


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase      =   logging.get_logger(__name__)







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "upernet"



 def __init__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=[1, 2, 3, 6]		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.4		,				_UpperCAmelCase=3_84		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=1		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_55		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )

       if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
             snake_case_	=				CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
       elif isinstance(_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				backbone_config.get('''model_type''' )
             snake_case_	=				CONFIG_MAPPING[backbone_model_type]
             snake_case_	=				config_class.from_dict(_UpperCAmelCase )

       snake_case_	=				backbone_config
       snake_case_	=				hidden_size
       snake_case_	=				initializer_range
       snake_case_	=				pool_scales
       snake_case_	=				use_auxiliary_head
       snake_case_	=				auxiliary_loss_weight
       snake_case_	=				auxiliary_in_channels
       snake_case_	=				auxiliary_channels
       snake_case_	=				auxiliary_num_convs
       snake_case_	=				auxiliary_concat_input
       snake_case_	=				loss_ignore_index





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				copy.deepcopy(self.__dict__ )
       snake_case_	=				self.backbone_config.to_dict()
       snake_case_	=				self.__class__.model_type
       return output



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


UpperCAmelCase      =   False







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

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



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       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 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       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=10_00		,				)
       return CLIPTextModel(_UpperCAmelCase )



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )

       snake_case_	=				12
       snake_case_	=				12

       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''',
       }

       snake_case_	=				TransformeraDModel(**_UpperCAmelCase )
       return model



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.dummy_vqvae
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_transformer
       snake_case_	=				VQDiffusionScheduler(self.num_embed )
       snake_case_	=				LearnedClassifierFreeSamplingEmbeddings(learnable=_UpperCAmelCase )

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

       snake_case_	=				'''teddy bear playing in the pool'''

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

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

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

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

       snake_case_	=				np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992] )

       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 ):
       snake_case_	=				'''cpu'''

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

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

       snake_case_	=				'''teddy bear playing in the pool'''

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

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

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

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

       snake_case_	=				np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988] )

       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 lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

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





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

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

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

       snake_case_	=				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image ).max() < 2.0