






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE__   :      str				=		logging.getLogger(__name__)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   git.Repo(search_parent_directories=snake_case			)
				__magic_name__		:Dict   =   {
				    '''repo_id''': str(snake_case			),
				    '''repo_sha''': str(repo.head.object.hexsha			),
				    '''repo_branch''': str(repo.active_branch			),
				}

				with open(os.path.join(snake_case,	'''git_log.json'''			),	'''w'''			) as f:
								json.dump(snake_case,	snake_case,	indent=4			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if params.n_gpu <= 0:
								__magic_name__		:Union[str, Any]   =   0
								__magic_name__		:Optional[Any]   =   -1
								__magic_name__		:int   =   True
								__magic_name__		:Union[str, Any]   =   False
								return

				assert torch.cuda.is_available()

				logger.info('''Initializing GPUs'''			)
				if params.n_gpu > 1:
								assert params.local_rank != -1

								__magic_name__		:Any   =   int(os.environ['''WORLD_SIZE''']			)
								__magic_name__		:str   =   int(os.environ['''N_GPU_NODE''']			)
								__magic_name__		:List[str]   =   int(os.environ['''RANK''']			)

								# number of nodes / node ID
								__magic_name__		:Optional[Any]   =   params.world_size // params.n_gpu_per_node
								__magic_name__		:Optional[int]   =   params.global_rank // params.n_gpu_per_node
								__magic_name__		:str   =   True

								assert params.n_nodes == int(os.environ['''N_NODES''']			)
								assert params.node_id == int(os.environ['''NODE_RANK''']			)

				# local job (single GPU)
				else:
								assert params.local_rank == -1

								__magic_name__		:str   =   1
								__magic_name__		:str   =   0
								__magic_name__		:Dict   =   0
								__magic_name__		:Tuple   =   0
								__magic_name__		:int   =   1
								__magic_name__		:int   =   1
								__magic_name__		:Dict   =   False

				# sanity checks
				assert params.n_nodes >= 1
				assert 0 <= params.node_id < params.n_nodes
				assert 0 <= params.local_rank <= params.global_rank < params.world_size
				assert params.world_size == params.n_nodes * params.n_gpu_per_node

				# define whether this is the master process / if we are in multi-node distributed mode
				__magic_name__		:List[Any]   =   params.node_id == 0 and params.local_rank == 0
				__magic_name__		:Tuple   =   params.n_nodes > 1

				# summary
				__magic_name__		:Optional[int]   =   f'''--- Global rank: {params.global_rank} - '''
				logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes			)
				logger.info(PREFIX + '''Node ID        : %i''' % params.node_id			)
				logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank			)
				logger.info(PREFIX + '''World size     : %i''' % params.world_size			)
				logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node			)
				logger.info(PREFIX + '''Master         : %s''' % str(params.is_master			)			)
				logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node			)			)
				logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu			)			)
				logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()			)

				# set GPU device
				torch.cuda.set_device(params.local_rank			)

				# initialize multi-GPU
				if params.multi_gpu:
								logger.info('''Initializing PyTorch distributed'''			)
								torch.distributed.init_process_group(
								    init_method='''env://''',	backend='''nccl''',	)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed			)










'''simple docstring'''





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												=						False




class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return 1_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return 1_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    return 3_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:List[str]      =      VQModel(
        block_out_channels=[3_2, 6_4]			,       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        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Tuple      =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
    return tokenizer


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:Optional[Any]      =      CLIPTextConfig(
        bos_token_id=0			,       eos_token_id=2			,       hidden_size=self.text_embedder_hidden_size			,       intermediate_size=3_7			,       layer_norm_eps=1e-05			,       num_attention_heads=4			,       num_hidden_layers=5			,       pad_token_id=1			,       vocab_size=1_0_0_0			,       )
    return CLIPTextModel(__lowerCamelCase )


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

    UpperCAmelCase__	:List[str]      =      1_2
    UpperCAmelCase__	:Optional[int]      =      1_2

    UpperCAmelCase__	:Optional[int]      =      {
        '''attention_bias''': True,
        '''cross_attention_dim''': 3_2,
        '''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''': 3_2,
        '''sample_size''': width,
        '''activation_fn''': '''geglu-approximate''',
    }

    UpperCAmelCase__	:List[str]      =      TransformeraDModel(**__lowerCamelCase )
    return model


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      '''cpu'''

    UpperCAmelCase__	:Optional[Any]      =      self.dummy_vqvae
    UpperCAmelCase__	:Optional[int]      =      self.dummy_text_encoder
    UpperCAmelCase__	:Union[str, Any]      =      self.dummy_tokenizer
    UpperCAmelCase__	:List[Any]      =      self.dummy_transformer
    UpperCAmelCase__	:str      =      VQDiffusionScheduler(self.num_embed )
    UpperCAmelCase__	:Optional[Any]      =      LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      VQDiffusionPipeline(
        vqvae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       transformer=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       learned_classifier_free_sampling_embeddings=__lowerCamelCase			,       )
    UpperCAmelCase__	:Any      =      pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:str      =      '''teddy bear playing in the pool'''

    UpperCAmelCase__	:Optional[int]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Any      =      pipe([prompt]			,       generator=__lowerCamelCase			,       num_inference_steps=2			,       output_type='''np''' )
    UpperCAmelCase__	:str      =      output.images

    UpperCAmelCase__	:List[Any]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Dict      =      pipe(
        [prompt]			,       generator=__lowerCamelCase			,       output_type='''np'''			,       return_dict=__lowerCamelCase			,       num_inference_steps=2 )[0]

    UpperCAmelCase__	:int      =      image[0, -3:, -3:, -1]
    UpperCAmelCase__	:List[Any]      =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 2_4, 2_4, 3)

    UpperCAmelCase__	:int      =      np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )

    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        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:List[Any]      =      '''cpu'''

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

    UpperCAmelCase__	:List[Any]      =      VQDiffusionPipeline(
        vqvae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       transformer=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       learned_classifier_free_sampling_embeddings=__lowerCamelCase			,       )
    UpperCAmelCase__	:str      =      pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:str      =      '''teddy bear playing in the pool'''

    UpperCAmelCase__	:str      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipe([prompt]			,       generator=__lowerCamelCase			,       num_inference_steps=2			,       output_type='''np''' )
    UpperCAmelCase__	:Any      =      output.images

    UpperCAmelCase__	:Optional[int]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:int      =      pipe(
        [prompt]			,       generator=__lowerCamelCase			,       output_type='''np'''			,       return_dict=__lowerCamelCase			,       num_inference_steps=2 )[0]

    UpperCAmelCase__	:List[Any]      =      image[0, -3:, -3:, -1]
    UpperCAmelCase__	:Optional[Any]      =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 2_4, 2_4, 3)

    UpperCAmelCase__	:Union[str, Any]      =      np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )

    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						):


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





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

    UpperCAmelCase__	:Optional[Any]      =      VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
    UpperCAmelCase__	:List[str]      =      pipeline.to(__lowerCamelCase )
    pipeline.set_progress_bar_config(disable=__lowerCamelCase )

    # requires GPU generator for gumbel softmax
    # don't use GPU generator in tests though
    UpperCAmelCase__	:int      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipeline(
        '''teddy bear playing in the pool'''			,       num_images_per_prompt=1			,       generator=__lowerCamelCase			,       output_type='''np'''			,       )

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

    assert image.shape == (2_5_6, 2_5_6, 3)
    assert np.abs(expected_image - image ).max() < 2.0



