



import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowerCAmelCase__		:					List[Any]							=False

class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 12






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 12






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 32






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= VQModel(
													    block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=3				,							num_vq_embeddings=self.num_embed				,							vq_embed_dim=3				,							)
													return model






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
													return tokenizer






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													return CLIPTextModel(_A		)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)

													__SCREAMING_SNAKE_CASE									= 12
													__SCREAMING_SNAKE_CASE									= 12

													__SCREAMING_SNAKE_CASE									= {
													    'attention_bias': True,
													    'cross_attention_dim': 32,
													    'attention_head_dim': height * width,
													    'num_attention_heads': 1,
													    'num_vector_embeds': self.num_embed,
													    'num_embeds_ada_norm': self.num_embeds_ada_norm,
													    'norm_num_groups': 32,
													    'sample_size': width,
													    'activation_fn': 'geglu-approximate',
													}

													__SCREAMING_SNAKE_CASE									= TransformeraDModel(**_A		)
													return model






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'

													__SCREAMING_SNAKE_CASE									= self.dummy_vqvae
													__SCREAMING_SNAKE_CASE									= self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE									= self.dummy_tokenizer
													__SCREAMING_SNAKE_CASE									= self.dummy_transformer
													__SCREAMING_SNAKE_CASE									= VQDiffusionScheduler(self.num_embed		)
													__SCREAMING_SNAKE_CASE									= LearnedClassifierFreeSamplingEmbeddings(learnable=_A		)

													__SCREAMING_SNAKE_CASE									= VQDiffusionPipeline(
													    vqvae=_A				,							text_encoder=_A				,							tokenizer=_A				,							transformer=_A				,							scheduler=_A				,							learned_classifier_free_sampling_embeddings=_A				,							)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= 'teddy bear playing in the pool'

													__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe([prompt]				,							generator=_A				,							num_inference_steps=2				,							output_type='np'		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    [prompt]				,							generator=_A				,							output_type='np'				,							return_dict=_A				,							num_inference_steps=2		)[0]

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

													assert image.shape == (1, 24, 24, 3)

													__SCREAMING_SNAKE_CASE									= np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]		)

													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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'

													__SCREAMING_SNAKE_CASE									= self.dummy_vqvae
													__SCREAMING_SNAKE_CASE									= self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE									= self.dummy_tokenizer
													__SCREAMING_SNAKE_CASE									= self.dummy_transformer
													__SCREAMING_SNAKE_CASE									= VQDiffusionScheduler(self.num_embed		)
													__SCREAMING_SNAKE_CASE									= LearnedClassifierFreeSamplingEmbeddings(
													    learnable=_A				,							hidden_size=self.text_embedder_hidden_size				,							length=tokenizer.model_max_length		)

													__SCREAMING_SNAKE_CASE									= VQDiffusionPipeline(
													    vqvae=_A				,							text_encoder=_A				,							tokenizer=_A				,							transformer=_A				,							scheduler=_A				,							learned_classifier_free_sampling_embeddings=_A				,							)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= 'teddy bear playing in the pool'

													__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe([prompt]				,							generator=_A				,							num_inference_steps=2				,							output_type='np'		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    [prompt]				,							generator=_A				,							output_type='np'				,							return_dict=_A				,							num_inference_steps=2		)[0]

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

													assert image.shape == (1, 24, 24, 3)

													__SCREAMING_SNAKE_CASE									= np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]		)

													assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'		)

													__SCREAMING_SNAKE_CASE									= VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'		)
													__SCREAMING_SNAKE_CASE									= pipeline.to(_A		)
													pipeline.set_progress_bar_config(disable=_A		)

													# requires GPU generator for gumbel softmax
													# don't use GPU generator in tests though
													__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipeline(
													    'teddy bear playing in the pool'				,							num_images_per_prompt=1				,							generator=_A				,							output_type='np'				,							)

													__SCREAMING_SNAKE_CASE									= output.images[0]

													assert image.shape == (256, 256, 3)
													assert np.abs(expected_image - image		).max() < 2.0











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






