






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


lowercase  :							Tuple			  =    False




class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




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






						@property
						def       _lowerCAmelCase       (	self				) ->				int:
								return 12






						@property
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								return 12






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return 32






						@property
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								torch.manual_seed(0				)
								snake_case_				:							str						 =       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       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[str]						 =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
								return tokenizer






						@property
						def       _lowerCAmelCase       (	self				) ->				Any:
								torch.manual_seed(0				)
								snake_case_				:							str						 =       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=1000		,				)
								return CLIPTextModel(_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								torch.manual_seed(0				)

								snake_case_				:							Optional[Any]						 =       12
								snake_case_				:							int						 =       12

								snake_case_				:							List[str]						 =       {
								    "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",
								}

								snake_case_				:							Dict						 =       TransformeraDModel(**_SCREAMING_SNAKE_CASE				)
								return model






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Any						 =       "cpu"

								snake_case_				:							Dict						 =       self.dummy_vqvae
								snake_case_				:							int						 =       self.dummy_text_encoder
								snake_case_				:							Tuple						 =       self.dummy_tokenizer
								snake_case_				:							str						 =       self.dummy_transformer
								snake_case_				:							List[Any]						 =       VQDiffusionScheduler(self.num_embed				)
								snake_case_				:							List[Any]						 =       LearnedClassifierFreeSamplingEmbeddings(learnable=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       VQDiffusionPipeline(
								    vqvae=_SCREAMING_SNAKE_CASE		,				text_encoder=_SCREAMING_SNAKE_CASE		,				tokenizer=_SCREAMING_SNAKE_CASE		,				transformer=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		,				learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       pipe.to(_SCREAMING_SNAKE_CASE				)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       "teddy bear playing in the pool"

								snake_case_				:							int						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(0				)
								snake_case_				:							str						 =       pipe([prompt]		,				generator=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2		,				output_type="np"				)
								snake_case_				:							int						 =       output.images

								snake_case_				:							str						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(0				)
								snake_case_				:							Any						 =       pipe(
								    [prompt]		,				generator=_SCREAMING_SNAKE_CASE		,				output_type="np"		,				return_dict=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2				)[0]

								snake_case_				:							str						 =       image[0, -3:, -3:, -1]
								snake_case_				:							Tuple						 =       image_from_tuple[0, -3:, -3:, -1]

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

								snake_case_				:							Dict						 =       np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]				)

								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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Optional[Any]						 =       "cpu"

								snake_case_				:							Optional[int]						 =       self.dummy_vqvae
								snake_case_				:							int						 =       self.dummy_text_encoder
								snake_case_				:							int						 =       self.dummy_tokenizer
								snake_case_				:							Union[str, Any]						 =       self.dummy_transformer
								snake_case_				:							Union[str, Any]						 =       VQDiffusionScheduler(self.num_embed				)
								snake_case_				:							Tuple						 =       LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_SCREAMING_SNAKE_CASE		,				hidden_size=self.text_embedder_hidden_size		,				length=tokenizer.model_max_length				)

								snake_case_				:							str						 =       VQDiffusionPipeline(
								    vqvae=_SCREAMING_SNAKE_CASE		,				text_encoder=_SCREAMING_SNAKE_CASE		,				tokenizer=_SCREAMING_SNAKE_CASE		,				transformer=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		,				learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							Any						 =       pipe.to(_SCREAMING_SNAKE_CASE				)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       "teddy bear playing in the pool"

								snake_case_				:							Any						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(0				)
								snake_case_				:							int						 =       pipe([prompt]		,				generator=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2		,				output_type="np"				)
								snake_case_				:							Union[str, Any]						 =       output.images

								snake_case_				:							List[str]						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(0				)
								snake_case_				:							Union[str, Any]						 =       pipe(
								    [prompt]		,				generator=_SCREAMING_SNAKE_CASE		,				output_type="np"		,				return_dict=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2				)[0]

								snake_case_				:							Optional[Any]						 =       image[0, -3:, -3:, -1]
								snake_case_				:							str						 =       image_from_tuple[0, -3:, -3:, -1]

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

								snake_case_				:							int						 =       np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]				)

								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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							int						 =       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"				)

								snake_case_				:							List[str]						 =       VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"				)
								snake_case_				:							Dict						 =       pipeline.to(_SCREAMING_SNAKE_CASE				)
								pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_				:							List[Any]						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(0				)
								snake_case_				:							Optional[Any]						 =       pipeline(
								    "teddy bear playing in the pool"		,				num_images_per_prompt=1		,				generator=_SCREAMING_SNAKE_CASE		,				output_type="np"		,				)

								snake_case_				:							List[str]						 =       output.images[0]

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[Any]						 =       initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
