




'''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       (						_a	,unittest.TestCase       ):
							lowercase_							     =							KandinskyImgaImgPipeline
							lowercase_							     =							['prompt', 'image_embeds', 'negative_image_embeds', 'image']
							lowercase_							     =							[
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							]
							lowercase_							     =							[
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_							     =							False







							@property
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


														return 32







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														return 32







							@property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														return 1_00







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
														return tokenizer







							@property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							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				,     )

														_a      	=							MultilingualCLIP(lowerCAmelCase_   )
														_a      	=							text_encoder.eval()

														return text_encoder







							@property
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Tuple:


														"""simple docstring"""


														torch.manual_seed(0   )

														_a      	=							{
														    '''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,
														}

														_a      	=							UNetaDConditionModel(**lowerCAmelCase_   )
														return model







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														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   __lowerCAmelCase     (    self			:    Any   )     ->				str:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							VQModel(**self.dummy_movq_kwargs   )
														return model







							def   __lowerCAmelCase     (    self			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							self.dummy_text_encoder
														_a      	=							self.dummy_tokenizer
														_a      	=							self.dummy_unet
														_a      	=							self.dummy_movq

														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''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,
														}

														_a      	=							DDIMScheduler(**lowerCAmelCase_   )

														_a      	=							{
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]=0   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
														_a      	=							floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1   )   ).to(lowerCAmelCase_   )
														# create init_image
														_a      	=							floats_tensor((1, 3, 64, 64)				,     rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
														_a      	=							image.cpu().permute(0				,     2				,     3				,     1   )[0]
														_a      	=							Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

														if str(lowerCAmelCase_   ).startswith('''mps'''   ):
																					_a      	=							torch.manual_seed(lowerCAmelCase_   )
														else:
																					_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
														_a      	=							{
														    '''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   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							'''cpu'''

														_a      	=							self.get_dummy_components()

														_a      	=							self.pipeline_class(**lowerCAmelCase_   )
														_a      	=							pipe.to(lowerCAmelCase_   )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							pipe(**self.get_dummy_inputs(lowerCAmelCase_   )   )
														_a      	=							output.images

														_a      	=							pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_   )				,     return_dict=lowerCAmelCase_				,     )[0]

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

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

														_a      	=							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   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinsky/kandinsky_img2img_frog.npy'''   )

														_a      	=							load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
														_a      	=							'''A red cartoon frog, 4k'''

														_a      	=							KandinskyPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-1-prior'''				,     torch_dtype=torch.floataa   )
														pipe_prior.to(lowerCAmelCase_   )

														_a      	=							KandinskyImgaImgPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-1'''				,     torch_dtype=torch.floataa   )
														_a      	=							pipeline.to(lowerCAmelCase_   )

														pipeline.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							torch.Generator(device='''cpu'''   ).manual_seed(0   )
														_a	,				_a      	=							pipe_prior(
														    lowerCAmelCase_				,     generator=lowerCAmelCase_				,     num_inference_steps=5				,     negative_prompt=''''''				,     ).to_tuple()

														_a      	=							pipeline(
														    lowerCAmelCase_				,     image=lowerCAmelCase_				,     image_embeds=lowerCAmelCase_				,     negative_image_embeds=lowerCAmelCase_				,     generator=lowerCAmelCase_				,     num_inference_steps=1_00				,     height=7_68				,     width=7_68				,     strength=0.2				,     output_type='''np'''				,     )

														_a      	=							output.images[0]

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

														assert_mean_pixel_difference(lowerCAmelCase_				,     lowerCAmelCase_   )







'''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 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_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=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

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

							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(
							        UpperCamelCase					,		({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(
							        UpperCamelCase					,		({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(
							        UpperCamelCase					,		({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


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							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.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



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



def 						snake_case_       ():





							'''simple docstring'''


							_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=UpperCamelCase					,		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(UpperCamelCase )					,		labelaid=UpperCamelCase					,		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=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

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

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

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

							# 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(UpperCamelCase					,		'''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(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

