





'''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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			1
          __A		:					List[str]						=			3
          __A		:					str						=			(32, 32)

          __A		:					List[Any]						=			floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0)).to(_UpperCAmelCase)
          return image


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A		:					Union[str, Any]						=			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=_UpperCAmelCase		,      only_cross_attention=(True, True, False)		,      num_class_embeds=100		,      )
          return model


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A		:					Optional[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A		:					Tuple						=			CLIPTextConfig(
              bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      hidden_act='gelu'		,      projection_dim=512		,      )
          return CLIPTextModel(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					str						=			self.dummy_image.cpu().permute(0		,      2		,      3		,      1)[0]
          __A		:					Tuple						=			Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((64, 64))

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

          __A		:					Dict						=			'A painting of a squirrel eating a burger'
          __A		:					Optional[int]						=			torch.Generator(device=_UpperCAmelCase).manual_seed(0)
          __A		:					Dict						=			sd_pipe(
              [prompt]		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      guidance_scale=6.0		,      noise_level=20		,      num_inference_steps=2		,      output_type='np'		,      )

          __A		:					Tuple						=			output.images

          __A		:					Dict						=			torch.Generator(device=_UpperCAmelCase).manual_seed(0)
          __A		:					str						=			sd_pipe(
              [prompt]		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      guidance_scale=6.0		,      noise_level=20		,      num_inference_steps=2		,      output_type='np'		,      return_dict=_UpperCAmelCase		,      )[0]

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

          __A		:					Dict						=			low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          __A		:					Optional[Any]						=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A		:					Optional[Any]						=			self.dummy_cond_unet_upscale
          __A		:					Any						=			DDPMScheduler()
          __A		:					Union[str, Any]						=			DDIMScheduler(prediction_type='v_prediction')
          __A		:					List[str]						=			self.dummy_vae
          __A		:					Optional[Any]						=			self.dummy_text_encoder
          __A		:					Tuple						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          __A		:					List[str]						=			self.dummy_image.cpu().permute(0		,      2		,      3		,      1)[0]
          __A		:					List[Any]						=			Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((64, 64))

          # make sure here that pndm scheduler skips prk
          __A		:					Optional[int]						=			StableDiffusionUpscalePipeline(
              unet=_UpperCAmelCase		,      low_res_scheduler=_UpperCAmelCase		,      scheduler=_UpperCAmelCase		,      vae=_UpperCAmelCase		,      text_encoder=_UpperCAmelCase		,      tokenizer=_UpperCAmelCase		,      max_noise_level=350		,      )
          __A		:					Any						=			sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A		:					Optional[Any]						=			'A painting of a squirrel eating a burger'
          __A		:					List[str]						=			sd_pipe(
              2 * [prompt]		,      image=2 * [low_res_image]		,      guidance_scale=6.0		,      noise_level=20		,      num_inference_steps=2		,      output_type='np'		,      )
          __A		:					int						=			output.images
          assert image.shape[0] == 2

          __A		:					int						=			torch.Generator(device=_UpperCAmelCase).manual_seed(0)
          __A		:					int						=			sd_pipe(
              [prompt]		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      num_images_per_prompt=2		,      guidance_scale=6.0		,      noise_level=20		,      num_inference_steps=2		,      output_type='np'		,      )
          __A		:					Optional[Any]						=			output.images
          assert image.shape[0] == 2


     @unittest.skipIf(torch_device != 'cuda'		,      'This test requires a GPU')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.dummy_cond_unet_upscale
          __A		:					Any						=			DDPMScheduler()
          __A		:					Optional[Any]						=			DDIMScheduler(prediction_type='v_prediction')
          __A		:					Any						=			self.dummy_vae
          __A		:					Union[str, Any]						=			self.dummy_text_encoder
          __A		:					Optional[int]						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          __A		:					Any						=			self.dummy_image.cpu().permute(0		,      2		,      3		,      1)[0]
          __A		:					Union[str, Any]						=			Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((64, 64))

          # put models in fp16, except vae as it overflows in fp16
          __A		:					Optional[Any]						=			unet.half()
          __A		:					str						=			text_encoder.half()

          # make sure here that pndm scheduler skips prk
          __A		:					Union[str, Any]						=			StableDiffusionUpscalePipeline(
              unet=_UpperCAmelCase		,      low_res_scheduler=_UpperCAmelCase		,      scheduler=_UpperCAmelCase		,      vae=_UpperCAmelCase		,      text_encoder=_UpperCAmelCase		,      tokenizer=_UpperCAmelCase		,      max_noise_level=350		,      )
          __A		:					Any						=			sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A		:					int						=			'A painting of a squirrel eating a burger'
          __A		:					int						=			torch.manual_seed(0)
          __A		:					Optional[Any]						=			sd_pipe(
              [prompt]		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      num_inference_steps=2		,      output_type='np'		,      ).images

          __A		:					List[str]						=			low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)




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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png')
          __A		:					Union[str, Any]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat.npy')

          __A		:					Optional[Any]						=			'stabilityai/stable-diffusion-x4-upscaler'
          __A		:					Any						=			StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()

          __A		:					List[Any]						=			'a cat sitting on a park bench'

          __A		:					Optional[int]						=			torch.manual_seed(0)
          __A		:					Any						=			pipe(
              prompt=_UpperCAmelCase		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      output_type='np'		,      )
          __A		:					Tuple						=			output.images[0]

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png')
          __A		:					Union[str, Any]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat_fp16.npy')

          __A		:					Any						=			'stabilityai/stable-diffusion-x4-upscaler'
          __A		:					Tuple						=			StableDiffusionUpscalePipeline.from_pretrained(
              _UpperCAmelCase		,      torch_dtype=torch.floataa		,      )
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()

          __A		:					List[str]						=			'a cat sitting on a park bench'

          __A		:					Tuple						=			torch.manual_seed(0)
          __A		:					int						=			pipe(
              prompt=_UpperCAmelCase		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      output_type='np'		,      )
          __A		:					List[str]						=			output.images[0]

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

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

          __A		:					Any						=			'stabilityai/stable-diffusion-x4-upscaler'
          __A		:					Dict						=			StableDiffusionUpscalePipeline.from_pretrained(
              _UpperCAmelCase		,      torch_dtype=torch.floataa		,      )
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing(1)
          pipe.enable_sequential_cpu_offload()

          __A		:					Optional[int]						=			'a cat sitting on a park bench'

          __A		:					Dict						=			torch.manual_seed(0)
          __A		:					Tuple						=			pipe(
              prompt=_UpperCAmelCase		,      image=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      num_inference_steps=5		,      output_type='np'		,      )

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

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__a         =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep			) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''			)

					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE_			):
									copyfile(self.vocab_file			,				SCREAMING_SNAKE_CASE_			)

					return (out_vocab_file,)




