


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   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return 12
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return 12
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       Tuple        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
        return tokenizer
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       Dict        =       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(lowerCAmelCase__			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       Optional[int]        =       12
        lowercase__:       Union[str, Any]        =       12

        lowercase__:       Any        =       {
            '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',
        }

        lowercase__:       Dict        =       TransformeraDModel(**lowerCAmelCase__			)
        return model
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       Tuple        =       'cpu'

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

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

        lowercase__:       Tuple        =       'teddy bear playing in the pool'

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

        lowercase__:       Tuple        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(0			)
        lowercase__:       Optional[Any]        =       pipe(
            [prompt]    ,			generator=lowerCAmelCase__    ,			output_type='np'    ,			return_dict=lowerCAmelCase__    ,			num_inference_steps=2			)[0]

        lowercase__:       str        =       image[0, -3:, -3:, -1]
        lowercase__:       Union[str, Any]        =       image_from_tuple[0, -3:, -3:, -1]

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

        lowercase__:       List[str]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       'cpu'

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

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

        lowercase__:       Optional[int]        =       'teddy bear playing in the pool'

        lowercase__:       List[str]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(0			)
        lowercase__:       Optional[int]        =       pipe([prompt]    ,			generator=lowerCAmelCase__    ,			num_inference_steps=2    ,			output_type='np'			)
        lowercase__:       Tuple        =       output.images

        lowercase__:       Union[str, Any]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(0			)
        lowercase__:       Optional[int]        =       pipe(
            [prompt]    ,			generator=lowerCAmelCase__    ,			output_type='np'    ,			return_dict=lowerCAmelCase__    ,			num_inference_steps=2			)[0]

        lowercase__:       List[str]        =       image[0, -3:, -3:, -1]
        lowercase__:       int        =       image_from_tuple[0, -3:, -3:, -1]

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

        lowercase__:       Dict        =       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   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       List[str]        =       load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'			)

        lowercase__:       Optional[int]        =       VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'			)
        lowercase__:       Dict        =       pipeline.to(lowerCAmelCase__			)
        pipeline.set_progress_bar_config(disable=lowerCAmelCase__			)

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

        lowercase__:       Union[str, Any]        =       output.images[0]

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






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


