



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase       :      Tuple							=							False






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



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



      def 		snake_case_      (  self  :			List[Any]		):
           _UpperCAmelCase    :			Optional[int]              =					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
           # remove text_unet
           pipe.remove_unused_weights()
           pipe.to(A		)
           pipe.set_progress_bar_config(disable=A		)

           _UpperCAmelCase    :			Union[str, Any]              =					load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

           _UpperCAmelCase    :			List[str]              =					torch.manual_seed(0		)
           _UpperCAmelCase    :			str              =					pipe(
               prompt="first prompt"  ,			image=A  ,			text_to_image_strength=0.75  ,			generator=A  ,			guidance_scale=7.5  ,			num_inference_steps=2  ,			output_type="numpy"  ,			).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                pipe.save_pretrained(A		)
                _UpperCAmelCase    :			Dict              =					VersatileDiffusionDualGuidedPipeline.from_pretrained(A		)

           pipe.to(A		)
           pipe.set_progress_bar_config(disable=A		)

           _UpperCAmelCase    :			List[str]              =					generator.manual_seed(0		)
           _UpperCAmelCase    :			Dict              =					pipe(
               prompt="first prompt"  ,			image=A  ,			text_to_image_strength=0.75  ,			generator=A  ,			guidance_scale=7.5  ,			num_inference_steps=2  ,			output_type="numpy"  ,			).images

           assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"



      def 		snake_case_      (  self  :			Optional[Any]		):
           _UpperCAmelCase    :			Union[str, Any]              =					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
           pipe.remove_unused_weights()
           pipe.to(A		)
           pipe.set_progress_bar_config(disable=A		)

           _UpperCAmelCase    :			Any              =					"cyberpunk 2077"
           _UpperCAmelCase    :			List[str]              =					load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
           _UpperCAmelCase    :			List[str]              =					torch.manual_seed(0		)
           _UpperCAmelCase    :			Optional[int]              =					pipe(
               prompt=A  ,			image=A  ,			text_to_image_strength=0.75  ,			generator=A  ,			guidance_scale=7.5  ,			num_inference_steps=5_0  ,			output_type="numpy"  ,			).images

           _UpperCAmelCase    :			Union[str, Any]              =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase    :			Optional[int]              =					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]		)

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





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:





     '''simple docstring'''

     for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
          yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]


# Configuration
_lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase       :      Dict							=							parser.parse_args()

# Base tokenizer
_lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())

# Load dataset
_lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
_lowerCAmelCase       :      List[Any]							=							iter(dataset)


# Training and saving
_lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
