permute(0							,	2							,	3							,	1    )[0]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					Image.fromarray(np.uinta(__A    )    ).convert("RGB"    ).resize((2_56, 2_56)    )
													# create mask
													SCREAMING_SNAKE_CASE__     :				int   =					np.ones((64, 64)							,	dtype=np.floataa    )
													SCREAMING_SNAKE_CASE__     :				str   =					0

													if str(__A    ).startswith("mps"    ):
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.manual_seed(__A    )
													else:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					torch.Generator(device=__A    ).manual_seed(__A    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{
													    "prompt": "horse",
													    "image": init_image,
													    "mask_image": mask,
													    "image_embeds": image_embeds,
													    "negative_image_embeds": negative_image_embeds,
													    "generator": generator,
													    "height": 64,
													    "width": 64,
													    "num_inference_steps": 2,
													    "guidance_scale": 4.0,
													    "output_type": "np",
													}
													return inputs

							def      A_  (      self				:     int    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Any   =					"cpu"

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :				str   =					self.pipeline_class(**__A    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe.to(__A    )

													pipe.set_progress_bar_config(disable=__A    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(**self.get_dummy_inputs(__A    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					output.images

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

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					image_from_tuple[0, -3:, -3:, -1]

													print(f"""image.shape {image.shape}"""    )

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

													SCREAMING_SNAKE_CASE__     :				List[str]   =					np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]    )

													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()}"""






							def      A_  (      self				:     Dict    )	->str:
													super().test_inference_batch_single_identical(expected_max_diff=3E-3    )







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






							"""simple docstring"""

							def      A_  (      self				:     Any    )	->Optional[int]:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






							def      A_  (      self				:     Any    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Dict   =					load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"    )
													SCREAMING_SNAKE_CASE__     :				Any   =					np.ones((7_68, 7_68)							,	dtype=np.floataa    )
													SCREAMING_SNAKE_CASE__     :				Any   =					0

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					"a hat"

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					KandinskyPriorPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-1-prior"							,	torch_dtype=torch.floataa    )
													pipe_prior.to(__A    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					KandinskyInpaintPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-1-inpaint"							,	torch_dtype=torch.floataa    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					pipeline.to(__A    )
													pipeline.set_progress_bar_config(disable=__A    )

													SCREAMING_SNAKE_CASE__     :				int   =					torch.Generator(device="cpu"    ).manual_seed(0    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					pipe_prior(
													    __A							,	generator=__A							,	num_inference_steps=5							,	negative_prompt=""							,	).to_tuple()

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipeline(
													    __A							,	image=__A							,	mask_image=__A							,	image_embeds=__A							,	negative_image_embeds=__A							,	generator=__A							,	num_inference_steps=1_00							,	height=7_68							,	width=7_68							,	output_type="np"							,	)

													SCREAMING_SNAKE_CASE__     :				Tuple   =					output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(__A							,	__A    )






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       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  PreTrainedTokenizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Optional[int] = None			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					{}

						if train_file is not None:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[train_file]
						if eval_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[eval_file]
						if test_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[test_file]

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					datasets.load_dataset("csv"			,   data_files=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					list(ds[list(files.keys()     )[0]].features.keys()     )
						SCREAMING_SNAKE_CASE__     :				int   =					features_name.pop(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(set(ds[list(files.keys()     )[0]][label_name]     )     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{label: i for i, label in enumerate(_lowerCamelCase     )}
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.model_input_names
						SCREAMING_SNAKE_CASE__     :				Any   =					{}

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

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

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

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

						SCREAMING_SNAKE_CASE__     :				Tuple   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({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:
												SCREAMING_SNAKE_CASE__     :				Any   =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({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:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({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:
												SCREAMING_SNAKE_CASE__     :				Dict   =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

						return train_ds, val_ds, test_ds, labelaid


__lowercase       :List[Any]       =  logging.getLogger(__name__)



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(metadata={"help": "Which column contains the label"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the training file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the development file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the test file"}							)
							snake_case_							 =						field(
							    default=1_28					,							metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }					,							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Overwrite the cached training and evaluation sets"}							)







@dataclass
class    _a     :






							"""simple docstring"""

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







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					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.

						SCREAMING_SNAKE_CASE__     :				Any   =					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__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_tfds(
						    train_file=data_args.train_file			,   eval_file=data_args.dev_file			,   test_file=data_args.test_file			,   tokenizer=_lowerCamelCase			,   label_column_id=data_args.label_column_id			,   max_seq_length=data_args.max_seq_length			,   )

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

						with training_args.strategy.scope():
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path			,   from_pt=bool(".bin" in model_args.model_name_or_path     )			,   config=_lowerCamelCase			,   cache_dir=model_args.cache_dir			,   )

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

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

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE__     :				str   =					TFTrainer(
						    model=_lowerCamelCase			,   args=_lowerCamelCase			,   train_dataset=_lowerCamelCase			,   eval_dataset=_lowerCamelCase			,   compute_metrics=_lowerCamelCase			,   )

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

						# Evaluation
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"     )
												SCREAMING_SNAKE_CASE__     :				str   =					trainer.evaluate()
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					os.path.join(training_args.output_dir			,   "eval_results.txt"     )

												with open(_lowerCamelCase			,   "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(_lowerCamelCase     )

						return results


if __name__ == "__main__":
				main()