import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class   _lowerCAmelCase		(     __snake_case      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     KandinskyVaaInpaintPipeline
							_UpperCAmelCase						    =     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							_UpperCAmelCase						    =     [
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							_UpperCAmelCase						    =     [
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							_UpperCAmelCase						    =     False







							@property
							def 				snake_case  (				self     :    Optional[int]		):
										return 32







							@property
							def 				snake_case  (				self     :    List[str]		):
										return 32







							@property
							def 				snake_case  (				self     :    List[Any]		):
										return self.time_input_dim







							@property
							def 				snake_case  (				self     :    Any		):
										return self.time_input_dim * 4







							@property
							def 				snake_case  (				self     :    str		):
										return 100







							@property
							def 				snake_case  (				self     :    List[str]		):
										torch.manual_seed(0		)

										lowerCamelCase						:List[Any]						  =     {
										    "in_channels": 9,
										    # Out channels is double in channels because predicts mean and variance
										    "out_channels": 8,
										    "addition_embed_type": "image",
										    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
										    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
										    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
										    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    "layers_per_block": 1,
										    "encoder_hid_dim": self.text_embedder_hidden_size,
										    "encoder_hid_dim_type": "image_proj",
										    "cross_attention_dim": self.cross_attention_dim,
										    "attention_head_dim": 4,
										    "resnet_time_scale_shift": "scale_shift",
										    "class_embed_type": None,
										}

										lowerCamelCase						:Optional[int]						  =     UNetaDConditionModel(**A_		)
										return model







							@property
							def 				snake_case  (				self     :    Optional[int]		):
										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}







							@property
							def 				snake_case  (				self     :    Dict		):
										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     VQModel(**self.dummy_movq_kwargs		)
										return model







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:List[Any]						  =     self.dummy_unet
										lowerCamelCase						:Optional[Any]						  =     self.dummy_movq

										lowerCamelCase						:Optional[Any]						  =     DDIMScheduler(
										    num_train_timesteps=1000	,					beta_schedule='''linear'''	,					beta_start=0.0_0_0_8_5	,					beta_end=0.0_1_2	,					clip_sample=A_	,					set_alpha_to_one=A_	,					steps_offset=1	,					prediction_type='''epsilon'''	,					thresholding=A_	,					)

										lowerCamelCase						:Dict						  =     {
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components







							def 				snake_case  (				self     :    int	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    int=0		):
										lowerCamelCase						:int						  =     floats_tensor((1, self.text_embedder_hidden_size)	,					rng=random.Random(A_		)		).to(A_		)
										lowerCamelCase						:Any						  =     floats_tensor((1, self.text_embedder_hidden_size)	,					rng=random.Random(seed + 1		)		).to(
										    A_		)
										# create init_image
										lowerCamelCase						:Any						  =     floats_tensor((1, 3, 64, 64)	,					rng=random.Random(A_		)		).to(A_		)
										lowerCamelCase						:Optional[Any]						  =     image.cpu().permute(0	,					2	,					3	,					1		)[0]
										lowerCamelCase						:str						  =     Image.fromarray(np.uinta(A_		)		).convert('''RGB'''		).resize((256, 256)		)
										# create mask
										lowerCamelCase						:Dict						  =     np.ones((64, 64)	,					dtype=np.floataa		)
										lowerCamelCase						:Union[str, Any]						  =     0

										if str(A_		).startswith('''mps'''		):
													lowerCamelCase						:Tuple						  =     torch.manual_seed(A_		)
										else:
													lowerCamelCase						:Optional[int]						  =     torch.Generator(device=A_		).manual_seed(A_		)
										lowerCamelCase						:Optional[int]						  =     {
										    "image": init_image,
										    "mask_image": mask,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 2,
										    "guidance_scale": 4.0,
										    "output_type": "np",
										}
										return inputs







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:int						  =     "cpu"

										lowerCamelCase						:Dict						  =     self.get_dummy_components()

										lowerCamelCase						:str						  =     self.pipeline_class(**A_		)
										lowerCamelCase						:Any						  =     pipe.to(A_		)

										pipe.set_progress_bar_config(disable=A_		)

										lowerCamelCase						:int						  =     pipe(**self.get_dummy_inputs(A_		)		)
										lowerCamelCase						:List[Any]						  =     output.images

										lowerCamelCase						:List[str]						  =     pipe(
										    **self.get_dummy_inputs(A_		)	,					return_dict=A_	,					)[0]

										lowerCamelCase						:Optional[int]						  =     image[0, -3:, -3:, -1]
										lowerCamelCase						:int						  =     image_from_tuple[0, -3:, -3:, -1]

										print(F"image.shape {image.shape}"		)

