



# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


UpperCAmelCase_									=							Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

UpperCAmelCase_									=							{"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

UpperCAmelCase_									=							"zero2"
UpperCAmelCase_									=							"zero3"
UpperCAmelCase_									=							[ZEROa, ZEROa]
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->Tuple:
      # customize the test name generator function as we want both params to appear in the sub-test
      # name, as by default it shows only the first param
      _lowerCAmelCase 			=  parameterized.to_safe_name('''_'''.join(str(_SCREAMING_SNAKE_CASE				) for x in param.args				)				)
      return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
UpperCAmelCase_									=							list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 		UpperCAmelCase						(						snake_case_				):





   @parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         self.run_and_check(
             stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





   @require_torch_multi_gpu
   @parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         self.run_and_check(
             stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





   @parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         self.run_and_check(
             stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





   @require_torch_multi_gpu
   @parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         self.run_and_check(
             stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         # XXX: run_asr is premature and doesn't save any results
         # so all we check for now is that the process didn't fail
         pass





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 10       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  models[model]

         _lowerCAmelCase 			=  self.run_trainer(
             stage=_lowerCAmelCase       ,     model_name=_lowerCAmelCase       ,     eval_steps=_lowerCAmelCase       ,     num_train_epochs=1       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )

         self.do_checks(_lowerCAmelCase			)

         return output_dir





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 10       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  self.get_auto_remove_tmp_dir('''./xxx'''       ,     after=_lowerCAmelCase			)
         _lowerCAmelCase 			=  F'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_lowerCAmelCase			)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

         if fpaa:
               args.extend(['''--fp16''']			)

         # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
         # hence the separate config files
         _lowerCAmelCase 			=  F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
         _lowerCAmelCase 			=  [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
         _lowerCAmelCase 			=  self.get_launcher(_lowerCAmelCase			)

         _lowerCAmelCase 			=  launcher + script + args + ds_args
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         execute_subprocess_async(_lowerCAmelCase       ,     env=self.get_env()			)

         return output_dir





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase=False			):
         # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
         # - it won't be able to handle that
         # 2. for now testing with just 2 gpus max (since some quality tests may give different
         # results with mode gpus because we use very little data)
         _lowerCAmelCase 			=  min(2       ,     get_gpu_count()			) if distributed else 1
         return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     DiTPipeline
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   SCREAMING_SNAKE_CASE__							=     PipelineTesterMixin.required_optional_params - {
       '''latents''',
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(0			)
         _lowerCAmelCase 			=  TransformeraDModel(
             sample_size=16       ,     num_layers=2       ,     patch_size=4       ,     attention_head_dim=8       ,     num_attention_heads=2       ,     in_channels=4       ,     out_channels=8       ,     attention_bias=_lowerCAmelCase       ,     activation_fn='''gelu-approximate'''       ,     num_embeds_ada_norm=1_000       ,     norm_type='''ada_norm_zero'''       ,     norm_elementwise_affine=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  AutoencoderKL()
         _lowerCAmelCase 			=  DDIMScheduler()
         _lowerCAmelCase 			=  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
         if str(_lowerCAmelCase			).startswith('''mps'''			):
               _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
         _lowerCAmelCase 			=  {
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''cpu'''

         _lowerCAmelCase 			=  self.get_dummy_components()
         _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)
         pipe.to(_lowerCAmelCase			)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
         _lowerCAmelCase 			=  pipe(**_lowerCAmelCase			).images
         _lowerCAmelCase 			=  image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,     (1, 16, 16, 3)			)
         _lowerCAmelCase 			=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]			)
         _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
         self.assertLessEqual(_lowerCAmelCase       ,     1E-3			)





   def 	__lowerCAmelCase							(	self			):
         self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase       ,     expected_max_diff=1E-3			)





   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()       ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,     )
   def 	__lowerCAmelCase							(	self			):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)






@require_torch_gpu
@slow
class 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self			):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  torch.manual_seed(0			)

         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=40       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''			)
               assert np.abs((expected_image - image).max()			) < 1E-2





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''			)
         _lowerCAmelCase 			=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=25       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   F'''/dit/{word}_512.npy'''			)

               assert np.abs((expected_image - image).max()			) < 1E-1