


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							lowercase__(						snake_case__			,							snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					StableDiffusionParadigmsPipeline
      snake_case__       =					TEXT_TO_IMAGE_PARAMS
      snake_case__       =					TEXT_TO_IMAGE_BATCH_PARAMS
      snake_case__       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      snake_case__       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         torch.manual_seed(0)
         UpperCamelCase__ :			Tuple 						=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   ,			attention_head_dim=(2, 4)   ,			use_linear_projection=__SCREAMING_SNAKE_CASE   ,			)
         UpperCamelCase__ :			Optional[int] 						=DDIMParallelScheduler(
             beta_start=0.0_00_85   ,			beta_end=0.0_12   ,			beta_schedule="scaled_linear"   ,			clip_sample=__SCREAMING_SNAKE_CASE   ,			set_alpha_to_one=__SCREAMING_SNAKE_CASE   ,			)
         torch.manual_seed(0)
         UpperCamelCase__ :			str 						=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   ,			sample_size=1_28   ,			)
         torch.manual_seed(0)
         UpperCamelCase__ :			Union[str, Any] 						=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=10_00   ,			hidden_act="gelu"   ,			projection_dim=5_12   ,			)
         UpperCamelCase__ :			str 						=CLIPTextModel(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

         UpperCamelCase__ :			str 						={
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=0)					->						int:







         """simple docstring"""







         if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
            UpperCamelCase__ :			Optional[int] 						=torch.manual_seed(__SCREAMING_SNAKE_CASE)
         else:
            UpperCamelCase__ :			List[Any] 						=torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Tuple 						={
             "prompt": "a photograph of an astronaut riding a horse",
             "generator": generator,
             "num_inference_steps": 10,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "parallel": 3,
             "debug": True,
         }
         return inputs




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Any 						="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase__ :			Union[str, Any] 						=self.get_dummy_components()
         UpperCamelCase__ :			Tuple 						=StableDiffusionParadigmsPipeline(**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Tuple 						=sd_pipe.to(__SCREAMING_SNAKE_CASE)
         sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			List[Any] 						=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			int 						=sd_pipe(**__SCREAMING_SNAKE_CASE).images
         UpperCamelCase__ :			Any 						=image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         UpperCamelCase__ :			int 						=np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23])

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




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase__ :			Optional[int] 						=self.get_dummy_components()
         torch.manual_seed(0)
         UpperCamelCase__ :			Optional[Any] 						=DDPMParallelScheduler()
         torch.manual_seed(0)
         UpperCamelCase__ :			List[str] 						=StableDiffusionParadigmsPipeline(**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			str 						=sd_pipe.to(__SCREAMING_SNAKE_CASE)
         sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Dict 						=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=sd_pipe(**__SCREAMING_SNAKE_CASE).images
         UpperCamelCase__ :			List[Any] 						=image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         UpperCamelCase__ :			Union[str, Any] 						=np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68])

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




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         super().test_inference_batch_consistent(batch_sizes=[1, 2])




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         super().test_inference_batch_single_identical(batch_size=2   ,			expected_max_diff=3E-3)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase__ :			Tuple 						=self.get_dummy_components()
         UpperCamelCase__ :			Union[str, Any] 						=StableDiffusionParadigmsPipeline(**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			List[str] 						=sd_pipe.to(__SCREAMING_SNAKE_CASE)
         sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Any 						=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						="french fries"
         UpperCamelCase__ :			Optional[int] 						=sd_pipe(**__SCREAMING_SNAKE_CASE   ,			negative_prompt=__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						=output.images
         UpperCamelCase__ :			Any 						=image[0, -3:, -3:, -1]

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

         UpperCamelCase__ :			Any 						=np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15])

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




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






      '''simple docstring'''







      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=0)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			List[Any] 						={
             "prompt": "a photograph of an astronaut riding a horse",
             "generator": generator,
             "num_inference_steps": 10,
             "guidance_scale": 7.5,
             "output_type": "numpy",
             "parallel": 3,
             "debug": True,
         }
         return inputs




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						="stabilityai/stable-diffusion-2-base"
         UpperCamelCase__ :			Tuple 						=DDIMParallelScheduler.from_pretrained(__SCREAMING_SNAKE_CASE   ,			subfolder="scheduler")
         UpperCamelCase__ :			Any 						=StableDiffusionParadigmsPipeline.from_pretrained(__SCREAMING_SNAKE_CASE   ,			scheduler=__SCREAMING_SNAKE_CASE   ,			safety_checker=__SCREAMING_SNAKE_CASE)
         pipe.to(__SCREAMING_SNAKE_CASE)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
         pipe.enable_attention_slicing()

         UpperCamelCase__ :			Dict 						=self.get_inputs()
         UpperCamelCase__ :			List[Any] 						=pipe(**__SCREAMING_SNAKE_CASE).images
         UpperCamelCase__ :			int 						=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)

         UpperCamelCase__ :			Optional[Any] 						=np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41])

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





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 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






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


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  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)

