



"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							LEDTokenizer
						__UpperCamelCase		    =							LEDTokenizerFast
						__UpperCamelCase		    =							True






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Any:
												super().setUp()
												UpperCAmelCase					=			[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												UpperCAmelCase					=			dict(zip(lowercase_       ,  range(len(lowercase_ ) ) ) )
												UpperCAmelCase					=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												UpperCAmelCase					=			{'unk_token': '<unk>'}

												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['vocab_file'] )
												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['merges_file'] )
												with open(self.vocab_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write(json.dumps(lowercase_ ) + '\n' )
												with open(self.merges_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write('\n'.join(lowercase_ ) )






						def      UpperCAmelCase__					( self			:List[Any]       ,  **lowercase_			:List[Any] )    ->      str:
												kwargs.update(self.special_tokens_map )
												return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  **lowercase_			:str )    ->      Union[str, Any]:
												kwargs.update(self.special_tokens_map )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:str )    ->      List[Any]:
												return "lower newer", "lower newer"






						@cached_property
						def      UpperCAmelCase__					( self			:Tuple )    ->      List[Any]:
												return LEDTokenizer.from_pretrained('allenai/led-base-16384' )






						@cached_property
						def      UpperCAmelCase__					( self			:int )    ->      Optional[int]:
												return LEDTokenizerFast.from_pretrained('allenai/led-base-16384' )






						@require_torch
						def      UpperCAmelCase__					( self			:Tuple )    ->      Optional[int]:
												UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
												UpperCAmelCase					=			[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  max_length=len(lowercase_ )       ,  padding=lowercase_       ,  return_tensors='pt' )
																		self.assertIsInstance(lowercase_       ,  lowercase_ )

																		self.assertEqual((2, 9)       ,  batch.input_ids.shape )
																		self.assertEqual((2, 9)       ,  batch.attention_mask.shape )
																		UpperCAmelCase					=			batch.input_ids.tolist()[0]
																		self.assertListEqual(lowercase_       ,  lowercase_ )






						@require_torch
						def      UpperCAmelCase__					( self			:str )    ->      List[str]:
												UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  padding=lowercase_       ,  return_tensors='pt' )
																		self.assertIn('input_ids'       ,  lowercase_ )
																		self.assertIn('attention_mask'       ,  lowercase_ )
																		self.assertNotIn('labels'       ,  lowercase_ )
																		self.assertNotIn('decoder_attention_mask'       ,  lowercase_ )






						@require_torch
						def      UpperCAmelCase__					( self			:Tuple )    ->      List[str]:
												UpperCAmelCase					=			[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(text_target=lowercase_       ,  max_length=32       ,  padding='max_length'       ,  return_tensors='pt' )
																		self.assertEqual(32       ,  targets['input_ids'].shape[1] )






						@require_torch
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Tuple:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(
																		    ['I am a small frog' * 10_24, 'I am a small frog']       ,  padding=lowercase_       ,  truncation=lowercase_       ,  return_tensors='pt' )
																		self.assertIsInstance(lowercase_       ,  lowercase_ )
																		self.assertEqual(batch.input_ids.shape       ,  (2, 51_22) )






						@require_torch
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												UpperCAmelCase					=			['A long paragraph for summarization.']
												UpperCAmelCase					=			[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  return_tensors='pt' )
																		UpperCAmelCase					=			tokenizer(text_target=lowercase_       ,  return_tensors='pt' )
																		UpperCAmelCase					=			inputs['input_ids']
																		UpperCAmelCase					=			targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






						@require_torch
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Any:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			['Summary of the text.', 'Another summary.']
																		UpperCAmelCase					=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		UpperCAmelCase					=			tokenizer(lowercase_       ,  padding=lowercase_ )
																		UpperCAmelCase					=			[[0] * len(lowercase_ ) for x in encoded_output['input_ids']]
																		UpperCAmelCase					=			tokenizer.pad(lowercase_ )
																		self.assertSequenceEqual(outputs['global_attention_mask']       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Any:
												pass






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
																								UpperCAmelCase					=			self.rust_tokenizer_class.from_pretrained(lowercase_       ,  **lowercase_ )
																								UpperCAmelCase					=			self.tokenizer_class.from_pretrained(lowercase_       ,  **lowercase_ )
																								UpperCAmelCase					=			'A, <mask> AllenNLP sentence.'
																								UpperCAmelCase					=			tokenizer_r.encode_plus(lowercase_       ,  add_special_tokens=lowercase_       ,  return_token_type_ids=lowercase_ )
																								UpperCAmelCase					=			tokenizer_p.encode_plus(lowercase_       ,  add_special_tokens=lowercase_       ,  return_token_type_ids=lowercase_ )
																								self.assertEqual(sum(tokens_r['token_type_ids'] )       ,  sum(tokens_p['token_type_ids'] ) )
																								self.assertEqual(
																								    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )       ,  sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )       ,  )

																								UpperCAmelCase					=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																								UpperCAmelCase					=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
																								self.assertSequenceEqual(tokens_p['input_ids']       ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
																								self.assertSequenceEqual(tokens_r['input_ids']       ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

																								self.assertSequenceEqual(
																								    lowercase_       ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
																								self.assertSequenceEqual(
																								    lowercase_       ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )










"""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 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			1
												UpperCAmelCase					=			3
												UpperCAmelCase					=			(32, 32)

												UpperCAmelCase					=			floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(lowercase_ )
												return image






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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=lowercase_       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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       ,  )
												return CLIPTextModel(lowercase_ )






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

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

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

												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=lowercase_       ,  )[0]

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

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												UpperCAmelCase					=			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      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

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

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2






						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU' )
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# put models in fp16, except vae as it overflows in fp16
												UpperCAmelCase					=			unet.half()
												UpperCAmelCase					=			text_encoder.half()

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)







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



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(lowercase_ )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 1E-3






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

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 5E-1






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

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

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  output_type='np'       ,  )

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





