




'''simple docstring'''


import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command





class    __lowercase						(  unittest.TestCase ):
   _a							=      inspect.getfile(accelerate.test_utils )
   _a							=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_cli.py"""] )

   _a							=      ["""accelerate""", """launch"""]
   _a							=      Path.home() / """.cache/huggingface/accelerate"""
   _a							=      """default_config.yaml"""
   _a							=      config_folder / config_file
   _a							=      config_folder / """_default_config.yaml"""

   _a							=      Path("""tests/test_configs""" )







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    int:
     if cls.config_path.is_file():
       cls.config_path.rename(cls.changed_path      )







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    str:
     if cls.changed_path.is_file():
       cls.changed_path.rename(cls.config_path      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
       cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]      ,							env=os.environ.copy()      )







   def 		UpperCamelCase__			(	self      )				->    str:
     for config in sorted(self.test_config_path.glob('**/*.yaml'      )      ):
       with self.subTest(config_file=UpperCamelCase      ):
         execute_subprocess_async(
             self.base_cmd + ['--config_file', str(UpperCamelCase      ), self.test_file_path]      ,							env=os.environ.copy()      )







   def 		UpperCamelCase__			(	self      )				->    int:
     execute_subprocess_async(['accelerate', 'test']      ,							env=os.environ.copy()      )





class    __lowercase						(  unittest.TestCase ):
   _a							=      """test-tpu"""
   _a							=      """us-central1-a"""
   _a							=      """ls"""
   _a							=      ["""accelerate""", """tpu-config"""]
   _a							=      """cd /usr/share"""
   _a							=      """tests/test_samples/test_command_file.sh"""
   _a							=      """Running gcloud compute tpus tpu-vm ssh"""







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']      ,							return_stdout=UpperCamelCase      )
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"      ,							UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]      ,							return_stdout=UpperCamelCase      ,							)
     self.assertIn(
         f"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"      ,							UpperCamelCase      ,							)









'''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


UpperCAmelCase_ 		=      False





class    __lowercase						(  unittest.TestCase ):







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







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     torch.manual_seed(0      )
     __a			=					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      )				->    Optional[int]:
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
     return tokenizer







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					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(UpperCamelCase      )







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

     __a			=					12
     __a			=					12

     __a			=					{
         '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',
     }

     __a			=					TransformeraDModel(**UpperCamelCase      )
     return model







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					'cpu'

     __a			=					self.dummy_vqvae
     __a			=					self.dummy_text_encoder
     __a			=					self.dummy_tokenizer
     __a			=					self.dummy_transformer
     __a			=					VQDiffusionScheduler(self.num_embed      )
     __a			=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase      )

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					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      )				->    int:
     __a			=					'cpu'

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

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					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    __lowercase						(  unittest.TestCase ):







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







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

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

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

     __a			=					output.images[0]

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