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

										lowerCamelCase						:List[str]						  =     np.array(
										    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]		)

										assert (
										    np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







							def 				snake_case  (				self     :    Union[str, Any]		):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3		)


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







							def 				snake_case  (				self     :    List[Any]		):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Optional[Any]						  =     load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''		)

										lowerCamelCase						:Any						  =     load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
										lowerCamelCase						:Tuple						  =     np.ones((768, 768)	,					dtype=np.floataa		)
										lowerCamelCase						:str						  =     0

										lowerCamelCase						:Tuple						  =     "a hat"

										lowerCamelCase						:Tuple						  =     KandinskyVaaPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''	,					torch_dtype=torch.floataa		)
										pipe_prior.to(A_		)

										lowerCamelCase						:Optional[Any]						  =     KandinskyVaaInpaintPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''	,					torch_dtype=torch.floataa		)
										lowerCamelCase						:int						  =     pipeline.to(A_		)
										pipeline.set_progress_bar_config(disable=A_		)

										lowerCamelCase						:Tuple						  =     torch.Generator(device='''cpu'''		).manual_seed(0		)
										lowerCamelCase						:List[Any]						  =     pipe_prior(
										    A_	,					generator=A_	,					num_inference_steps=5	,					negative_prompt=''''''	,					).to_tuple()

										lowerCamelCase						:Union[str, Any]						  =     pipeline(
										    image=A_	,					mask_image=A_	,					image_embeds=A_	,					negative_image_embeds=A_	,					generator=A_	,					num_inference_steps=100	,					height=768	,					width=768	,					output_type='''np'''	,					)

										lowerCamelCase						:Union[str, Any]						  =     output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(A_	,					A_		)




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		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     LEDTokenizer
							_UpperCAmelCase						    =     LEDTokenizerFast
							_UpperCAmelCase						    =     True







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

										lowerCamelCase						:Optional[int]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						: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(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    int	,					**__snake_case     :    int		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Dict	,					**__snake_case     :    Any		):
										kwargs.update(self.special_tokens_map		)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Union[str, Any]		):
										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     :    int		):
										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''		)







							@require_torch
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Any						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										lowerCamelCase						:List[Any]						  =     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(__snake_case	,					max_length=len(__snake_case		)	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)

													self.assertEqual((2, 9)	,					batch.input_ids.shape		)
													self.assertEqual((2, 9)	,					batch.attention_mask.shape		)
													lowerCamelCase						:List[Any]						  =     batch.input_ids.tolist()[0]
													self.assertListEqual(__snake_case	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Dict						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIn('''input_ids'''	,					__snake_case		)
													self.assertIn('''attention_mask'''	,					__snake_case		)
													self.assertNotIn('''labels'''	,					__snake_case		)
													self.assertNotIn('''decoder_attention_mask'''	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Union[str, Any]						  =     [
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:List[Any]						  =     tokenizer(text_target=__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     :    List[Any]		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(
													    ['''I am a small frog''' * 1024, '''I am a small frog''']	,					padding=__snake_case	,					truncation=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertEqual(batch.input_ids.shape	,					(2, 5122)		)







							@require_torch
							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Union[str, Any]						  =     ['''A long paragraph for summarization.''']
										lowerCamelCase						:Any						  =     [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Any						  =     tokenizer(text_target=__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Optional[int]						  =     inputs['''input_ids''']
													lowerCamelCase						:Any						  =     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     :    Dict		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[int]						  =     ['''Summary of the text.''', '''Another summary.''']
													lowerCamelCase						:List[Any]						  =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													lowerCamelCase						:Optional[int]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [[0] * len(__snake_case		) for x in encoded_output['''input_ids''']]
													lowerCamelCase						:str						  =     tokenizer.pad(__snake_case		)
													self.assertSequenceEqual(outputs['''global_attention_mask''']	,					__snake_case		)







							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    Optional[int]		):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase						:Optional[Any]						  =     self.rust_tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:Tuple						  =     self.tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:int						  =     '''A, <mask> AllenNLP sentence.'''
																lowerCamelCase						:str						  =     tokenizer_r.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__snake_case		)
																lowerCamelCase						:str						  =     tokenizer_p.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__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''']		)	,					)

																lowerCamelCase						:Optional[int]						  =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																lowerCamelCase						:Tuple						  =     tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)
																self.assertSequenceEqual(tokens_p['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																self.assertSequenceEqual(tokens_r['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

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



