



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 							lowerCAmelCase_						(    a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   Dict  	=    LEDTokenizer
       UpperCAmelCase__							:   Union[str, Any]  	=    LEDTokenizerFast
       UpperCAmelCase__							:   int  	=    True
       def    snake_case_			(     self			)	->						Tuple:
          super().setUp()
          UpperCamelCase       :    str		      =      [
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
          ]
          UpperCamelCase       :    str		      =      dict(zip(SCREAMING_SNAKE_CASE_,     range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
          UpperCamelCase       :    int		      =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          UpperCamelCase       :    List[str]		      =      {'unk_token': '<unk>'}

          UpperCamelCase       :    Tuple		      =      os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['vocab_file']			)
          UpperCamelCase       :    Any		      =      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(SCREAMING_SNAKE_CASE_			) + '\n'			)
          with open(self.merges_file,     'w',     encoding='utf-8'			) as fp:
             fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          kwargs.update(self.special_tokens_map			)
          return self.tokenizer_class.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						Dict:
          kwargs.update(self.special_tokens_map			)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          return "lower newer", "lower newer"
       @cached_property
       def    snake_case_			(     self			)	->						Any:
          return LEDTokenizer.from_pretrained('allenai/led-base-16384'			)
       @cached_property
       def    snake_case_			(     self			)	->						Dict:
          return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'			)
       @require_torch
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    str		      =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          UpperCamelCase       :    Dict		      =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

             self.assertEqual((2, 9),     batch.input_ids.shape			)
             self.assertEqual((2, 9),     batch.attention_mask.shape			)
             UpperCamelCase       :    Optional[int]		      =      batch.input_ids.tolist()[0]
             self.assertListEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       @require_torch
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[str]		      =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             UpperCamelCase       :    Dict		      =      tokenizer(SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			)
             self.assertIn('input_ids',     SCREAMING_SNAKE_CASE_			)
             self.assertIn('attention_mask',     SCREAMING_SNAKE_CASE_			)
             self.assertNotIn('labels',     SCREAMING_SNAKE_CASE_			)
             self.assertNotIn('decoder_attention_mask',     SCREAMING_SNAKE_CASE_			)
       @require_torch
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      [
              'Summary of the text.',
              'Another summary.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             UpperCamelCase       :    str		      =      tokenizer(text_target=SCREAMING_SNAKE_CASE_,     max_length=32,     padding='max_length',     return_tensors='pt'			)
             self.assertEqual(32,     targets['input_ids'].shape[1]			)
       @require_torch
       def    snake_case_			(     self			)	->						Any:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             UpperCamelCase       :    int		      =      tokenizer(
                 ['I am a small frog' * 1024, 'I am a small frog'],     padding=SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			)
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             self.assertEqual(batch.input_ids.shape,     (2, 5122)			)
       @require_torch
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    List[str]		      =      ['A long paragraph for summarization.']
          UpperCamelCase       :    Tuple		      =      [
              'Summary of the text.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             UpperCamelCase       :    Union[str, Any]		      =      tokenizer(SCREAMING_SNAKE_CASE_,     return_tensors='pt'			)
             UpperCamelCase       :    str		      =      tokenizer(text_target=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			)
             UpperCamelCase       :    List[Any]		      =      inputs['input_ids']
             UpperCamelCase       :    str		      =      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    snake_case_			(     self			)	->						Union[str, Any]:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             UpperCamelCase       :    Dict		      =      ['Summary of the text.', 'Another summary.']
             UpperCamelCase       :    int		      =      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             UpperCamelCase       :    Any		      =      tokenizer(SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Tuple		      =      [[0] * len(SCREAMING_SNAKE_CASE_			) for x in encoded_output['input_ids']]
             UpperCamelCase       :    Dict		      =      tokenizer.pad(SCREAMING_SNAKE_CASE_			)
             self.assertSequenceEqual(outputs['global_attention_mask'],     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[int]:
          pass






       def    snake_case_			(     self			)	->						Dict:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                UpperCamelCase       :    Optional[int]		      =      self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    str		      =      self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Dict		      =      'A, <mask> AllenNLP sentence.'
                UpperCamelCase       :    int		      =      tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Tuple		      =      tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                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       :    Union[str, Any]		      =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                UpperCamelCase       :    Optional[Any]		      =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)
                self.assertSequenceEqual(tokens_p['input_ids'],     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)
                self.assertSequenceEqual(tokens_r['input_ids'],     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)

                self.assertSequenceEqual(
                    SCREAMING_SNAKE_CASE_,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                self.assertSequenceEqual(
                    SCREAMING_SNAKE_CASE_,     ['<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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,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=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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=128      ,)
									torch.manual_seed(0						)
									_a	: 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=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







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







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_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() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

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







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_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	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




